G²VD Planner: Efficient Motion Planning With Grid-Based Generalized Voronoi Diagrams

In this paper, an efficient motion planning approach with grid-based generalized Voronoi diagrams (G2VD) is newly proposed for mobile robots. Different from existing approaches, the novelty of this work is twofold: 1) a new state lattice-based path searching approach is proposed, in which the search space is reduced to a novel Voronoi corridor to further improve the search efficiency; 2) an efficient quadratic programming-based path smoothing approach is presented, wherein the clearance to obstacles is considered to improve the path clearance of hard-constrained path smoothing approaches. We validate the efficiency and smoothness of our approach in various challenging simulation scenarios and outdoor environments. It is shown that the computational efficiency is improved by 17.1% in the path searching stage, and path smoothing with the proposed approach is 6.6 times faster than an advanced sparse-banded structure-based path smoothing approach and 53.3 times faster than the popular timed-elastic-band planner. A video showing outdoor navigation on our campus is available at https://youtu.be/iMXGthgvp58. Note to Practitioners—This paper is motivated by the challenges of motion planning problems of mobile robots. An efficient motion planning approach called G2VD planner is proposed by combining path searching, path smoothing, and time-optimal velocity planning. Extensive simulation and experimental results show the effectiveness of the proposed motion planning approach. However, the prediction information of dynamic obstacles is not incorporated in the proposed motion planner, thus the motion planner may be a bit sluggish in response to dynamic obstacles. Furthermore, we plan to integrate the intention/trajectory prediction of pedestrians/vehicles into the proposed framework to enhance the foreseeability of the motion planner.

I. Introduction

Mobile robots have been widely applied in various fields including but not limited to intelligent inspection, logistics, and search-and-rescue applications [1], [2], [3]. Autonomous navigation is the key technology for mobile robots to achieve full autonomy in these scenarios [4], [5], which typically consists of localization [6], [7], mapping [8], [9], motion planning, and control, wherein motion planning plays an essential role in generating safe, smooth, and efficient motions [10], [11]. Although plenty of works on motion planning of mobile robots have been put forward [12], [13], [14], it is still challenging to design a motion planning approach that can ensure both efficiency, safety, and smoothness in complex environments [15], [16], [17], [18], [19].