Collision Avoidance

A special type of random access memory (RAM) array called as content addressable memory (CAM), in which stored data is compared with the search data which can be returning the address. In the applications of high speed searching, the CAMs are used. NOR type matchline CAMs are helpful for applications requiring faster search speeds. Because the NOR type match line (ML) CAM consumes a lot of power, therefore many published designs have attempted to reduce power consumption. The design self-controlled high-performance content addressable memories (SC-CAM) using 45-nm technology is presented in this paper. The 6T 4×4 CAM arrays in this paper uses SC logic and Tanner tools 45-nm technology. When compared to the conventional CAM, described SC-CAM architecture reduces the number of voltages sources. Described 6T 4×4 SC-CAM design needs less number of MOSFETs than existed 8T 4×4 CAM array and thus reduces the area with high speed.

An automated braking decision or steering decision and control framework is proposed in this paper to avoid pedestrian collisions. Collision avoidance is an important prerequisite for safe and automated driving in modern traffic systems. This paper explores intelligent and safe obstacle avoidance in complex traffic scenarios with main focus on pedestrian collision avoidance. The driver's purpose is important in collision avoidance, when the driver intends to avoid a collision by relying only on braking a safe stopping distance is 0.5 meters. But, when a driver intends to avoid collision by relying only on steering, for the most part, it is necessary to ensure that automated braking intervention will not occur.

This paper reports the phase B mission analysis work performed for the safe orbit and the Collision Avoidance Maneuvers (CAM) of the PROBA-3 formation flying mission, including the recovery to nominal conditions. PROBA-3 will perform formation flying in a highly elliptical orbit, and perform Solar coronagraphy and formation maneuvering demonstrations in a six-hour arc around apogee. Mission analysis in this paper addresses the safe orbit and the CAM and focuses on safety and ΔV requirements. Important constraints on the mission analysis are the absence of an omnidirectional sensor, and the requirement that the spacecraft cannot turn more than 30° away from the Sun due to thermal and power constraints. In addition, no on-board action can be taken after safe orbit entry nor after a CAM, which means that the recovery needs to be performed under ground control.

The safe control of multi-robot swarms is a challenging and active field of research, where common goals include maintaining group cohesion while simultaneously avoiding obstacles and inter-agent collision. Building off our previously developed theory for distributed collaborative safety-critical control for networked dynamic systems, we propose a distributed algorithm for the formation control of robot swarms given individual agent dynamics, induced formation dynamics, and local neighborhood position and velocity information within a defined sensing radius for each agent. Individual safety guarantees for each agent are obtained using rounds of communication between neighbors to restrict unsafe control actions among cooperating agents through safety conditions derived from high-order control barrier functions. We provide conditions under which a swarm is guaranteed to achieve collective safety with respect to multiple obstacles using a modified collaborative safety algorithm. We demonstrate the performance of our distributed algorithm via simulation in a simplified physics-based environment.

This article discusses the design of an application specific MPSoC architecture dedicated to Multiple Target Tracking (MTT). This application has its utility in driver assistant systems, more precisely in collision avoidance and warning systems. An Automotive-radar is used as the front end sensor in our application. The article examines the tradeoffs that must be taken into consideration in the realization of the entire MTT application in an embedded system. In our implementation of MTT, several independent parallel tasks have been identified and mapped onto a multiprocessor architecture to ensure the deadlines imposed by the application. Our study demonstrates that the joint utilization of reconfigurable circuits (namely FPGA) and MPSoC, facilitates the development of a flexible and efficient MTT system.

Planning of safe and efficient trajectories is a critical task in the operation of unmanned aerial vehicles (UAVs), especially in urban or complex environments. With the increasing use of UAVs for various applications, such as surveillance, delivery, and inspection, it is becoming more important to automatically generate collision-free paths that also consider aircraft dynamics. This paper proposes an algorithmic approach based on the Rapidly exploring random tree (RRT) algorithm combined with a clothoid-based smoothing procedure to account for aircraft performance.

Autonomous vehicles stand at the forefront of technological advancement, promising to alter fundamentally how we navigate, with implications for everything from inter city logistics to daily commuting. The expected benefits of this technology include advancements in safety protocols, streamlining of traffic flow, and superior comfort for those onboard. However, the path to widespread integration is paved with signifi cant barriers that need to be overcome. This article provides a thorough review of the historical development of autonomous driving technologies, beginning with the early vehicles powered by internal combustion engines and extending to the present excite ment over Apple’s Project Titan. Rumors suggest this particular project involves the creation of an electric vehicle fully capable of operating without traditional manual controls, including steering wheels and seats. The article concludes by spotlighting the potential for autonomous vehicles to enable disruptive changes in the transport sector, while also considering the myriad of challenges that must be surmounted for their adoption on a grand scale.

