Toward safety navigation in cluttered dynamic environment: A robot neural-based hybrid autonomous navigation and obstacle avoidance with moving target tracking
Nacer Hacene, Boubekeur Mendil · 2015
In this paper, an autonomous navigation and obstacle avoidance strategy is proposed for an omnidirectional mobile robot. The robot plans a path, starting from an initial point going to a target point. A hybrid approach has been developed where a global approach has been applied to the motion along the desired path (DP) using 2ndorder polynomial planning, while a local reactive approach is used to avoid collisions with static and/or dynamic obstacles based on the use of neural control. The neural controller design is based on the “sensing vector” and the “gap vector” concepts. The “sensing vector” is a binary vector which provides information about obstacles detection, while the “gap vector” provides information about a possible nearest gap the robot can pass through it. The proposed approach is extended to include the problem of moving target.