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.

scholarly journals Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

2020 ◽  
Vol 17 (3) ◽  
pp. 172988141989897 ◽  
Author(s):  
Shinan Zhu ◽  
Weiyi Zhu ◽  
Xueqin Zhang ◽  
Tao Cao
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Avoidance Strategy

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

Research on Dynamic Obstacle Avoidance Method of Manipulator Based on Binocular Vision

2021 ◽  
Vol 16 ◽  
Author(s):  
Hongxin Zhang ◽  
Jiaming Li ◽  
Rongzijun Shu ◽  
Hongyu Wang ◽  
Guangsen Li
Keyword(s):  
Obstacle Avoidance ◽  
Binocular Vision ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Robot Arm ◽  
Potential Field Method ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Artificial Potential Field Method

Background: With the development of robotics, more and more robots are used in manufacturing. However, in actual work, safety accidents happen to robots from time to time. How to ensure the safe operation of robots in a limited and complex working environment is the key to improve robot technology. Therefore, it is of great significance to study the dynamic obstacle avoidance of robots in complex environment for improving the intelligence and safety of robots, and the application of human-robot collaboration. Objective: The primary purpose of this paper is to improve the traditional artificial potential field method, including he disadvantages that the improved target is inaccessible and easily plunged into local optimal solution of the drawback of the improved method, second. Secondly, the background difference method based on binocular vision and Kalman filtering algorithm, and the environmental map containing the static and dynamic obstacles is obtained. After obtaining the position information of static and dynamic obstacles, the robot arm can make good use of the improved artificial potential field method to plan its own trajectory, thus realizing the dynamic obstacle avoidance of the robot arm in complex environment. Methodology: The background difference method and the Kalman filtering algorithm based on binocular vision were introduced to track the dynamic obstacles, and the improved artificial potential field method for path planning was applied to the dynamic obstacle avoidance path planning of the manipulator. Finally, the simulation and experimental results show that under the complex environment with dynamic obstacles exist, robot arm can realize independent dynamic obstacle avoidance. Results: By using background difference method and Kalman filtering algorithm to track the target in real time, the result showed that the target could be detected and tracked well. By improving the defect that the traditional artificial potential field method is easy to fall into local optimum, the improved algorithm can well realize the dynamic obstacle avoidance of the manipulator. Conclusions: For the development requirements of the industrial robots in the future, this paper based on binocular vision, which can make the manipulator realize more intelligent industrial production activities in complex working environment, meet the needs of future industrial development, and make this technology play an important role in production activities.

Research on the Obstacle Avoidance Strategy for the Wave Glider in the Maritime Environment Containing Dynamic Obstacles Based on the Improved Artificial Potential Field Method

2020 ◽  
Author(s):  
Daoyong Wang ◽  
Xinliang Tian ◽  
Xiantao Zhang ◽  
Xiaoxian Guo ◽  
Peng Wang
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Avoidance Task ◽  
Marine Monitoring ◽  
Dynamic Obstacle Avoidance ◽  
Wave Glider ◽  
Avoidance Strategy ◽  
Dynamic Obstacle

Abstract As a wave-propelled and persistent unmanned surface vehicle, wave glider has been widely applied in marine monitoring. Due to its long voyage, the wave glider inevitably encounters various obstacles at sea, which may cause collision accidents. However, considering the characteristics of weak maneuverability and underactuation of it, the obstacle avoidance for the wave glider is challenging. In this paper, the dynamic obstacle avoidance strategy for the wave glider based on an improved artificial potential field (IAPF) is proposed. The IAPF is committed to addressing the local minimum in the traditional artificial potential field and the obstacle avoidance difficulties caused by the weak maneuverability of the wave glider. Various simulations are conducted to demonstrate the feasibility of the proposed strategy by adopting an eight-degree-of-freedom mathematical model of the wave glider. The simulation results show that the wave glider can accomplish the obstacle avoidance task with the proposed IAPF algorithm when facing with different dynamic obstacles under various marine environments.

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