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Dynamic Obstacle-Avoidance Algorithm for Multi-Robot Flocking Based on Improved Artificial Potential Field
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DOI：10.1109/TCE.2023.3340327.png)
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Abstract
En 中文
In unknown or complex environments, the security threats of multi-robot system come from various aspects. Security protection requires that robots can avoid collisions with each other while meeting obstacles to ensure overall stability and safety. This study proposes a predictable and highly secure multi-robot flocking obstacle avoidance algorithm based on an improved artificial potential field (MRF-IAPF). In the proposed MRF-IAPF algorithm, the robot detects an obstacle and calculates its inhibiting velocity based on the associated dynamic characteristics. The inhibiting velocity is then combined with the interaction velocity between robots and their neighbors, as well as the interaction velocity between robots and the target point to obtain the control inputs that can maintain the robot flocking state and implement dynamic obstacle avoidance, simultaneously. The control part adopts decentralized manner. When some robots are attacked or faulty, others are not affected. Thus, the proposed approach can dynamically adjust to increase the safety and stability of the system. Theoretical analysis and experimental results verify that the proposed obstacle avoidance algorithm can effectively avoid dynamic obstacles and maintain the state of a multi-robot flocking to avoid accidents in the multi-robot system.
Keywords:
Robots
Collision avoidance
Heuristic algorithms
Lattices
Multi-robot systems
Prediction algorithms
Path planning
Artificial potential field
dynamic obstacles
flocking
multi-robot
security protection
Journal
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10.9
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5.1K
Citations:
6.8K
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