High-resolution through-the-wall radar imaging (TWRI) is a formidable challenge in a cluttered and dynamic environment, because strong wall returns often overwhelm the weaker echoes from targets. Indoor clutter and dense multipath further blur fine spatial details and complicate target discrimination. These challenges demand computationally efficient imaging algorithms to preserve high-resolution performance in real operational settings. This research work investigates the trade-offs between computational complexity and estimation accuracy for highresolution through-the-wall image reconstruction. This paper develops a multiple-input multiple-output (MIMO) virtual array framework that synthesizes a large aperture to improve spatial resolution. It presents a hybrid image reconstruction (HIR) technique that integrates FFT-based processing with subspace-based spectral estimation. The proposed hybrid methodology exploits the efficiency of FFT for coarse focusing and the high resolution of MUSIC for fine imaging. The objective of this hybrid approach is to achieve superior image reconstruction compared with 3D-FFT, range migration algorithm (RMA) and conventional beamforming (CBF) approaches. A systematic system model and analytical formulation of the proposed methodology is presented. This paper also presents a comprehensive stepwise computational complexity derivation and analysis for the proposed methodology alongwith the 3D-FFT and RMA imaging techniques. The analysis provides quantitative insight into the tradeoffs between estimation accuracy, angular resolution and computational burden. Comprehensive analytical and numerical performance evaluation highlights the practical feasibility of the hybrid FFT-MUSIC methodology for real-world TWRI scenarios. With the advent of resource-rich modern hardware platforms, the computational burden of the proposed FFT-MUSIC framework can be significantly mitigated, enabling practical real-time high-resolution TWRI imaging.

This paper presents the design and implementation of a model predictive control (MPC) system to guide and control a chasing spacecraft during rendezvous with a passive target spacecraft in an elliptical or circular orbit, from the point of target detection all the way to capture. To achieve an efficient system design, the rendezvous manoeuvre has been partitioned into three main phases based on the range of operation, plus a collision-avoidance manoeuvre to be used in event of a fault. Each has its own associated MPC controller. Linear time varying models are used to enable trajectory predictions in elliptical orbits, whilst a variable prediction horizon is used to achieve finite-time completion of manoeuvres, and a 1-norm cost on velocity change minimises propellant consumption. Constraints are imposed to ensure that trajectories do not collide with the target. A key feature of the design is the implementation of non-convex constraints as switched convex constraints, enabling the use of convex linear and quadratic programming. The system is implemented using commercial-off-the-shelf tools with deployment through the use of automatic code generation in mind, and validated by closed-loop simulation. A significant reduction in total propellant consumption in comparison with a baseline benchmark solution is observed.

The Optimal Reciprocal Collision Avoidance (ORCA) algorithm is a foundational method for decentralized multiagent navigation, particularly valued for its lightweight implementation and robustness in crowded contexts. Originally developed for robotics, environmental noise and motion uncertainty are inherent, ORCA has become a standard in video game Artificial Intelligence (AI). However, in highly deterministic environments characterized by small integration time steps, which typically arise in modern asynchronous game simulations, the absence of stochasticity leads to systemic deadlocks in symmetric configurations that do not occur in noisy real-world systems. By analyzing the construction of the half-plane velocity constraints in ORCA, we identify the geometric origin of these deadlocks. We demonstrate that deadlocks occur systematically across all symmetric scenarios for n ∈ [2, 30] agents when exact rotational symmetry is preserved, especially with small time steps or slow agents. We present Revised ORCA Logic (RORCAL), a lightweight post-processing mechanism that introduces minimal directional bias to resolve degenerate solutions while preserving ORCA's computational efficiency. Experimental results show that RORCAL resolves all tested deadlock scenarios with a 2% periteration overhead and achieves 23% faster resolution in a 250agents benchmark through emergent rotational coordination.

A new real-time obstacle avoidance method for mobile robots has been developed and implemented. This method, named the Vector Field Hisfog" (VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. A VFH-controlled mobile robot maneuvers quickly and without stopping among densely cluttered obstacles. The VFH method uses a two-dimensional Cartesian Histognun Gfid as a world model. This world model is updated continuously and in real-time with range data sampled by the onboard ultrasonic range sensors. Based on the accumulated environmental data, the VFH method then computes a one-dimensional Polar Histogram that is constructed around the robot's momentary location. Each sector in the Polar Histogram holds thepolar obstacle density in that direction. Finally, the algorithm selects the most suitable sector from among aU Polar Hisfogmi sectors with low obstacle density, and the steering of the robot is aligned with that direction. Experimental results from a mobile robot traversing a densely cluttered obstacle course at an average speed of 0.7 m/sec demonstrate the power of the VFH method.

