Open AccessNeighbor Reward with Optimal Reciprocal Collision Avoidance for Swarm Agents*
Author(s) -
Lei Du,
Hao Tang,
Pengfei Li,
Tao Ma,
Shuangquan Ge,
Kang Cao
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2216/1/012082
Subject(s) - collision avoidance , swarm behaviour , reciprocal , computer science , obstacle avoidance , collision , task (project management) , swarm intelligence , action (physics) , obstacle , distributed computing , artificial intelligence , robot , machine learning , particle swarm optimization , computer security , mobile robot , engineering , linguistics , philosophy , physics , systems engineering , quantum mechanics , law , political science
Navigating in an unknown area safely is counted as the underlying work which can support swarm agents for more complex tasks. When available information of search regions are lacking, agents make real-time action decisions according to surrounding environments they have perceived. For swarm agent system, connectivity maintenance and collision avoidance are both essential. Based on optimal Reciprocal Collision Avoidance (ORCA) algorithm, we proposed a method that agents can provide assistances to surrounding agents by spreading the status information of themselves, which is the neighbor reward method (NRM). This kind of status information contains ambient information and perceptions of the task which are transferred to reward data for convenient and uniform distributions. In other words, individuals utilize inter-neighbor interactions to achieve the same high-level goal, as well as result in an intelligent independent swarm agents system. This method solves the velocity selection problem of ORCA and optimizes the obstacle avoidance of the original NRM. The algorithm has been integrated in ROS framework and simulated on GAZEBO. In the tested scenario, our method is efficient for swarm agents collision avoidance in decentralized way.