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Connectivity Preservation and Collision Avoidance in Multi-Agent Systems Using Model Predictive Control
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AI
DOI：10.1109/TNSE.2023.3234720.png)
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Abstract
En 中文
This paper presents an innovative predictive control scheme based on a potential field in order to maintain the connectivity of a flock of agents in a leader-follower configuration with dynamic topology. We consider a group of agents navigating in an environment with obstacles towards their target. The followers avoid collisions with each other and obstacles without using any communication links. It is also required to maintain connectivity with the leader, which is unidentified to the followers. The potential field is dynamically updated by introducing time-varying weighted links between the followers to preserve connectivity as we assume only the leader knows the target position. The values of these weights are adjusted continuously according to agents' trajectories by which the critical neighbours of each agent are determined. The superior performance of the proposed predictive controller for navigation of agents to quickly reach their target is demonstrated comparatively by simulation.
Keywords:
Navigation
Sensors
Network topology
Multi-agent systems
Collision avoidance
Predictive control
Trajectory
multi-agent systems
obstacle avoidance
path planning
predictive control
Journal
I
IF:
7.9
Papers:
2.5K
Citations:
10.0K