Nowadays, there are many different home networking solutions: wired, wireless, and the so called "no new wires"; all compete for their market share. The most widely used metric to compare these technologies is the physical rate. Nevertheless, this metric does not reflect the peculiarities of each MAC protocol, which limit the bandwidth actually available to users. In this article, we analyze different home networking technologies taking the main features of their MAC protocols into account. We have chosen the saturation throughput as the basic metric and have provided analytical results. Then, through simulations, we have varied the number of nodes in the network to verify how each protocol deals with contention and to analyze their efficiency. Results show that collision-avoidance protocols have lower efficiency than collision-detection protocols. Nevertheless, there may be exceptions. HomePNA 3.0 has a relatively low efficiency because it uses the same basic rate as HomePNA 2.0, to keep compatibility. The same happens within a protocol family; IEEE 802.11g at 54 Mbps is less efficient than IEEE 802.11b at 11 Mbps.

Autonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles.

Products implementing the HomePlug 1.0 standard allowing high‐speed communication on low‐voltage powerlines have recently started arriving on the U.S. market for home and office networking without the requirement for installing new wires. Effective use of the powerline bandwidth requires robust physical (PHY) and medium access control (MAC) protocols to mitigate the harsh conditions of the powerline channel as well as the capability to support prioritized multimedia traffic. This paper describes powerline communications and the HomePlug 1.0 protocol, based on orthogonal frequency division multiplexing (OFDM) and carrier sense multiple access with collision avoidance (CSMA/CA), along with its changes to allow prioritized channel access. It then presents performance results for the HomePlug 1.0 protocol using a simulation model, ideal laboratory measurements with actual HomePlug 1.0 devices and field tests in a residential building. Simulation and laboratory data rates were around 6 M...

In autonomous driving applications a critical challenge is to identify the action to take to avoid an obstacle on a collision course. For example, when a heavy object is suddenly encountered it is critical to stop the vehicle or change the lane even if it causes other traffic disruptions. However, there are situations when it is preferable to collide with the object rather than take an action that would result in a much more serious accident than collision with the object. For example, a heavy object which falls from a truck should be avoided whereas a bouncing ball or a soft target such as a foam box need not be. We present a novel method to discriminate between the motion characteristics of these types of objects based on their physical properties such as bounciness, elasticity, etc. In this preliminary work, we use recurrent neural network with LSTM (Long Short Term Memory) cells to train a classifier to classify objects based on their motion trajectories. We test the algorithm on synthetic data, and, as a proof of concept, demonstrate its effectiveness on a limited set of real-world data.

This paper introduces selected methods of a game theory for ship collision avoidance at sea. The application of game control algorithms to determine a ship's safe trajectory, whilst passing other encountered ships in good and restricted visibility at sea, is presented. Regarding the methods of comparison for the safe ship's control in a collision situation, multi-stage positional noncooperative and cooperative games and multi-step matrix non-cooperative and cooperative games have been introduced. The considerations have been illustrated with examples of computer simulations of the algorithms to determine the safety of the ship's trajectories in a navigational situation of restricted visibility in the Kattegat Strait.

This paper examines a novel particle swarm algorithm which has been applied to the generation of interactive, improvised music. An important feature of this algorithm is a balance between particle attraction to the centre of mass and repulsive, collision avoiding forces. These forces are not present in the classic particle swarm optimisation algorithms. A number of experiments illuminate the nature of these new forces and it is suggested that the algorithm may have applications to dynamic optimisation problems.

Collision avoidance systems play a vital role in reducing the number of vehicle accidents and saving human lives. This paper extends the previous work using evolutionary neural networks for reactive collision avoidance. We are proposing a new method we have called symmetric neural networks. The method improves the model's performance by enforcing constraints between the network weights which reduces the model optimization search space and hence, learns more accurate control of the vehicle steering for improved maneuvering. The training and validation processes are carried out using a simulation environment -the codebase is publicly available. Extensive experiments are conducted to analyze the proposed method and evaluate its performance. The method is tested in several simulated driving scenarios. In addition, we have analyzed the effect of the rangefinder sensor resolution and noise on the overall goal of reactive collision avoidance. Finally, we have tested the generalization of the proposed method. The results are encouraging; the proposed method has improved the model's learning curve for training scenarios and generalization to the new test scenarios. Using constrained weights has significantly improved the number of generations required for the Genetic Algorithm optimization.

