scholarly journals Dynamic Obstacle Avoidance of Autonomous Mobile Robot Based on Probabilistic Potential Field

2001 ◽  
Vol 121 (12) ◽  
pp. 1284-1290 ◽  
Author(s):  
Seiichiro Katsura ◽  
Kouhei Ohnishi
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Autonomous Mobile Robot ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle

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.

Application of NSTRSS in Mobile Robot Obstacle Avoidance Simulation

2013 ◽  
Vol 321-324 ◽  
pp. 1495-1498
Author(s):  
Na Chai ◽  
Wang Bao Xu
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Software Design ◽  
Basic Function ◽  
Experimental Result ◽  
Time Dynamic ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
3D Scene ◽  
Good Operation

Mobile robot becomes more and more perfect with people research continuously, obstacle avoidance is one of the most basic function in the process of operation. At present, the robot simulation system is an important tool of researching robot. In this paper, hardware design and software design were done by NSTRSS and real-time dynamic obstacle avoidance simulation of mobile robot is completed successfully. The experimental result shows that the simulation has 3D scene, lively effect and a good operation interface.

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.

Development of Autonomous Mobile Robot for Obstacle Avoidance

1993 ◽  
Vol 5 (5) ◽  
pp. 481-486 ◽  
Author(s):  
Masafumi Uchida ◽  
◽  
Syuichi Yokoyama ◽  
Hideto Ide ◽  
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Potential Method ◽  
Working Environment ◽  
Planning Problem ◽  
Autonomous Mobile Robot ◽  
The Real

The potential method is superior for solving the problem of motion planning; however, it must address the problem of the real-time generation of potential field. Obstacle avoidance is a motion planning problem. In a previous study, we investigated the real-time generation of potential field. Based on parallel processing with element group, we proposed the system by Sensory Point Moving (SPM) method. As a result of computer simulation, it was confirmed that the SPM method is effective for generating an obstacle avoidance path in 2-D and a more complex working environment like a 3-D one. In this paper, we discuss the development of autonomous mobile robot for obstacle avoidance based on the SPM method.

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