RRT* Combined with GVO for Real-time Nonholonomic Robot Navigation in Dynamic Environment

TL;DR

This paper introduces a real-time navigation framework for nonholonomic robots in dynamic environments by integrating RRT* with generalized velocity obstacles and uncertainty modeling, enhancing safety and efficiency.

Contribution

It combines GVO with RRT* for differential drive robots, incorporating uncertainty and path feasibility checks to improve nonholonomic robot navigation in dynamic settings.

Findings

Reduced trajectory uncertainty and computational time.

Enhanced obstacle avoidance safety with Kalman filter-based obstacle prediction.

Feasible path generation considering robot nonholonomic constraints.

Abstract

Challenges persist in nonholonomic robot navigation in dynamic environments. This paper presents a framework for such navigation based on the model of generalized velocity obstacles (GVO). The idea of velocity obstacles has been well studied and developed for obstacle avoidance since being proposed in 1998. Though it has been proved to be successful, most studies have assumed equations of motion to be linear, which limits their application to holonomic robots. In addition, more attention has been paid to the immediate reaction of robots, while advance planning has been neglected. By applying the GVO model to differential drive robots and by combining it with RRT*, we reduce the uncertainty of the robot trajectory, thus further reducing the range of concern, and save both computation time and running time. By introducing uncertainty for the dynamic obstacles with a Kalman filter, we dilute the risk of considering the obstacles as uniformly moving along a straight line and guarantee the safety. Special concern is given to path generation, including curvature check, making the generated path feasible for nonholonomic robots. We experimentally demonstrate the feasibility of the framework.