Collision avoidance systems can play a vital role in reducing the number of accidents and saving human lives. In this paper, we introduce and validate a novel method for vehicles reactive collision avoidance using evolutionary neural networks (ENN). A single front-facing rangefinder sensor is the only input required by our method. The training process and the proposed method analysis and validation are carried out using simulation. Extensive experiments are conducted to analyse the proposed method and evaluate its performance. Firstly, we experiment the ability to learn collision avoidance in a static free track. Secondly, we analyse the effect of the rangefinder sensor resolution on the learning process. Thirdly, we experiment the ability of a vehicle to individually and simultaneously learn collision avoidance. Finally, we test the generality of the proposed method. We used a more realistic and powerful simulation environment (CarMaker), a camera as an alternative input sensor, and lane keeping as an extra feature to learn. The results are encouraging; the proposed method successfully allows vehicles to learn collision avoidance in different scenarios that are unseen during training. It also generalizes well if any of the input sensor, the simulator, or the task to be learned is changed.

This report covers a field study on an innovative large-scale data collection technique used to gather information about how crash avoidance systems operate in the field and how drivers respond to them. Although the specific systems studied were the General Motors (GM) camera-based forward collision alert (FCA) and lane departure warning (LDW) systems, this technique could be readily applied to other emerging active safety (crash avoidance) systems and used to better inform emerging active safety consumer metrics. It should be noted that both the FCA and LDW systems evaluated have consistently met the National Highway Traffic Safety Administration’s Crash Avoidance New Car Assessment Program (CA NCAP) performance criteria since this program was initiated. The study team found that this data collection technique has several distinct strengths, including cost, sample size, drivers using their own vehicles where they can turn systems off, ability to look at long-term effects, data effi...

This project continues to build upon the foundation provided by the human factors experimentation conducted in the previous Crash Avoidance Metrics Partnership (CAMP) Forward Collision Warning (FCW) system efforts. As in the previous CAMP FCW research, this work was conducted with a surrogate target, test-track methodology, which allows driver behavior to be observed under controlled, real approach, rear-end crash scenario conditions. The surrogate target, test-track methodology involves three vehicles--a lead vehicle, a mock vehicle (or surrogate target vehicle), and a subject vehicle that is driven by the test participant. The real driving conditions created with the surrogate target, test track methodology are likely to increase the chance that the crash alert timing approach developed will generalize to real-world conditions. The major conclusions from this research are as follows: (1) Based on test driver intervention rates during surprise trials, the alert timing approach eval...

Drivers' last-second braking and last-second steering judgments have been studied extensively by the Crash Avoidance Metrics Partnership (CAMP) Forward Collision Warning (FCW) Requirements project. This previous work was conducted under closed-course conditions using a realistic surrogate target lead vehicle. In the current research, a subset of these tests involving more than 4000 individual test runs has been replicated in the National Advanced Driving Simulator (NADS) facility for comparison purposes. The major conclusions from this research are as follows: • Scenarios need to pay careful attention to ensure initial headway conditions prior to the critical approach event correspond to those that are typically experienced in real world driving. More generally, scenarios should have real-world validation. • Scenarios should emphasize high lead vehicle decelerations. The 0.39-g deceleration levels gave the best results and have been used in previous CAMP surprise trial research. • Scenarios should emphasize cases where the relative speed differential is high, particularly when the lead vehicle is stationary. • Scenarios should emphasize last-second hard braking or hard steering over last-second "normal" maneuvers. • Crash rates should not be used as a metric, and instead, attention should be focused on the interpretation of last-second maneuver onset behavior.

This report covers a field study on an innovative large-scale data collection technique used to gather information about how crash avoidance systems operate in the field and how drivers respond to them. Although the specific systems studied were the General Motors (GM) camera-based forward collision alert (FCA) and lane departure warning (LDW) systems, this technique could be readily applied to other emerging active safety (crash avoidance) systems and used to better inform emerging active safety consumer metrics. It should be noted that both the FCA and LDW systems evaluated have consistently met the National Highway Traffic Safety Administration's Crash Avoidance New Car Assessment Program (CA NCAP) performance criteria since this program was initiated. The study team found that this data collection technique has several distinct strengths, including cost, sample size, drivers using their own vehicles where they can turn systems off, ability to look at long-term effects, data efficiency, and the ability to acquire "rapid-turnaround" large-scale results, and that this new data collection technique is ideally suited for understanding the safety impacts of crash avoidance systems.

