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Multirobot Collaborative Pursuit Target Robot by Improved MADDPG

Journal Article · · Computational Intelligence and Neuroscience
DOI:https://doi.org/10.1155/2022/4757394· OSTI ID:1846468
 [1];  [1];  [1];  [2];
  1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
  2. Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
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Policy formulation is one of the main problems in multirobot systems, especially in multirobot pursuit-evasion scenarios, where both sparse rewards and random environment changes bring great difficulties to find better strategy. Existing multirobot decision-making methods mostly use environmental rewards to promote robots to complete the target task that cannot achieve good results. This paper proposes a multirobot pursuit method based on improved multiagent deep deterministic policy gradient (MADDPG), which solves the problem of sparse rewards in multirobot pursuit-evasion scenarios by combining the intrinsic reward and the external environment. The state similarity module based on the threshold constraint is as a part of the intrinsic reward signal output by the intrinsic curiosity module, which is used to balance overexploration and insufficient exploration, so that the agent can use the intrinsic reward more effectively to learn better strategies. The simulation experiment results show that the proposed method can improve the reward value of robots and the success rate of the pursuit task significantly. The intuitive change is obviously reflected in the real-time distance between the pursuer and the escapee, the pursuer using the improved algorithm for training can get closer to the escapee more quickly, and the average following distance also decreases.

Sponsoring Organization:
USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research
OSTI ID:
1846468
Journal Information:
Computational Intelligence and Neuroscience, Journal Name: Computational Intelligence and Neuroscience Vol. 2022; ISSN 1687-5265
Publisher:
Hindawi Publishing CorporationCopyright Statement
Country of Publication:
Country unknown/Code not available
Language:
English

References (7)

Search and pursuit-evasion in mobile robotics: A survey journal July 2011
Policy Improvements for Probabilistic Pursuit-Evasion Game journal July 2013
Human-level control through deep reinforcement learning journal February 2015
Nonlinear Model Predictive Control for Omnidirectional Robot Motion Planning and Tracking With Avoidance of Moving Obstacles journal July 2014
Curiosity-Driven Exploration by Self-Supervised Prediction conference July 2017
Pursuers-Coalition Construction Algorithm in Multi-robot Pursuit-Evasion Game journal January 2013
A New Approach of Multi-Robot Cooperative Pursuit Based on Association Rule Data Mining journal December 2009

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