Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

TL;DR

This paper presents a decentralized MPC approach for multi-robot target tracking that integrates obstacle avoidance through convexified potential field constraints, enabling flexible and cooperative navigation in dynamic environments.

Contribution

It introduces a novel convex optimization framework embedding potential field obstacle avoidance as constraints within decentralized MPC for multi-robot systems.

Findings

The method effectively avoids obstacles in simulations.

It demonstrates convergence and cooperative tracking capabilities.

The approach handles dynamic environments without predefined trajectories.

Abstract

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies in different environmental scenarios to showcase the convergence and efficacy of the proposed algorithm. Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M}