Improve Computing Efficiency and Motion Safety by Analyzing Environment With Graphics

Exploring topologically distinctive trajectories provides more options for robot motion planning. Since computing time grows greatly with environment complexity, improving exploration efficiency and picking the optimal trajectory in complex environments are critical issues. To this end, this paper proposes a Graphic- and Timed-Elastic-Band-based approach (GraphicTEB) with spatial completeness and high computing efficiency. The environment is analyzed utilizing computer graphics, where obstacles are extracted as nodes and their relationships are built as edges. Three contributions are presented. 1) By assembling directed detours formed by nodes and segmented paths formed by edges, a generalized path consisting of nodes and edges derives various normal paths efficiently. 2) By multiplying two vectors starting from the obstacle point closest to the waypoint and the boundary point farthest from the waypoint, an novel obstacle gradient is introduced to guide safer optimization. 3) By assigning edges with asymmetric Gaussian model, a trajectory evaluation strategy is designed to reflect the motion tendency and motion uncertainty of dynamic obstacles. Qualitative and quantitative simulations demonstrate that the proposed GraphicTEB achieves spatial completeness, higher scene pass rate, and fastest computing efficiency. Experiments are implemented in long corridor and broad room scenarios, where the robot goes through gaps safely, finds trajectories quickly, and passes pedestrians politely. Note to Practitioners—The motivation stems from the fact that our daily cruising robot occasionally gets trapped in a corridor with piled obstacles or in a complex dynamic crowd due to the lack of a reliable trajectory. The solution is to search for more topologically distinctive trajectories and pick the optimal one. Considering that existing open-source approaches are either incomplete or highly time-consuming, a method for clustering and searching trajectories in the obstacle-occupied regions is proposed to achieve spatial completeness and high computing efficiency. In addition, an optimization technique and a trajectory selection strategy are proposed to improve motion safety. However, at present, the search is incomplete in the temporal-spatial dimension when dynamic obstacle are moving fast. How to perform a complete and fast search in temporal-spatial space will be developed in the future.

I. Introduction

Mobile robots are increasingly integrated into human life [1], [2]. The robot receives sensor data and computes kinodynamic-constrained trajectories in real-time [3]. This motion planning process can be completed by velocity-based methods that sample candidate trajectories in velocity space, such as Dynamic Window Approach [4] and Reciprocal Velocity Obstacle [5], or state-based methods that fit trajectories by candidate goal states, such as lattice planner [6]. However, they suffer from either a short horizon or tracking error. As a result, an optimization-based Timed Elastic Band (TEB) [7], [8], [9] forms a time-optimal trajectory in a long horizon and guarantees safe separation from obstacles.