3D X-Y-T Space Path Planning for Autonomous Mobile Robots Considering Dynamic Constraints

Abstract

An autonomous mobile robot in a human living space should be able to not only realize collision-free motion but also give way to humans depending on the situation. Although various reactive obstacle avoidance methods have been proposed, it is difficult to achieve such motion. On the other hand, 3D X-Y-T space path planning, which takes into account the motion of both the robot and the human in a look-ahead time horizon, is effective. This paper proposes a real-time obstacle avoidance method for an autonomous mobile robot that considers the robots dynamic constraints, the personal space, and human directional area based on grid-based 3D X-Y-T space path planning. The proposed method generates collision-free motion in which the robot can yield to humans. To verify the effectiveness of the proposed method, various experiments in which the humans position and velocity were estimated using laser range finders were carried out.

Human-centered X–Y–T space path planning for mobile robot in dynamic environments

“…We have confirmed that the proposed method can realize collision-free motion giving way to a human in the case where the human approaches slowly from immediately in front of the robot [24]. However, the relative position, velocity, and avoidance motion with respect to the robot varies from person to person.…”

A survey of formation control and motion planning of multiple unmanned vehicles

“…Among them, the potential field and the evolutionary based methods are the most widely adopted approaches. These are significantly different from single vehicle path planning, where the grid based [87][88][89] or the road map based methods [90][91][92][93] are preferred.…”

Development of fuzzy logic control system of the mobile robot

“…Анализ публикаций в таких научных журналах как Искусственній интеллект, Robotics and Autonomous Systems, Neural Networks, Expert Systems with Applications, Control Engineering Practice [4][5][6][7][8][9][10][11][12][13][14] показывает, что для решения задач управления сложными динамическими объектами в масштабе реального времени в условиях неопределенности и дефицита ресурсов используются методы принятия решений на основе систем искусственного интеллекта. В работе [4] выполнен обзор интеллектуальных алгоритмов планирования на основе технологии мягких вычислений: искусственных нейронных сетей (ИНС), нечеткой логики (НЛ), и генетических алгоритмов (ГА).…”