Distributed Adaptive Consensus with Obstacle and Collision Avoidance for Networks of Heterogeneous Multi-Agent Systems

TL;DR

This paper introduces a distributed adaptive control method for heterogeneous multi-agent systems that achieves leader-following consensus while effectively avoiding obstacles and collisions through potential functions and neural network-based disturbance estimation.

Contribution

It presents a novel adaptive control strategy that combines neural network disturbance estimation with collision avoidance for heterogeneous multi-agent systems.

Findings

Achieves leader-following formation consensus.

Ensures stability under fixed network topologies.

Effectively manages obstacle and collision avoidance.

Abstract

This paper presents a distributed adaptive control strategy for multi-agent systems with heterogeneous dynamics and collision avoidance. We propose an adaptive control strategy designed to ensure leader-following formation consensus while effectively managing collision and obstacle avoidance using potential functions. By integrating neural network-based disturbance estimation and adaptive tuning laws, the proposed strategy ensures consensus and stability in leader-following formations under fixed topologies.