Obstacle Avoidance by DSmT for Mobile Robot in Unknown Environment

2019 ◽  
Author(s):  
Huimin Chai ◽  
Shaonan Lv ◽  
Min Fang
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Unknown Environment

A Mobile Robot Obstacle Avoidance Method Based on Improved Potential Field Method

2013 ◽  
Vol 467 ◽  
pp. 496-501 ◽  
Author(s):  
Hao Wang ◽  
Lian Yu Zhao ◽  
Wei Chen
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Simulation Analysis ◽  
Field Method ◽  
Artificial Potential Field ◽  
Position Information ◽  
Unknown Environment ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Proposing an obstacle avoidance method for mobile robot under unknown environment, which makes use of multiple ultrasonic sensors coordinating with each other and collects position information of obstacles followed by information fuzzy processing, applies artificial potential field method with improved potential function to project the travel path of the robot. It has solved the "deadlock" problem of the traditional artificial potential field method and achieved obstacle avoidance of mobile robot under unknown environment. By simulation analysis, robot obstacle avoidance can be implemented flexibly using this method.

On a Sensor-Based Navigation for a Mobile Robot

1996 ◽  
Vol 8 (1) ◽  
pp. 2-14 ◽  
Author(s):  
Hiroshi Noborio ◽  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Unknown Environment ◽  
Navigation Algorithms ◽  
On Line ◽  
Sensor Information ◽  
Made In

The sensor-based navigation for a mobile robot is a problem of how to select a sequence of sensor-based behaviors between start and goal positions. If a mobile robot does not know its 2-d environment completely or partially, it is obliged to rely on sensor information reflected from closer obstacles in order to avoid them on-line. In the on-line framework, we should consider how a mobile robot reaches its goal position in an uncertain 2-d world. Therefore we will study some previous sensor-based navigation algorithms for mobile robots. Our motivations are to ascertain the convergence of a mobile robot to its goal position, compare the lengths of sensor-based sequences made in the previous algorithms, and decrease the length of a sequence of sensor-based motions, which is generated between start and goal positions by a sensor-feedback obstacle avoidance. Because the mobile robot itself, its sensors, and its environment usually have several uncertainties, it is notable as to how a mobile robot arrives at or near its goal in overcoming such uncertainties. It is demonstrated that the sensor-based navigation still has an enormous potential as an actual navigation of a mobile robot in a completely or partially unknown environment.

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

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