scholarly journals Real-Time Obstacle Avoidance Using Potential Field for a Nonholonomic Vehicle

10.5772/9508 ◽  
2010 ◽  
Author(s):  
Hiroaki Seki ◽  
Yoshitsugu Kamiya ◽  
Masatoshi Hikizu
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field

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.

Motion planning approach for car-like robots in unstructured scenario

2021 ◽  
pp. 014233122199439
Author(s):  
Xuehao Sun ◽  
Shuchao Deng ◽  
Tingting Zhao ◽  
Baohong Tong
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Optimal Path ◽  
Conversion Function ◽  
Elastic Band ◽  
Emergency Situations ◽  
Time Motion ◽  
Planning Approach

When a car-like robot travels in an unstructured scenario, real-time motion planning encounters the problem of unstable motion state in obstacle avoidance planning. This paper presents a hybrid motion planning approach based on the timed-elastic-band (TEB) approach and artificial potential field. Different potential fields in an unstructured scenario are established, and the real-time velocity of the car-like robot is planned by using the conversion function of the virtual potential energy of the superimposed potential field and the virtual kinetic energy of the robot. The optimized TEB approach plans the local optimal path and solves the problems related to the local minimum region and non-reachable targets. The safety area of the dynamic obstacle is constructed to realize turning or emergency stop obstacle avoidance, thereby effectively ensuring the safety of the car-like robot in emergency situations. The simulation experiments show that the proposed approach has superior kinematic characteristics and satisfactory obstacle avoidance planning effects and can improve the motion comfort and safety of the car-like robot. In the practical test, the car-like robot moves stably in a dynamic scenario, and the proposed approach satisfies the actual application requirements.

scholarly journals A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Jang-Ho Cho ◽  
Dong-Sung Pae ◽  
Myo-Taeg Lim ◽  
Tae-Koo Kang
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Calculation Method ◽  
Potential Field ◽  
Autonomous Vehicles ◽  
Field Calculation ◽  
Gaussian Potential

A new obstacle avoidance method for autonomous vehicles calledobstacle-dependent Gaussian potential field(ODG-PF) was designed and implemented. It detects obstacles and calculates the likelihood of collision with them. In this paper, we present a novel attractive field and repulsive field calculation method and direction decision approach. Simulations and the experiments were carried out and compared with other potential field-based obstacle avoidance methods. The results show that ODG-PF performed the best in most cases.

scholarly journals “Real-time” obstacle avoidance in the absence of primary visual cortex

2009 ◽  
Vol 106 (37) ◽  
pp. 15996-16001 ◽  
Author(s):  
Christopher L. Striemer ◽  
Craig S. Chapman ◽  
Melvyn A. Goodale
Keyword(s):  
Visual Cortex ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Primary Visual Cortex ◽  
Posterior Parietal Cortex ◽  
Dorsal Stream ◽  
Visual Pathways ◽  
Visual Stream ◽  
Reaching Task ◽  
Visual Inputs

When we reach toward objects, we easily avoid potential obstacles located in the workspace. Previous studies suggest that obstacle avoidance relies on mechanisms in the dorsal visual stream in the posterior parietal cortex. One fundamental question that remains unanswered is where the visual inputs to these dorsal-stream mechanisms are coming from. Here, we provide compelling evidence that these mechanisms can operate in “real-time” without direct input from primary visual cortex (V1). In our first experiment, we used a reaching task to demonstrate that an individual with a dense left visual field hemianopia after damage to V1 remained strikingly sensitive to the position of unseen static obstacles placed in his blind field. Importantly, in a second experiment, we showed that his sensitivity to the same obstacles in his blind field was abolished when a short 2-s delay (without vision) was introduced before reach onset. These findings have far-reaching implications, not only for our understanding of the time constraints under which different visual pathways operate, but also in relation to how these seemingly “primitive” subcortical visual pathways can control complex everyday behavior without recourse to conscious vision.

scholarly journals Formation Control and Obstacle Avoidance Algorithm of a Multi-USV System Based on Virtual Structure and Artificial Potential Field

2021 ◽  
Vol 9 (2) ◽  
pp. 161
Author(s):  
Xun Yan ◽  
Dapeng Jiang ◽  
Runlong Miao ◽  
Yulong Li
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Shape Change ◽  
Artificial Potential Field ◽  
Generation Algorithm ◽  
Time Performance ◽  
Movement Ability ◽  
Window Approach ◽  
Avoidance Problem

This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs). The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change. To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV. By applying this method, the USV formation can avoid obstacles while maintaining its shape. The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV. Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms.

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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