Papers by Andreas Kolling
Abstract. In practical applications of robot swarms with bio-inspired behaviors, a human operator... more Abstract. In practical applications of robot swarms with bio-inspired behaviors, a human operator will need to exert control over the swarm to fulfill the mission objectives. In many operational settings, human operators are remotely located and the communication environment is harsh. Hence, there exists some latency in information (or control command) transfer between the human and the swarm.
Abstract Human interaction with robot swarms (HSI) is a young field with very few user studies th... more Abstract Human interaction with robot swarms (HSI) is a young field with very few user studies that explore operator behavior. All these studies assume perfect communication between the operator and the swarm. A key challenge in the use of swarm robotic systems in human supervised tasks is to understand human swarm interaction in the presence of limited communication bandwidth, which is a constraint arising in many practical scenarios.
Synchronous video has long been the preferred mode for controlling remote robots with other modes... more Synchronous video has long been the preferred mode for controlling remote robots with other modes such as asynchronous control only used when unavoidable as in the case of interplanetary robotics. We identify two basic problems for controlling multiple robots using synchronous displays: operator overload and information fusion. Synchronous displays from multiple robots can easily overwhelm an operator who must search video for targets.
Abstract In practical applications of robot swarms with bioinspired behaviors, a human operator w... more Abstract In practical applications of robot swarms with bioinspired behaviors, a human operator will need to exert control over the swarm to fulfill the mission objectives. In many operational settings, human operators are remotely located and the communication environment is harsh. Hence, there exists some latency in information (or control command) transfer between the human and the swarm.
Abstract—Autonomous swarm algorithms have been studied extensively in the past several years. How... more Abstract—Autonomous swarm algorithms have been studied extensively in the past several years. However, there is little research on the effect of injecting human influence into a robot swarm—whether it be to update the swarm's current goals or reshape swarm behavior. While there has been growing research in the field of human-swarm interaction (HSI), no previous studies have investigated how humans interact with swarms under communication latency.
Abstract—Swarm robots use simple local rules to create complex emergent behaviors. The simplicity... more Abstract—Swarm robots use simple local rules to create complex emergent behaviors. The simplicity of the local rules allows for large numbers of low-cost robots in deployment, but the same simplicity creates difficulties when deploying in many applicable environments. These complex missions sometimes require human operators to influence the swarms towards achieving the mission goals. Human swarm interaction (HSI) is a young field with few user studies exploring operator behavior.
Abstract—In this paper we present an approach for a pursuit-evasion problem that considers a 2.5 ... more Abstract—In this paper we present an approach for a pursuit-evasion problem that considers a 2.5 d environment represented by a height map. Such a representation is particularly suitable for large-scale outdoor pursuit-evasion. By allowing height information we not only capture some aspects of 3d visibility but can also consider target heights. In our approach we construct a graph representation of the environment by sampling points and their detection sets which extend the usual notion of visibility.
Abstract Hidden Markov Models (HMMs) today are the method of choice for blackbox modelling of sym... more Abstract Hidden Markov Models (HMMs) today are the method of choice for blackbox modelling of symbolic, stochastic time series with memory. HMMs are usually trained using the expectation-maximization (EM) algorithm. This learning algorithm is note entirely satisfactory due to slow convergence and the presence of many globally suboptimal solutions. Observable operator models (OOMs) present an alternative.
Hierarchical visibility for guaranteed search in large-scale outdoor terrain
Searching for moving targets in large environments is a challenging task that is relevant in seve... more Searching for moving targets in large environments is a challenging task that is relevant in several problem domains, such as capturing an invader in a camp, guarding security facilities, and searching for victims in large-scale search and rescue scenarios. The guaranteed search problem is to coordinate the search of a team of agents to guarantee the discovery of all targets. In this paper we present a self-contained solution to this problem in 2.5D real-world domains represented by digital elevation models (DEMs). We introduce hierarchical sampling on DEMs for selecting heuristically the close to minimal set of locations from which the entire surface of the DEM can be guarded. Locations are utilized to form a search graph on which search strategies for mobile agents are computed. For these strategies schedules are derived which include agent paths that are directly executable in the terrain. Presented experimental results demonstrate the performance of the method. The practical feasibility of our approach has been validated during a field experiment at the Gascola robot training site where teams of humans equipped with iPads successfully searched for adversarial and omniscient evaders. The field demonstration is the largest-scale implementation of a guaranteed search algorithm to date.
Robotics, IEEE Transactions on, Jan 1, 2010
We present Graph-Clear, a novel pursuit-evasion problem on graphs which models the detection of i... more We present Graph-Clear, a novel pursuit-evasion problem on graphs which models the detection of intruders in complex indoor environments by robot teams. The environment is represented by a graph, and a robot team can execute sweep and block actions on vertices and edges respectively. A sweep action detects intruders in a vertex and represents the capability of the robot team to detect intruders in the region associated to the vertex. Similarly, a block action prevents intruders from crossing an edge and represents the capability to detect intruders as they move between regions. Both actions may require multiple robots to be executed. A strategy is a sequence of block and sweep actions detecting all intruders. When solving instances of Graph-Clear the goal is to determine optimal strategies, i.e. strategies using the least number of robots. We prove that for the general case of graphs the problem of computing optimal strategies is NP-hard. Next, for the special case of trees we provide a polynomial time algorithm. The algorithm ensures that throughout the execution of the strategy all cleared vertices form a connected subtree, and we show it produces optimal strategies.
The International Journal of Robotics …, Jan 1, 2007
This paper presents a distributed control algorithm for multi-target surveillance by multiple rob... more This paper presents a distributed control algorithm for multi-target surveillance by multiple robots. Robots equipped with sensors and communication devices discover and track as many evasive targets as possible in an open region. The algorithm utilizes information from sensors, communication, and a mechanism to predict the minimum time before a robot loses a target. Workload is shared locally between robots using a greedy assignment of targets. Across long distances robots cooperate through explicit communication. The approach is coined Behavioral Cooperative Multi-robot Observation of Multiple Moving Targets. A formal representation of the proposed algorithm as well as proofs of performance guarantee are provided. Extensive simulations confirm the theoretical results in practice.
Older adults as adaptive decision makers: Evidence from the Iowa gambling task
Older adults process emotional information differently than younger adults and may demonstrate le... more Older adults process emotional information differently than younger adults and may demonstrate less of a negativity bias on cognitive tasks. The Iowa Gambling Task designed by A. Bechara, H. Damasio, D. Tranel, and A. R. Damasio (1997) has been used to examine the integration of emotion and cognition in a risky-choice decision task and may give insight into differences in the decision-making strategies in younger and older adults. Eighty-eight younger adults (18-34 years) and 67 older adults (65-88 years) completed the Iowa Gambling Task. Using a theoretical decomposition of the task designed by J. R. Busemeyer and J. C. Stout (2002), the authors found that both groups were successful at solving the task but used very different strategies that reflected each group's strength. For younger adults, that strength was learning and memory. For older adults, that strength was an accurate representation of wins and losses (valence).
Towards Human Control of Robot Swarms
In this paper we address the problem of enabling operators to control large swarms of robots for ...
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Papers by Andreas Kolling