NRL is developing compact optic flow sensors for use in a variety of small-scale navigation and collision avoidance tasks. These sensors are being developed for use in micro air vehicles (MAVs), which are autonomous aircraft whose maximum dimension is on the order of 15 cm. To achieve desired weight specifications of 1-2 grams, mixed-signal VLSI circuitry is being used to develop compact focal plane sensors that directly compute optic flow. As an interim proof of principle, we have constructed a sensor comprising a focal plane sensor head with on-chip processing and a back-end PIC microcontroller. This interim sensors weighs approximately 25 grams and is able to measure optic flow with realworld and low-contrast textures. Variations of this sensor have been used to control the flight of a glider in real-time to avoid collisions with walls.

Fuzzy set, since its advent has played an important role in control systems and many other area of applications. One of such area is the control of autonomous vehicle. There seem to be some difficulty however, for a new timer trying to get a clear picture of the autonomous navigation problem. To this end, this survey presents a panoramic view of the Intelligent Transportation Systems with some few example of the Advance Driver Assistance Systems and a good discussion on the autonomous systems with its eminent problems. More attention was focused on the fuzzy controllers designed for collision avoidance; as its performance has largely simplified and smoothens the collision avoidance process of an autonomous vehicular system.

This paper presents a model predictive control based trajectory optimization method for Nap-of-the-Earth (NOE) flight including obstacle avoidance, emphasizing the mission objective of low altitude at high speed. A NOE trajectory reference is generated over a subspace of the terrain. It is then inserted into the cost function and the resulting trajectory tracking error term is weighted for more precise longitudinal tracking than lateral tracking through the introduction of the TF/TA ratio. Obstacle avoidance including preclusion of ground collision is accomplished through the establishment of hard state constraints. These state constraints create a 'safe envelope' within which the optimal trajectory can be found. Steps are taken to reduce complexity in the optimization problem including perturbational linearization in the prediction model generation and the use of control basis functions. Preliminary results over a variety of sample terrains are provided to show the mission objective of low altitude and high speed was met satisfactorily without terrain or obstacle collision, however, methods to preclude or deal with infeasibility must be investigated as speed is increased to and past 30 knots.

This paper presents a model predictive control based trajectory optimization method for Nap-of-the-Earth (NOE) flight including obstacle avoidance, emphasizing the mission objective of low altitude at high speed. A NOE trajectory reference is generated over a subspace of the terrain. It is then inserted into the cost function and the resulting trajectory tracking error term is weighted for more precise longitudinal tracking than lateral tracking through the introduction of the TF/TA ratio. Obstacle avoidance including preclusion of ground collision is accomplished through the establishment of hard state constraints. These state constraints create a 'safe envelope' within which the optimal trajectory can be found. Steps are taken to reduce complexity in the optimization problem including perturbational linearization in the prediction model generation and the use of control basis functions. Preliminary results over a variety of sample terrains are provided to show the mission objective of low altitude and high speed was met satisfactorily without terrain or obstacle collision, however, methods to preclude or deal with infeasibility must be investigated as speed is increased to and past 30 knots.

This paper explores the state of the art on to methods to verify and validate navigation algorithms for autonomous surface ships. We perform a systematic mapping study to find research works published in the last 10 years proposing new algorithms for autonomous navigation and collision avoidance and we have extracted what verification and validation approaches have been applied on these algorithms. We observe that most research works use simulations to validate their algorithms. However, these simulations often involve just a few scenarios designed manually. This raises the question if the algorithms have been validated properly. To remedy this, we propose the use of a systematic scenario-based testing approach to validate navigation algorithms extensively.

Vehicle tailgating or simply tailgating is a hazardous driving habit. Tailgating occurs when a vehicle moves very close behind another one while not leaving adequate separation distance in case the vehicle in front stops unexpectedly; this separation distance is technically called "Assured Clear Distance Ahead" (ACDA) or Safe Driving Distance. Advancements in Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV) have made it of tremendous significance to have an intelligent approach for connected vehicles to avoid tailgating; this paper proposes a new Internet of Vehicles (IoV) based technique that enables connected vehicles to determine ACDA or Safe Driving Distance and Safe Driving Speed to avoid a forward collision. The technique assumes two cases: In the first case, the vehicle has Autonomous Emergency Braking (AEB) system, while in the second case, the vehicle has no AEB. Safe Driving Distance and Safe Driving Speed are calculated under several variables. ...

