Real-Time Sampling-Based Safe Motion Planning for Robotic Manipulators in Dynamic Environments

TL;DR

This paper introduces the DRGBT algorithm, a real-time sampling-based motion planner for robotic manipulators operating safely in dynamic environments, including scenarios with humans, using efficient computation on standard hardware.

Contribution

The paper presents the DRGBT algorithm with detailed time analysis and a novel dynamic expanded bubble structure for guaranteed safe motion planning in real-time.

Findings

Effective real-time performance demonstrated in simulations

Successful real-world robot experiments with human presence

Operates on low-cost hardware without GPUs

Abstract

In this paper, we present the main features of Dynamic Rapidly-exploring Generalized Bur Tree (DRGBT) algorithm, a sampling-based planner for dynamic environments. We provide a detailed time analysis and appropriate scheduling to facilitate a real-time operation. To this end, an extensive analysis is conducted to identify the time-critical routines and their dependence on the number of obstacles. Furthermore, information about the distance to obstacles is used to compute a structure called dynamic expanded bubble of free configuration space, which is then utilized to establish sufficient conditions for a guaranteed safe motion of the robot while satisfying all kinematic constraints. An extensive randomized simulation trial is conducted to compare the proposed algorithm to a competing state-of-the-art method. Finally, an experimental study on a real robot is carried out covering a variety of scenarios including those with human presence. The results show the effectiveness and feasibility of real-time execution of the proposed motion planning algorithm within a typical sensor-based arrangement, using cheap hardware and sequential architecture, without the necessity for GPUs or heavy parallelization.