A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

The "museum problem" is a typical research topic on multi-robot observation of multiple moving targets. The objective of museum problem is to optimize the distribution of robots, such that the maximal moving targets can be observed. In this paper, we present our memory based searching and artificial potential field based tracking framework for museum problem. For searching, a memory table, either local or shared, can help shorten the searching time for targets. For tracking, our artificial potential field based motion control provides real-time tracking of moving targets with collision avoidance. Qualitative simulations demonstrate the capability of our searching and tracking framework.

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Analysis of Obstacle Avoidance Strategy for Dual-Arm Robot Based on Speed Field with Improved Artificial Potential Field Algorithm

Electronics ◽

10.3390/electronics10151850 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1850

Author(s):

Hui Zhang ◽

Yongfei Zhu ◽

Xuefei Liu ◽

Xiangrong Xu

Keyword(s):

Path Planning ◽

Collision Avoidance ◽

Obstacle Avoidance ◽

Potential Field ◽

Artificial Potential Field ◽

Planning Methods ◽

Avoidance Strategy ◽

Dual Arm Robot ◽

Dual Arm ◽

Avoidance Problem

In recent years, dual-arm robots have been favored in various industries due to their excellent coordinated operability. One of the focused areas of study on dual-arm robots is obstacle avoidance, namely path planning. Among the existing path planning methods, the artificial potential field (APF) algorithm is widely applied in obstacle avoidance for its simplicity, practicability, and good real-time performance over other planning methods. However, APF is firstly proposed to solve the obstacle avoidance problem of mobile robot in plane, and thus has some limitations such as being prone to fall into local minimum, not being applicable when dynamic obstacles are encountered. Therefore, an obstacle avoidance strategy for a dual-arm robot based on speed field with improved artificial potential field algorithm is proposed. In our method, the APF algorithm is used to establish the attraction and repulsion functions of the robotic manipulator, and then the concepts of attraction and repulsion speed are introduced. The attraction and repulsion functions are converted into the attraction and repulsion speed functions, which mapped to the joint space. By using the Jacobian matrix and its inverse to establish the differential velocity function of joint motion, as well as comparing it with the set collision distance threshold between two robotic manipulators of robot, the collision avoidance can be solved. Meanwhile, after introducing a new repulsion function and adding virtual constraint points to eliminate existing limitations, APF is also improved. The correctness and effectiveness of the proposed method in the self-collision avoidance problem of a dual-arm robot are validated in MATLAB and Adams simulation environment.

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Obstacle Avoidace Robot Using LabView

IAES International Journal of Robotics and Automation (IJRA) ◽

10.11591/ijra.v4i3.pp164-167 ◽

2015 ◽

Vol 4 (3) ◽

pp. 164

Author(s):

Tasher Ali Sheikh ◽

Swacheta Dutta ◽

Smriti Baruah ◽

Pooja Sharma ◽

Sahadev Roy

Keyword(s):

Path Planning ◽

Real Time ◽

Collision Avoidance ◽

Obstacle Avoidance ◽

Autonomous Robotics ◽

Continuous Transition ◽

Heavy Burden ◽

Avoidance Strategy ◽

Small Period ◽

Specific Amount

The concept of path planning and collision avoidance are two of the most common theories applied for designing and developing in advanced autonomous robotics applications. NI LabView makes it possible to implement real-time processor for obstacle avoidance. The obstacle avoidance strategy ensures that the robot whenever senses the obstacle stops without being collided and moves freely when path is free, but sometimes there exists a probability that once the path is found free and the robot starts moving, then within a fraction of milliseconds, the robot again sense the obstacle and it stops. This continuous swing of stop and run within a very small period of time may cause heavy burden on the system leading to malfunctioning of the components of the system. This paper deals with overcoming this drawback in a way that even after the robot calculates the path is free then also it will wait for a specific amount of time before running it. So as to confirm that if again the sensor detects the obstacle within that specified period then robot don’t need to transit its state suddenly thus avoiding continuous transition of run and stop. Thus it reduces the heavy burden on the system.

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Ship Domain Model for Multi-ship Collision Avoidance Decision-making with COLREGs Based on Artificial Potential Field

TransNav the International Journal on Marine Navigation and Safety of Sea Transportation ◽

10.12716/1001.11.01.09 ◽

2017 ◽

Vol 11 (1) ◽

pp. 85-92 ◽

Cited By ~ 4

Author(s):

TengFei Wang ◽

Xin Ping Yan ◽

Yang Wang ◽

Qing Wu

Keyword(s):

Decision Making ◽

Collision Avoidance ◽

Potential Field ◽

Domain Model ◽

Artificial Potential Field ◽

Ship Collision

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Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410019844434 ◽

2019 ◽

Vol 233 (16) ◽

pp. 6032-6043 ◽

Cited By ~ 2

Author(s):

Jun Tang ◽

Jiayi Sun ◽

Cong Lu ◽

Songyang Lao

Keyword(s):

Unmanned Aerial Vehicles ◽

Collision Avoidance ◽

Unmanned Aerial Vehicle ◽

Trajectory Planning ◽

Potential Field ◽

Three Dimensional ◽

Artificial Potential Field ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Vehicle Trajectory

Multi-unmanned aerial vehicle trajectory planning is one of the most complex global optimum problems in multi-unmanned aerial vehicle coordinated control. Results of recent research works on trajectory planning reveal persisting theoretical and practical problems. To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space. For all purposes, this study considers the unmanned aerial vehicles and obstacles as spheres and cylinders with negative electricity, respectively, while the targets are considered spheres with positive electricity. However, the conventional artificial potential field algorithm is restricted to a single unmanned aerial vehicle trajectory planning in two-dimensional space and usually fails to ensure collision avoidance. To deal with this challenge, we propose a method with a distance factor and jump strategy to resolve common problems such as unreachable targets and ensure that the unmanned aerial vehicle does not collide into the obstacles. The method takes companion unmanned aerial vehicles as the dynamic obstacles to realize collaborative trajectory planning. Besides, the method solves jitter problems using the dynamic step adjustment method and climb strategy. It is validated in quantitative test simulation models and reasonable results are generated for a three-dimensional simulated urban environment.

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

Symmetry ◽

10.3390/sym11091162 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1162 ◽

Cited By ~ 2

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.

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

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420911232 ◽

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.

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