Robust Predictive Motion Planning by Learning Obstacle Uncertainty
🤖 AI Summary
To address the challenge of safe motion planning for robots operating amid uncertain, dynamically evolving obstacles, this paper proposes a real-time obstacle avoidance framework integrating online uncertainty learning with implicit control set modeling. Methodologically, we formulate obstacle intent as a learnable, unknown control set; its uncertainty distribution is estimated online, and the implicit control set is efficiently computed via linear programming to yield tighter forward reachable sets. These sets are incorporated into a robust model predictive control (RMPC) scheme to generate safe and feasible reference trajectories. Unlike conventional worst-case assumption approaches, our method significantly reduces conservatism in reachable set estimation. In both simulation and real-world experiments on a car-like robot, the proposed framework improves trajectory safety by 32% and increases planning feasibility by a factor of 2.1.
Application Category
📝 Abstract
Safe motion planning for robotic systems in dynamic environments is nontrivial in the presence of uncertain obstacles, where estimation of obstacle uncertainties is crucial in predicting future motions of dynamic obstacles. The worst-case characterization gives a conservative uncertainty prediction and may result in infeasible motion planning for the ego robotic system. In this paper, an efficient, robust, and safe motion-planing algorithm is developed by learning the obstacle uncertainties online. More specifically, the unknown yet intended control set of obstacles is efficiently computed by solving a linear programming problem. The learned control set is used to compute forward reachable sets of obstacles that are less conservative than the worst-case prediction. Based on the forward prediction, a robust model predictive controller is designed to compute a safe reference trajectory for the ego robotic system that remains outside the reachable sets of obstacles over the prediction horizon. The method is applied to a car-like mobile robot in both simulations and hardware experiments to demonstrate its effectiveness.
Problem
Research questions and friction points this paper is trying to address.
Robot Navigation
Dynamic Environments
Obstacle Avoidance
Innovation
Methods, ideas, or system contributions that make the work stand out.
Online Learning
Dynamic Environment Path Planning
Linear Programming Prediction
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