Reactive Planning based Control for Mobile Robots in Obstacle-Cluttered Environments

2026-05-13

AI Summary

This work addresses the challenge of enabling mobile robots to achieve collision-free navigation in cluttered environments using only local sensing. To this end, the paper proposes a reactive planning and adaptive trajectory tracking control strategy (RPCS) that generates and dynamically refines reference trajectories online for obstacle avoidance, while an adaptive tracking controller ensures precise following of the adjusted paths. The key innovation lies in unifying a reactive planning strategy (RPS), an adaptive tracking control strategy (ATCS), and discretization techniques within a local perception framework, thereby enabling seamless coordination between dynamic obstacle avoidance and high-accuracy trajectory tracking. Simulation results demonstrate that the proposed method effectively guarantees collision-free motion while maintaining superior tracking performance in complex, densely obstructed scenarios.

Abstract

This paper addresses the motion control problem for mobile robots in obstacle-cluttered environments. The mobile robot has partial environment information only, and aims to move from an initial position to a target position without collisions. For this purpose, a reactive planning based control strategy (RPCS) is proposed. First, the initial and target positions are connected as a reference trajectory. Then, a reactive planning strategy (RPS) is developed to ensure the collision avoidance by modifying the reference trajectory locally based on the partial environment information. Next, an adaptive tracking control strategy (ATCS) is proposed to track the reference trajectory with potentially local modifications via the discretization techniques. Finally, the RPS and ATCS are combined to establish the RPCS, whose efficacy and advantages are illustrated by numerical examples.

Problem

• mobile robots
• obstacle-cluttered environments
• motion control
• collision avoidance
• partial environment information

Innovation

• reactive planning
• collision avoidance
• adaptive tracking control
• mobile robots
• partial environment information