scholarly journals A 3-Dimensional Force Field Method for Robot Collision Avoidance in Complex Environment

2007 ◽  
Author(s):  
P. Chotiprayanakul ◽  
D. K. Liu ◽  
D. Wang ◽  
G. Dissanayake
Keyword(s):  
Force Field ◽  
Collision Avoidance ◽  
Field Method ◽  
Complex Environment ◽  
3 Dimensional

Integrated Steering and Braking Control System for Collision Avoidance by Using Virtual Repulsive Force Field Method

2018 ◽  
Author(s):  
Atsushi Yokoyama ◽  
Pongsathorn Raksincharoensak ◽  
Naoto Yoshikawa
Keyword(s):  
Force Field ◽  
Collision Avoidance ◽  
Autonomous Driving ◽  
Field Method ◽  
Repulsive Force ◽  
Driver Assistance Systems ◽  
Mass Model ◽  
Braking Control ◽  
And Control ◽  
Control Activation

Advanced Driver Assistance Systems (ADAS) and autonomous driving systems are being enhanced to deal with various types of collision avoidance use-case scenarios. To handle those complicated scenarios, a unified two-dimensional planar motion control methodology assuming virtual repulsive force from obstacles is introduced, which is physically interpretable and comprehensible. The direction and magnitude of virtual repulsive force are determined considering the orientation of obstacle surface planes and the friction limit between tires and road surface respectively. Applying the concept of virtual repulsive force field, the collision avoidance path can be derived from geometrical relationship and the control activation points can be obtained as algebraic solutions. By using a simple particle mass model, the formulation for path and control activation point is described. The simulation is conducted against not only in the case of a straight roadway but also in the case of a curve roadway. By designing feedforward and feedback controllers based on a two-wheel vehicle dynamics model, the effectiveness of the proposed method is verified and the feasibility of controller implementation for actual vehicle is also investigated.

scholarly journals UAV Group Formation Collision Avoidance Method Based on Second-Order Consensus Algorithm and Improved Artificial Potential Field

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1162 ◽  
Author(s):  
Yang Huang ◽  
Jun Tang ◽  
Songyang Lao
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Group Formation ◽  
Target Position ◽  
Field Method ◽  
Second Order ◽  
Consensus Algorithm ◽  
Artificial Potential Field ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the “leader–follower” strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method.

Research on active collision avoidance algorithm for intelligent vehicle based on improved artificial potential field model

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091123
Author(s):  
Chaochun Yuan ◽  
Shuofeng Weng ◽  
Jie Shen ◽  
Long Chen ◽  
Youguo He ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Superior Performance ◽  
Intelligent Vehicle ◽  
Artificial Potential Field ◽  
Sideslip Angle ◽  
Lane Changing ◽  
Model Based ◽  
Braking Process

In this article, an active collision avoidance based on improved artificial potential field is proposed to satisfy collision avoidance for intelligent vehicle. A longitudinal safety distance model based on analysis of braking process and a lane-changing safety spacing model based on minimum time of lane changing under the constraint of sideslip angle are presented. In addition, an improved artificial potential field method is introduced, which represents the influence of environmental information with artificial force. Simulation results demonstrate the superior performance of the proposed algorithm over collision avoidance for intelligent vehicle.

Dynamic Collision Avoidance Algorithm for Unmanned Surface Vehicles via Layered Artificial Potential Field with Collision Cone

2020 ◽  
Vol 73 (6) ◽  
pp. 1306-1325
Author(s):  
Xinli Xu ◽  
Wei Pan ◽  
Yubo Huang ◽  
Weidong Zhang
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Repulsive Force ◽  
Artificial Potential Field ◽  
Detection Function ◽  
Risk Detection ◽  
Collision Avoidance Control ◽  
Avoidance Control ◽  
Artificial Potential Field Method

A dynamic collision avoidance algorithm via layered artificial potential field with collision cone (LAPF-CC) is proposed to overcome the shortcomings of the traditional artificial potential field method in dynamic collision avoidance. In order to reduce invalid actions for collision avoidance, the potential field is divided into four layers, and a collision cone with risk detection function is introduced. Relative distance and relative velocity are used as variables to establish the risk of collision, and a torque named ‘speed torque’ is constructed. Speed torque, attractive force and repulsive force work together to change the speed and heading of the unmanned surface vehicle (USV). Driving force and torque are controlled separately, which makes it possible for the LAPF-CC algorithm to be used for real-time collision avoidance control of underactuated USVs. Simulation results show that the LAPF-CC algorithm performs well in dynamic collision avoidance.

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