Trajectory Planning for Non-Communicating Mobile Robots using Inverse Optimal Control

2026-05-29

AI Summary

This work addresses the challenge of collision-free coordination among multiple mobile robots operating without communication in environments where target states are unknown. The authors propose a trajectory planning framework that integrates inverse optimal control with a self-predictive perspective. By inferring other agents’ intended goals from observed trajectories and jointly predicting their future paths from their own perspectives, each robot embeds these predictions into its own optimization-based planning process. This approach represents the first integration of inverse optimal formulate control with multi-agent self-prediction, enabling efficient, communication-free coordination while guaranteeing the feasibility of the planning problem. Simulation results across systems of 2 to 8 robots demonstrate a 9.8% improvement in median task completion time compared to a constant-acceleration baseline.

Abstract

To enable an efficient interaction of non-communicating mobile robots in collision avoidance scenarios, we present a novel combined trajectory planning and prediction algorithm. Inverse optimal control is used to estimate unknown goal states of all robots based on observed past trajectories. Each robot also takes the perspective of other robots in considering self-prediction and solves a joint prediction problem using the estimated goal states. The resulting predictions are then considered for planning. Simulation results of scenarios with 2-8 robots show that the median of the durations until all vehicles reach their goals is 9.8 % faster compared to planning with constant acceleration based estimated goal states. Moreover, the proposed approach never leads to the solver being unable to find a solution to the planning or prediction problem.

Problem

• trajectory planning
• non-communicating robots
• collision avoidance
• inverse optimal control
• goal estimation

Innovation

• inverse optimal control
• trajectory planning
• non-communicating robots
• goal state estimation
• joint prediction