Obstacle Avoidance for Autonomous Mobile Robots Based on Position Prediction Using Fuzzy Inference

Abstract

In the future, it’s not difficult to image that we will often come across many autonomous mobile robots traversing densely populated place we live in. In such situation, because the autonomous mobile robots need to carry out their tasks in a place with unknown obstacles, the obstacle avoidance is one of the important functions of the robots. With a view to implementation of autonomous mobile robot working in doors, we employ an omnidirectional platform as shown in Fig. 1 (left). For experimental verification, an omnidirectional mobile robot shown in Fig. 1 (right) is developed. The robot has an omnidirectional camera for environmental recognition, and can move to all directions by four omni wheels. While there are many studies about obstacle avoidance method focusing attention on possibility of avoidance, this paper presents the method focusing on not only possibility but also safer trajectory of avoidance. Even if there are the same situations that the robot needs to avoid a static obstacle, timing of beginning avoidance behaviour should vary according to the robot speed. If the obstacles are moving also, the timing should vary according to the velocities of the obstacles. To cite a case, in a situation that a robot and an obstacle go by each other as shown in Fig. 2, the robot should avoid along the curved line like (iii) according to the speeds of the obstacle and own speed. In order to get to the goal with efficient and safe avoidance behaviour in the unknown environment for the robots, predicting the future obstacles’ positions by their current motions is needed. This paper introduces a real-time obstacle avoidance method introducing the velocity of obstacle relative to the robot. By means of considering predicted positions of the robot and the obstacle calculated from the time and the relative velocity, the robot can begin the avoidance behaviour at an appropriate time according to the velocity of the obstacle and the robot. Some researches focus attention on the velocity of obstacle (Ko & Lee, 1996) to avoid moving obstacles efficiently. In this research, virtual distance function is defined based on distance from the obstacle and speed of obstacle, however, only projection of the obstacle velocity on the unit vector from the obstacle to the robot is considered. In other words, the velocity of the robot is not considered.