Vehicle tailgating or simply tailgating is a hazardous driving habit. Tailgating occurs when a vehicle moves very close behind another one while not leaving adequate separation distance in case the vehicle in front stops unexpectedly; this separation distance is technically called “Assured Clear Distance Ahead” (ACDA) or Safe Driving Distance. Advancements in Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV) have made it of tremendous significance to have an intelligent approach for connected vehicles to avoid tailgating; this paper proposes a new Internet of Vehicles (IoV) based technique that enables connected vehicles to determine ACDA or Safe Driving Distance and Safe Driving Speed to avoid a forward collision. The technique assumes two cases: In the first case, the vehicle has Autonomous Emergency Braking (AEB) system, while in the second case, the vehicle has no AEB. Safe Driving Distance and Safe Driving Speed are calculated under several variables. ...

Due to their efficient locomotion and natural tolerance to hazardous environments, spherical robots have wide applications in security surveillance, exploration of unknown territory and emergency response. Numerous studies have been conducted on the driving mechanism, motion planning and trajectory tracking methods of spherical robots, yet very limited studies have been conducted regarding the obstacle avoidance capability of spherical robots. Most of the existing spherical robots rely on the “hit and run” technique, which has been argued to be a reasonable strategy because spherical robots have an inherent ability to recover from collisions. Without protruding components, they will not become stuck and can simply roll back after running into bstacles. However, for small scale spherical robots that contain sensitive surveillance sensors and cannot afford to utilize heavy protective shells, the absence of obstacle avoidance solutions would leave the robot at the mercy of potentially ...

Due to their efficient locomotion and natural tolerance to hazardous environments, spherical robots have wide applications in security surveillance, exploration of unknown territory and emergency response. Numerous studies have been conducted on the driving mechanism, motion planning and trajectory tracking methods of spherical robots, yet very limited studies have been conducted regarding the obstacle avoidance capability of spherical robots. Most of the existing spherical robots rely on the "hit and run" technique, which has been argued to be a reasonable strategy because spherical robots have an inherent ability to recover from collisions. Without protruding components, they will not become stuck and can simply roll back after running into bstacles. However, for small scale spherical robots that contain sensitive surveillance sensors and cannot afford to utilize heavy protective shells, the absence of obstacle avoidance solutions would leave the robot at the mercy of po...

This paper reports on a new approach to Signal Temporal Logic (STL) control synthesis, that 1) utilizes a navigation function as the basis to construct a Control Barrier Function (CBF), and 2) composes navigation function-based barrier functions using nonsmooth mappings to encode Boolean operations between the predicates that those barrier functions encode. Because of these two key features, the reported approach 1) covers a larger fragment of STL compared to existing approaches, 2) alleviates the computational cost associated with evaluation of the control law for the system in existing STL control barrier function methodologies, and 3) simultaneously relaxes some of the conservativeness of smooth combinations of barrier functions as a means of implementing Boolean operators. The paper demonstrates the efficacy of this new approach with three simulation case studies, one aiming at illustrating how complex STL motion planning specification can be realized, the second highlights the ...

Co-design and integration of vehicle navigation and control and state estimation is key for enabling ¯eld deployment of mobile robots in GPS-denied cluttered environments, and sensor calibration is critical for successful operation of both subsystems. This paper demonstrates the potential of this co-design approach with ¯eld tests of the integration of a reactive receding horizon-based motion planner and controller with an inertial aided multi-sensor calibration scheme. The reported method provides accurate calibration parameters that improve the performance of the state estimator, and enable the motion controller to generate smooth and continuous minimal-jerk trajectories based on local LiDAR data. Numerical simulations in Unity, and real-world experimental results from the ¯eld corroborate the claims of e±cacy for the reported autonomous navigation computational pipeline.

This is the second of a two-part paper, investigating the stability properties of a system of multiple mobile agents with double integrator dynamics. In this second part, we allow the topology of the control interconnections between the agents in the group to vary with time. Specifically, the control law of an agent depends on the state of a set of agents that are within a certain neighborhood around it. As the agents move around this set changes, giving rise to a dynamic control interconnection topology and a switching control law. This control law consists of a a combination of attractive/repulsive and alignment forces. The former ensure collision avoidance and cohesion of the group and the latter result to all agents attaining a common heading angle, exhibiting flocking motion. Despite the use of only local information and the time varying nature of agent interaction which affects the local controllers, flocking motion is established, as long as connectivity in the neighboring graph is maintained.

