Motion planning for mobile robots in dynamic environments using a potential field immune network

2007 ◽  
Vol 221 (7) ◽  
pp. 1033-1045 ◽  
Author(s):  
G-C Luh ◽  
W-W Liu
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Potential Field ◽  
Dynamic Environments ◽  
Immune Network

scholarly journals Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

Robotics ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 20 ◽  
Author(s):  
A poorva ◽  
Rahul Gautam ◽  
Rahul Kala
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Potential Field ◽  
A Chain

Hybrid ant colony and immune network algorithm based on improved APF for optimal motion planning

Robotica ◽  
2009 ◽  
Vol 28 (6) ◽  
pp. 833-846 ◽  
Author(s):  
Yuan Mingxin ◽  
Wang Sun'an ◽  
Wu Canyang ◽  
Li Kunpeng
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Immune Network ◽  
Network Algorithm ◽  
Dynamic Obstacle Avoidance ◽  
Optimal Dynamic ◽  
Dynamic Obstacle

SUMMARYInspired by the mechanisms of idiotypic network hypothesis and ant finding food, a hybrid ant colony and immune network algorithm (AC-INA) for motion planning is presented. Taking the environment surrounding the robot and robot action as antigen and antibody respectively, an artificial immune network is constructed through the stimulation and suppression between the antigen and antibody, and the antibody network is searched using improved ant colony algorithm (ACA) with pseudo- random-proportional rule and super excellent ant colony optimization strategy. To further accelerate the convergence speed of AC-INA and realize the optimal dynamic obstacle avoidance, an improved adaptive artificial potential field (AAPF) method is provided by constructing new repulsive potential field on the basis of the relative position and velocity between the robot and obstacle. Taking the planning results of AAPF method as the prior knowledge, the initial instruction definition of new antibody is initialized through vaccine extraction and inoculation. During the motion planning, once the robot meets with moving obstacles, the AAPF method is used for the optimal dynamic obstacle avoidance. The simulation results indicate that the proposed algorithm is characterized by good convergence property, strong planning ability, self-organizing, self-learning, and optimal obstacle avoidance in dynamic environments. The experiment in known indoor environment verifies the validity of AAPF-based AC-INA, too.

Motion Planning of Mobile Robots for Occluded Obstacles

2018 ◽  
Vol 30 (3) ◽  
pp. 485-492
Author(s):  
Satoshi Hoshino ◽  
◽  
Tomoki Yoshikawa
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Dynamic Environments ◽  
Simulation Experiments ◽  
Moving Obstacles ◽  
Moving Direction ◽  
Velocity Obstacle

Motion planning of mobile robots for occluded obstacles is a challenge in dynamic environments. The occlusion problem states that if an obstacle suddenly appears from the occluded area, the robot might collide with the obstacle. To overcome this, we propose a novel motion planner, the Velocity Obstacle for occlusion (VOO). The VOO is based on a previous motion planner, the Velocity Obstacle (VO), which is effective for moving obstacles. In the proposed motion planner, information uncertainties about occluded obstacles, such as position, velocity, and moving direction, are quantitatively addressed. Thus, the robot based on the VOO is able to move not only among observed obstacles, but also among the occluded ones. Through simulation experiments, the effectiveness of the VOO for the occlusion problem is demonstrated by comparison with the VO.

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