Formation Control of Multi-agent System with Local Interaction and Artificial Potential Field
A novel local interaction control method (LICM) is proposed in this paper to realize the formation control of multi-agent system (MAS). A local interaction leader follower (LILF) structure is provided by coupling the advantages of information consensus and leader follower frame, the agents can obtain the state information of the leader by interacting with their neighbours, which will reduce the communication overhead of the system and the dependence on a single node of the topology. In addition, the artificial potential field (APF) method is introduced to achieve obstacle avoidance and collision avoidance between agents. Inspired by the stress response of animals, a stress response mechanism-artificial potential field (SRM-APF) is proposed, which will be triggered when the local minimum problem of APF occurs. Ultimately, the simulation experiments of three formation shapes, including triangular formation, square formation and hexagonal formation, validate the effectiveness of the proposed method.
Code (0)
등록된 구현이 없습니다.
Tasks
Collision AvoidanceSimilar Papers 제목 키워드 기반
Event-Triggered Tracking Control of Networked Multi-Agent Systems
This paper studies the tracking control problem of networked multi-agent systems under both multiple networks and event-triggered mechanisms. Multiple networks are to connect multiple agents and reference systems with de…
Learning Predictive Communication by Imagination in Networked System Control
Dealing with multi-agent control in networked systems is one of the biggest challenges in Reinforcement Learning (RL) and limited success has been presented compared to recent deep reinforcement learning in single-agent …
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Adaptive Distributed Observer-based Model Predictive Control for Multi-agent Formation with Resilience to Communication Link Faults
In order to address the nonlinear multi-agent formation tracking control problem with input constraints and unknown communication faults, a novel adaptive distributed observer-based distributed model predictive control m…
Model Predictive ControlOn the observability of relative positions in left-invariant multi-agent control systems and its application to formation control
We consider the localization problem between agents while they run a formation control algorithm. These algorithms typically demand from the agents the information about their relative positions with respect to their nei…
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning
Multi-agent formation as well as obstacle avoidance is one of the most actively studied topics in the field of multi-agent systems. Although some classic controllers like model predictive control (MPC) and fuzzy control …
Model Predictive ControlMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1