Image feedback based optimal control and the value of information in a differential game

Macias, Vladimir; Becerra, Israel; Murrieta-Cid, Rafael; Becerra, Héctor M.; Hutchinson, Seth

doi:10.1016/j.automatica.2017.12.045

Cited by 25 publications

(13 citation statements)

References 23 publications

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“…Consider the system (1) and suppose that Assumptions 1, 3 and 4 hold. Given X 0 P and X 0 E , the number q of the evaders which the pursuit team can guarantee to prevent from reaching the target region Ω tar is given by (1), ..., z(N v )] T , z(i) = 0, 1 (50) and the maximum matching is given by z * = argmax z (c T z). The parameter matrixes and vectors are defined as follows: c = ones(N v , 1), b 1 = r T , A 1 = I Nv , b 2 = ones(N e , 1), A 2 = ones(1, N v /N e ) ⊗ I Ne , b 3 = ones(N p , 1) (51) and A 3 is computed by Algorithm 2 presented below.…”

Section: Definition 8 (Execution Pursuit Coalition) a Pursuit Coalitionmentioning

confidence: 99%

“…The construction of barrier, the most central and important part in RA games, has attracted a lot of attention in pursuitevasion games and until now, achieved remarkable results [45]- [48]. For example, in [49] and [50], the authors compute the barrier for a pursuit-evasion game between an omnidirectional evader and a differential drive robot. For the problem of tracking an evader in an environment containing a corner, the method of explicit policy is used to investigate the escape set and the track set [51].…”

Section: Introductionmentioning

confidence: 99%

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Task Assignment for Multiplayer Reach–Avoid Games in Convex Domains via Analytical Barriers

2020

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This work considers a multiplayer reach-avoid game between two adversarial teams in a general convex domain which consists of a target region and a play region. The evasion team, initially lying in the play region, aims to send as many its team members into the target region as possible, while the pursuit team with its team members initially distributed in both play region and target region, strives to prevent that by capturing the evaders. We aim at investigating a task assignment about the pursuer-evader matching, which can maximize the number of the evaders who can be captured before reaching the target region safely when both teams play optimally. To address this, two winning regions for a group of pursuers to intercept an evader are determined by constructing an analytical barrier which divides these two parts. Then, a task assignment to guarantee the most evaders intercepted is provided by solving a simplified 0-1 integer programming instead of a non-deterministic polynomial problem, easing the computation burden dramatically. It is worth noting that except the task assignment, the whole analysis is analytical. Finally, simulation results are also presented.

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Section: Definition 8 (Execution Pursuit Coalition) a Pursuit Coalitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Task Assignment for Multiplayer Reach–Avoid Games in Convex Domains via Analytical Barriers

2020

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“…This problem is a new variant of the classical pursuitevasion problems [7]. Our approach is closer to the visibilitybased [8], [9] pursuit-evasion games. However, the main distinction is that in classical pursuit-evasion games, the goal of the evader (i.e., the agent in our setting) is to always evade the pursuer (i.e., the guard) whereas in our setting, the agent has to explore the environment to increase its visibility while at the same time staying away from the guard.…”

Section: Introductionmentioning

confidence: 97%

Tree Search Techniques for Minimizing Detectability and Maximizing Visibility

Zhang

Lee

Smereka

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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We introduce and study the problem of planning a trajectory for an agent to carry out a scouting mission while avoiding being detected by an adversarial guard. This introduces a multi-objective version of classical visibility-based target search and pursuit-evasion problem. In our formulation, the agent receives a positive reward for increasing its visibility (by exploring new regions) and a negative penalty every time it is detected by the guard. The objective is to find a finitehorizon path for the agent that balances the trade off between maximizing visibility and minimizing detectability.We model this problem as a discrete, sequential, twoplayer, zero-sum game. We use two types of game tree search algorithms to solve this problem: minimax search tree and Monte-Carlo search tree. Both search trees can yield the optimal policy but may require possibly exponential computational time and space. We propose several pruning techniques to reduce the computational cost while still preserving optimality guarantees. Simulation results show that the proposed strategy prunes approximately three orders of magnitude nodes as compared to the brute-force strategy. We also find that the Monte-Carlo search tree saves approximately one order of computational time as compared to the minimax search tree.

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“…The proposed problem builds on classic pursuit evasion games [8][9][10] and visibility-based scouting problems [11,12]. In classic pursuit-evasion, the evader (i.e., the agent in our setting) always tries to avoid the capture of the pursuer (i.e., the opponent).…”

Section: Introductionmentioning

confidence: 99%

Game tree search for minimizing detectability and maximizing visibility

et al. 2021

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We introduce and study the problem of planning a trajectory for an agent to carry out a scouting mission while avoiding being detected by an adversarial opponent. This introduces a multi-objective version of classical visibility-based target search and pursuitevasion problem. In our formulation, the agent receives a positive reward for increasing its visibility (by exploring new regions) and a negative penalty every time it is detected by the opponent. The objective is to find a finite-horizon path for the agent that balances the trade off between maximizing visibility and minimizing detectability.We model this problem as a discrete, sequential, two-player, zero-sum game. We use two types of game tree search algorithms to solve this problem: minimax search tree and Monte-Carlo search tree. Both search

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Image feedback based optimal control and the value of information in a differential game

Cited by 25 publications

References 23 publications

Task Assignment for Multiplayer Reach–Avoid Games in Convex Domains via Analytical Barriers

Task Assignment for Multiplayer Reach–Avoid Games in Convex Domains via Analytical Barriers

Tree Search Techniques for Minimizing Detectability and Maximizing Visibility

Game tree search for minimizing detectability and maximizing visibility

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