This is the first of a two-part paper that investigates the stability properties of a system of multiple mobile agents with double integrator dynamics. In this first part we generate stable flocking motion for the group using a coordination control scheme which gives rise to smooth control laws for the agents. These control laws are a combination of attractive/repulsive and alignment forces, ensuring collision avoidance and cohesion of the group and an aggregate motion along a common heading direction. In this control scheme the topology of the control interconnections is fixed and time invariant. The control policy ensures that all agents eventually align with each other and have a common heading direction while at the same time avoid collisions and group into a tight formation.

The pseudo millimeter wave 22-29GHz ultra-wideband (UWB) is attractive for the applications in short-range (SR) automotive radar sensors in order to contribute to significant reduction of traffic fatalities. Although CMOS is suitable for the short-range radar since processing units can be implemented in the same chip with the UWB front-end building block, it is difficult to operate CMOS pulse generators at such a high frequency. To realize the pseudo-millimeter-wave band using CMOS, we have proposed a new pulse generator consisting of a series of delay cells and edge combiners with waveform shaping. As a result of measurement using 90nm CMOS technology, 1Gbps pulses are successfully generated with a power consumption of 1.4mW at a supply voltage of 0.9V. This result will be the key technology for a one-chip SR cost effective radar sensors.

This research developed a safe navigation system of an autonomous UAV within a comprehensive simulation framework. The navigation system can find a collision-free trajectory to a randomly assigned 3D target position without any prior map information. It contains four main components: mapping, localisation, cognition and control, where the cognition system makes execution command based on the perceived position information about obstacles and the UAV itself from mapping and localisation system respectively. The control system is responsible for executing the input command made from the cognition system. Three case studies for real-life scenarios, such as restricted area avoidance, static obstacle avoidance and dynamic obstacles, are conducted. The experiments demonstrate that the UAV is capable of determining a collision-free trajectory under all three cases of environments. All simulated components are designed to match their real-world counterparts' dynamics and properties. Ideally, the simulated navigation framework can be transferred to a real UAV without any changes. As the navigation system of a drone is implemented in a modular way, it is easier to test and validate to ensure the system's performance. Moreover, the system has good readability, maintainability and extendability. Hence, the simulation framework provides a good platform for future robotic research.

Collision avoidance is essential for the safe operation of unmanned surface vehicles (USVs) in marine environments. While existing studies have addressed USV collaboration and navigation, they often overlook environmental challenges. In this research, we develop a novel deep reinforcement learning model and train USVs to navigate safely under different situations, such as rain, wind, and their combined factors that can disrupt control and increase collision risk. We apply Generative Adversarial Imitation Learning (GAIL) and Proximal Policy Optimization (PPO) to improve the agent's performance using expert demonstrations. The experimental results demonstrate that the proposed framework significantly outperforms the baseline across multiple metrics: the maximum episode length increased from 15 to 350 steps, the cultivated reward improved by 158%, the extrinsic reward increased by 175%, and the number of collisions decreased by 75-78% across all environmental conditions. Moreover, the policy loss stabilized after 1.1 million training steps, confirming efficient convergence. These results show that our model performs better than the baseline model in mean reward, episode length, value estimates, and collision reduction. In the end, some concluding remarks and future directions are added by the authors.

Recent well-publicized cruise and ferry ship accidents have been a wake-up call to the shipping industry. These events raise the need for ship owners to use more advanced risk management approaches. Enterprise Risk Management (ERM) has become an integral part of modern corporations, especially in safety critical industries where unsafe operations can lead to catastrophic accidents that result in loss of life, huge financial losses and the potential demise of the corporation. This paper presents the current findings of an on-going research to determine the best implementation of a Risk Management System (RMS) integrated with the Safety Management System (SMS) at cruise and ferry operating companies. The hypothesis is that ship management companies with better enterprise risk management will have better safety records and stronger financial performance. The paper presents the results of the application of Systems-Theoretic Accident Model and Processes (STAMP)/Systems Theoretic Process...

This paper presents a lightweight, training-free lane-line detection pipeline for daytime and night-time driving videos implemented in Python using OpenCV. The method performs illumination-aware pre-processing using CLAHE, and applies mild gamma correction in LAB space for low-light scenes. A trapezoidal road region-of-interest is then masked to suppress background clutter, followed by adaptive edge extraction using Canny with data-driven thresholds. Candidate lane segments are obtained via the probabilistic Hough transform and separated into left/right groups using slope-based filtering. The final lane boundaries are estimated by length-weighted averaging of line parameters and rendered as an overlay to visualize the drivable lane region and heading direction. Experiments on day and night recordings captured on the same road segments demonstrate stable lane detection under shadows, headlight bloom, and worn markings. The proposed approach is suitable for CPU-only embedded/mobile deployment and can support lane-departure warning and lane-keeping assistance in ADAS and autonomous driving applications.

