Strategies of Pursuit-Evasion Game Based on Improved Potential Field and Differential Game Theory for Mobile Robots

Dong, Jie; Zhang, Xu; Jia, Xuemei

doi:10.1109/imccc.2012.340

Cited by 17 publications

(14 citation statements)

References 4 publications

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“…Numerical solutions to differential games by a sequence of finite state Markov games has been presented in [6] where players are assumed to be moving at constant speed. In [7], Improved Potential field method has been used for solving pursuit-evasion problem. A novel incremental sampling-based algorithm to compute the open-loop solutions for the evader assuming worst case scenario for the pursuer is presented in [8].…”

Section: Introductionmentioning

confidence: 99%

Pursuit-evasion Game for Nonholonomic Mobile Robots With Obstacle Avoidance using NMPC

Sani

Robu

Hably

2020

2020 28th Mediterranean Conference on Control and Automation (MED)

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In this work, non-cooperative competitive games between two unmanned ground robots using Nonlinear Model Predictive Control (NMPC) while incorporating obstacle avoidance techniques are studied. The objective of the first player (pursuer) is to minimize the relative distance and orientation between itself and the second player (evader) while avoiding obstacles, whereas the evader does the opposite. The Pursuit-Evasion Game (PEG) being a typical class of a differential game is formulated as a zero-sum game with two homogeneous players in five different game scenarios. The objective function of each player is formulated as a double optimization problem and is solved separately using NMPC techniques. The optimal trajectory of each player is computed iteratively by considering the best response of the opponent player. The level of information is assumed to be symmetric. Simulations of various scenarios show the winning possibility of each player.

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Section: Introductionmentioning

confidence: 99%

Pursuit-evasion Game for Nonholonomic Mobile Robots With Obstacle Avoidance using NMPC

Sani

Robu

Hably

2020

2020 28th Mediterranean Conference on Control and Automation (MED)

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“…In this example, among all Nash equilibrium, the evader selects the one which optimizes its deterministic distance to the pursuers' team. In order to resolve the problems often encountered in the algorithms of pursuit-evasion games such as computational complexity and the lack of universality, Dong et al [15] propose a hybrid algorithm founded on improved dynamic artificial potential field and differential game, where Nash equilibrium solution is optimal for both pursuer and evader in barrier-free zone in pursuit-evasion game, and in accordance with environment changes around the pursuit elements the algorithm is applied with flexibility. Moreover in [16], Amigoni and Basilico have presented an approach to calculate the optimal pursuer's strategy that maximizes the probability of the target's capture in a given environment.…”

Section: Related Workmentioning

confidence: 99%

“…The classic question relating game theory to multiagent systems is "what is the best action that an agent can perform?" This principle has been widely used in multiagent pursuit problems [14][15][16][17]. The negotiation based on Game Theory is focused on the value and rewards of each agent, which appropriately reflect the objective of agent's negotiation (satisfying the goal of each agent).…”

Section: Introductionmentioning

confidence: 99%

A New Decentralized Approach of Multiagent Cooperative Pursuit Based on the Iterated Elimination of Dominated Strategies Model

Souidi

Piao

2016

Mathematical Problems in Engineering

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Game Theory is a promising approach to acquire coalition formations in multiagent systems. This paper is focused on the importance of the distributed computation and the dynamic formation and reformation of pursuit groups in pursuit-evasion problems. In order to address this task, we propose a decentralized coalition formation algorithm based on the Iterated Elimination of Dominated Strategies (IEDS). This Game Theory process is common to solve problems requiring the withdrawal of dominated strategies iteratively. Furthermore, we have used the Markov Decision Process (MDP) principles to control the motion strategy of the agents in the environment. The simulation results demonstrate the feasibility and the validity of the given approach in comparison with different decentralized methods.

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“…A number of articles [54,61,[63][64][65]100] investigated pursuit-evasion games with slow evaders, where the capture of the evader is always guaranteed. However, in real-world applications, evaders may run with speed similar to or higher than the speed of pursuers.…”

Section: Motivationmentioning

confidence: 99%

“…A variety of pursuit-evasion games have been studied in different contexts [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71].…”

Section: The Pursuit-evasion Gamementioning

confidence: 99%

On Multi-Agent Reinforcement Learning in Matrix, Stochastic and Differential Games

Awheda¹

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In this thesis, we investigate reinforcement learning algorithms on matrix, stochastic, and differential games. In matrix and stochastic games, the states and actions are represented in continuous domains. We propose two decentralized multi-agent reinforcement learning algorithms to solve the problem of learning in matrix and stochastic games when the learning agent has only minimum knowledge about the underlying game and the other learning agents. The proposed algorithms are the constant learning rate-based exponential moving average Q-learning (CLR-EMAQL) algorithm, and the exponential moving average Q-learning (EMAQL) algorithm. We mathematically show that the proposed CLR-EMAQL algorithm converges to Nash equilibrium in games with pure Nash equilibrium. We introduce the concept of Win-or-Learn-Slow (WoLS) mechanism for the proposed EMAQL algorithm so that the proposed algorithm learns fast when it is winning, and learns cautiously when it is losing. We also provide a theoretical proof of convergence to Nash equilibrium for the proposed EMAQL algorithm in games with pure Nash equilibrium. In games with mixed Nash equilibrium, our mathematical analysis shows that the proposed EMAQL algorithm converges to an equilibrium. Although our mathematical analysis does not explicitly show that the proposed EMAQL algorithm converges to Nash equilibrium, our simulation results indicate that the proposed EMAQL algorithm does converge to Nash equilibrium. Our simulation iii I would like to express my deepest gratitude to my advisor, Professor Howard M.

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Strategies of Pursuit-Evasion Game Based on Improved Potential Field and Differential Game Theory for Mobile Robots

Cited by 17 publications

References 4 publications

Pursuit-evasion Game for Nonholonomic Mobile Robots With Obstacle Avoidance using NMPC

Pursuit-evasion Game for Nonholonomic Mobile Robots With Obstacle Avoidance using NMPC

A New Decentralized Approach of Multiagent Cooperative Pursuit Based on the Iterated Elimination of Dominated Strategies Model

On Multi-Agent Reinforcement Learning in Matrix, Stochastic and Differential Games

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