Simon X . Yang

University of Guelph, School of Engineering, Faculty Member

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Simon X. Yang received the B.Sc. degree in engineering physics from Beijing University, Beijing, China, in 1987, the first of two M.Sc. degrees in biophysics from the Chinese Academy of Sciences, Beijing, in 1990, the second M.Sc. degree in electrical engineering from the University of Houston, Houston, TX, in 1996, and the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, AB, Canada, in 1999. Dr. Yang is currently a Professor and the Head of the Advanced Robotics and Intelligent Systems Laboratory at the University of Guelph, Guelph, ON, Canada. His research interests include artificial intelligent, robotics, sensors and multi-sensor fusion, wireless sensor networks, control systems, bio-inspired intelligence, machine learning, neural networks, fuzzy systems, and computational neuroscience.

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Design and Control of an Intelligent Wheeled Robot

Mobile robotics is an emerging interdisciplinary, which lacks a standardized architecture. With t... more Mobile robotics is an emerging interdisciplinary, which lacks a standardized architecture. With the development of embedded system, Raspberry Pi has become a new control scheme for mobile robots with powerful processing ability, convenient network connectivity, real-time display of images and videos. In this paper, a wheeled mobile robot is designed. The kinematic model of the robot is developed. A series of experiments of motion control are conducted. A control strategy is proposed based on an attitude detection sensor and ultrasonic sensor to enable the robot with the capability of state perception and autonomous obstacle avoidance. The experimental and simulation results in different environments demonstrate the effectiveness of the developed wheeled mobile robot.

Tracking control of a mobile robot using a neural dynamics based approach

In this paper, a novel tacking control approach is proposed for real-time navigation of a nonholo... more In this paper, a novel tacking control approach is proposed for real-time navigation of a nonholonomic mobile robot. The proposed tracking controller is based on the error dynamics analysis of the mobile robot and a neural dynamics model derived from Hodgkin and Huxley's membrane model of a biological system. The stability of the control system and the convergence of tracking errors to zeros are guaranteed by a Lyapunov stability theory. Unlike many tracking control methods for mobile robot where the generated control velocities start with large initial velocities, the proposed neural dynamics based approach is capable of generating smooth, continuous robot control signals with zero initial velocities. In addition, it can deal with the situation with a very large tracking error. The effectiveness and efficiency are demonstrated by comparison and simulation studies.

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Fuzzy Linguistic Odor Cognition for Robotics Olfaction

IEEE Transactions on Cognitive and Developmental Systems, Jun 1, 2019

The robotic olfaction helps the robot to measure the odor and provides it the environment recogni... more The robotic olfaction helps the robot to measure the odor and provides it the environment recognition ability. This paper proposes a fuzzy linguistic odor cognition method for robotics olfaction. The language is the presentation mode and the information medium of human mind. The core idea of the proposed method is that the fuzzy linguistic rules are used to realize the brain-inspired knowledge representation. The proposed method is a cognitive model which consists of a set of fuzzy linguistic rules. The input variables of the rule antecedents are the signals of different gas sensors on the robotic electronic nose. The rule consequent is the odor label. The fuzzy linguistic rules are initialized by the density-based spatial clustering of applications with noise. The parameters of the membership functions and the rule consequents are estimated by an iterative optimization process. The experiments are performed on two real robotics olfaction data sets obtained under uncontrolled realistic conditions. The proposed method is compared with decision tree, ${k}$ -nearest neighbor (KNN), support vector machine (SVM), and backpropagation neural network (BPNN). The experimental results verify that the effectiveness of the proposed method is better than that of decision tree, KNN, SVM, and BPNN.

Machine Learning with Applications to Autonomous Systems

Mathematical Problems in Engineering, 2015

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A multilayer neural network model for perception of rotational motion

Science in China Series C-Life Sciences, Feb 1, 1997

A multilayer neural nerwork model for the perception of rotational motion has been developed usin... more A multilayer neural nerwork model for the perception of rotational motion has been developed using Reichardt&#39;s motion detector array of correlation type, Kohonen&#39;s self-organized feature map and Schuster-Wagner&#39;s oscillating neural network. It is shown that the unsupervised learning could make the neurons on the second layer of the network tend to be self-organized in a form resembling columnar organization of selective directions in area MT of the primate&#39;s visual cortex. The output layer can interpret rotation information and give the directions and velocities of rotational motion. The computer simulation results are in agreement with some psychophysical observations of rotational perception. It is demonstrated that the temporal correlation between the oscillating neurons would be powerful for solving the &quot;binding problem&quot; of shear components of rotational motion.

A Ranging Model Based on BP Neural Network

Intelligent Automation and Soft Computing, Nov 10, 2015

AbstractThe traditional Shadowing model relies too much on parameters and specific environment, s... more AbstractThe traditional Shadowing model relies too much on parameters and specific environment, so its application occasions in WSN are restricted. This paper studies the wireless signal propagation model and proposes the ranging model based on BP neural network model, it has the ability of autonomous learning according to different environments, and can improve the precision of measuring distance, then enhance the adaptability to the environment. The simulation results show that the ranging model based on BP neural network model can reduce the ranging error, which has the adaptability to the environment.

Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments

International Journal of Advanced Robotic Systems, 2014

The multi-AUV hunting problem is one of the key issues in multi-robot system research. In order t... more The multi-AUV hunting problem is one of the key issues in multi-robot system research. In order to hunt the target efficiently a new hunting algorithm based on a bio-inspired neural network has been proposed in this paper. Firstly, the AUV's working environment can be represented, based on the biological-inspired neural network model. There is one-to-one correspondence between each neuron in the neural network and the position of the grid map in the underwater environment. The activity values of biological neurons then guide the AUV's sailing path and finally the target is surrounded by AUVs. In addition, a method called negotiation is used to solve the AUV's allocation of hunting points. The simulation results show that the algorithm used in the paper can provide rapid and highly efficient path planning in the unknown environment with obstacles and non-obstacles.