As we have already seen that the railway has done a lot of efforts for collision avoidance and accidents prevention. Several techniques have been developed for avoiding such hazardous loss of life and property. In this paper we have proposed an approach using latest 4G technology WiMAX (Worldwide Interoperability for Microwave Access) i.e. IEEE802.16e standards for preventing train accidents. The approach helps the train to know the location of its own with the help of Fixed WiMAX base stations whose position is known. The approach doesn't require extra GPS service for the purpose calculating location like existing railway system uses. The WiMAX base station and the train (mobile stations) contribute in sending the information to the trains whenever a train comes in its vicinity and range. The trains will be equipped with WiMAX enabled laptops/Tablets for communicating with others. The speed location and distance of other trains will be known in advance to make safe from being p...

This paper presents a simulation-focused methodology for performing satellite re-entry dynamics analysis with MATLAB. When a satellite is descending, it is subjected to gravitational attraction, atmospheric drag and applied thrust; these factors can be described by solving a system of differential equations. The simulation is initiated at an altitude of 300 km with the given initial conditions and with the use of real-world properties such as altitude-varying air density and orbital dynamics. The simulations give an altitude decay, velocity change and flight path angle history of re-entry. In addition, this model illustrates that atmospheric resistance becomes the dominant force of deceleration at low altitudes.

ENGINEERING NOTES are short manuscripts describing new developments or important results of a preliminary nature. These Notes should not exceed 2500 words (where a figure or table counts as 200 words). Following informal review by the Editors, they may be published within a few months of the date of receipt. Style requirements are the same as for regular contributions (see inside back cover).

Efficient algorithms for collision-free energy sub-optimal path planning for formations of spacecraft flying in deep space are presented. The idea is to introduce a set of way-points through which the spacecraft are required to pass, combined with parameterizations of the trajectories which are energy-optimal for each spacecraft. The resulting constrained optimization problem is formulated as a quasiquadratic parameter optimization problem in terms of the way-points parameters. The mathematical structure of the problem is further exploited to develop gradient-based algorithms in which the gradients are computed analytically. The collision avoidance constraints are approximated such that closed form solutions are generated. This combination results in fast and robust numerical algorithms which work very well for scenarios involving a large number of spacecraft (e.g. 20).

An efficient method for energy optimal reconfiguration of formation flying involving multiple spacecraft is presented. The idea is to introduce a set of way-points through which the spacecraft are required to pass combined with certain parameterization of the trajectories. The resulting energy optimal with collision avoidance constraints problem is formulated as a parameter optimization problem in terms of the way-points parameters. A numerical algorithm is proposed and used for scenarios with multiple spacecraft.

Advances in wireless and mobile communication technologies have enabled the development of various RFID-based systems and applications in addition to the extension of the tag reading range of mobile readers. Thus, it has become commonplace that multiple readers concurrently attempt to read tags within ranges of the readers. However, this concurrent access among multiple mobile readers brings about a new problem called reader collision, where a reader's transmission is interfered by other readers. There have been several studies focusing on solving the reader collision problem. These studies employ time division, frequency division, space division, or the centralized scheduling approach. In this paper, a cooperative, distributed reader collision avoidance algorithm is introduced. In particular, the proposed DiCa (Distributed Tag Access with Collision-Avoidance) is considerably suitable for energy-efficient wireless mobile network environments cooperated with RFID, since the DiCa is capable not only of avoiding collisions, but also changing power states autonomously through simple interaction of adjacent readers.

This paper examines the regulatory gap concerning redundant navigation equipment on modern commercial vessels, specifically analyzing the functional overlap between traditional course recorders, echo sounders, and Voyage Data Recorders (VDRs). Through a systematic review of International Maritime Organization (IMO) regulations, classification society guidance, flag state interpretations, and industry association publications, this research reveals a comprehensive absence of official guidance harmonizing SOLAS Chapter V Regulations 19 and 20. The findings demonstrate that VDRs record all parameters captured by standalone course recorders and echo sounders, yet shipowners continue to bear the operational costs of maintaining redundant equipment due to regulatory ambiguity. This paper argues that the regulatory framework has not adapted to technological advancement, creating unnecessary economic burden on the maritime industry while maintaining the appearance of compliance with outdated operational assumptions. The analysis concludes with recommendations for regulatory modernization and calls for IMO action to address this systematic inefficiency.