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A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

Computational Intelligence and Neuroscience, 2017

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challengi... more Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

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Observer-Based Adaptive Neural Network Trajectory Tracking Control for Remotely Operated Vehicle

IEEE transactions on neural networks and learning systems, Jul 1, 2017

This paper focuses on the adaptive trajectory tracking control for a remotely operated vehicle (R... more This paper focuses on the adaptive trajectory tracking control for a remotely operated vehicle (ROV) with an unknown dynamic model and the unmeasured states. Unlike most previous trajectory tracking control approaches, in this paper, the velocity states and the angular velocity states in the body-fixed frame are unmeasured, and the thrust model is inaccurate. Obviously, it is more in line with the actual ROV systems. Since the dynamic model is unknown, a new local recurrent neural network (local RNN) structure with fast learning speed is proposed for online identification. To estimate the unmeasured states, an adaptive terminal sliding-mode state observer based on the local RNN is proposed, so that the finite-time convergence of the trajectory tracking error can be guaranteed. Considering the problem of inaccurate thrust model, an adaptive scale factor is introduced into thrust model, and the thruster control signal is considered as the input of the trajectory tracking system directly. Based on the local RNN output, the adaptive scale factor, and the state estimation values, an adaptive trajectory tracking control law is constructed. The stability of the trajectory tracking control system is analyzed by the Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations.

Bio-inspired artificial intelligence with applications to various robotic systems

Neural-Dynamics-Based Path Planning of a Bionic Robotic Fish

Without the ability of reversing, the bionic robotic fish has certain peculiarity compared with o... more Without the ability of reversing, the bionic robotic fish has certain peculiarity compared with other robots, and it is more difficult to complete the specified tasks. Therefore, in this paper, a neural-dynamics-based path planning method applied to a bionic robotic fish is discussed. In this method, the target, obstacles and map are regarded as the pixel points under the coverage of the given area. Avoid the overlap of the pixel points of the target and the obstacles to realize the obstacle avoidance. The method is computationally intensive and adaptable, and the theoretical optimal path can be planned for various motion modes.

Path Planning of Arbitrary Shaped Mobile Robots With Safety Consideration

IEEE Transactions on Intelligent Transportation Systems, Sep 1, 2022

A novel foraging algorithm for swarm robotics based on virtual pheromones and neural network

Applied Soft Computing, May 1, 2020

Abstract Swarm robotics is an emerging interdisciplinary field that has many potential real-world... more Abstract Swarm robotics is an emerging interdisciplinary field that has many potential real-world applications. Swarm robotics aims to produce robust, scalable, and flexible self-organizing behaviors through local interactions from a large number of simple robots. In this paper, a novel pheromone model of swarm foraging behavior is developed based on a neural network. The output of a single neuron corresponds to the density of a pheromone, which diffuses to neighboring neurons through their local connections. A neural network is updated based on the proposed evaporation model. Neural networks can often mimic the dynamics and features of pheromones. Therefore, in this work, we develop an optimization method to determine the key parameters of cooperative foraging based on mathematical modeling. The differential equation variables represent the number of foraging robots assigned different tasks. The solutions of the differential equations represent the dynamics of the foraging behavior. The key parameters that affect task allocation are determined to make optimal decision rules. Simulation experiments are conducted under different foraging scenarios. The experimental results demonstrate the effectiveness of the proposed pheromone model.

Bioinspired Neural Network-Based Q-Learning Approach for Robot Path Planning in Unknown Environments

International Journal of Robotics & Automation, 2016

A Survey on Robot Path Planning using Bio-inspired Algorithms

The path planning problem has been an essential topic in autonomous robotics. There are many new ... more The path planning problem has been an essential topic in autonomous robotics. There are many new path planning methods that have been developed, and those using bioinspired algorithms attract extensive attention. This paper analyzes several bio-inspired algorithms and surveys recent developments of robot path planning. Swarm intelligence, evolutionary algorithms, and neurodynamics are three primary robot path planning methods that are focused on in this survey. In addition, the pros and cons of these three methods are summarized and analyzed.

Dynamic Bioinspired Neural Network for Multi-Robot Formation Control in Unknown Environments

International Journal of Robotics & Automation, 2015

The CFD-Based Neural Dynamic Path Planner for Multi-Robot System

International Journal of Robotics & Automation, 2022

Intelligent Pattern Recognition Technology and Applications

Intelligent Automation and Soft Computing, Dec 1, 2013

Intelligent Automation with Applications to Agriculture

Intelligent Automation and Soft Computing, Apr 2, 2016

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Real-time collision-free path planning and tracking control of a nonholonomic mobile robot using a biologically inspired approach

In this paper, a novel biologically inspired neural network approach is proposed for real-time co... more In this paper, a novel biologically inspired neural network approach is proposed for real-time collision-free path planning and tracking control of a nonholonomic mobile robot in a nonstationary environment. The real-time robot trajectory with obstacle avoidance is generated by a topologically organized neural network, where the dynamics of each neuron is characterized by a shunting equation. The varying environment is represented by the dynamic activity landscape of the neural network, where the neural activity propagation is subject to the kinematic constraint of the nonholonomic mobile robots. The real-time tracking velocities are generated by a novel neural dynamics based controller, which is based on two shunting models and the backstepping technique. Unlike the backstepping controllers that produce non-smooth velocity commands with sharp jumps, the proposed tracking controller is capable of generating smooth, continuous commands not suffering from velocity jumps. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies.

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Simon X . Yang

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