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

2017 ◽  
Vol 11 (1) ◽  
pp. 85-92 ◽  
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

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

2019 ◽  
Vol 233 (16) ◽  
pp. 6032-6043 ◽  
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.

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.

scholarly journals Ship-Collision Avoidance Decision-Making Learning of Unmanned Surface Vehicles with Automatic Identification System Data Based on Encoder—Decoder Automatic-Response Neural Networks

2020 ◽  
Vol 8 (10) ◽  
pp. 754
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Time Series ◽  
Collision Avoidance ◽  
Time Series Data ◽  
Series Data ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Response ◽  
Ship Collision

Intelligent unmanned surface vehicle (USV) collision avoidance is a complex inference problem based on current navigation status. This requires simultaneous processing of the input sequences and generation of the response sequences. The automatic identification system (AIS) encounter data mainly include the time-series data of two AIS sets, which exhibit a one-to-one mapping relation. Herein, an encoder–decoder automatic-response neural network is designed and implemented based on the sequence-to-sequence (Seq2Seq) structure to simultaneously process the two AIS encounter trajectory sequences. Furthermore, this model is combined with the bidirectional long short-term memory recurrent neural networks (Bi-LSTM RNN) to obtain a network framework for processing the time-series data to obtain ship-collision avoidance decisions based on big data. The encoder–decoder neural networks were trained based on the AIS data obtained in 2018 from Zhoushan Port to achieve ship collision avoidance decision-making learning. The results indicated that the encoder–decoder neural networks can be used to effectively formulate the sequence of the collision avoidance decision of the USV. Thus, this study significantly contributes to the increased efficiency and safety of maritime transportation. The proposed method can potentially be applied to the USV technology and intelligent collision-avoidance systems.

An improved collision avoidance scheme using Artificial Potential Field with fuzzy logic

2016 ◽  
Author(s):  
John Paolo C. Tuazon ◽  
Ken Gilfed V. Prado ◽  
Neil John A. Cabial ◽  
Reeann L. Enriquez ◽  
Francesca Louise C. Rivera ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Potential Field ◽  
Artificial Potential Field

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.

Modular design of artificial potential field and nonlinear model predictive control for a vehicle collision avoidance system with move blocking strategy

2017 ◽  
Vol 232 (10) ◽  
pp. 1353-1373 ◽  
Author(s):  
Umar Zakir Abdul Hamid ◽  
Hairi Zamzuri ◽  
Tsuyoshi Yamada ◽  
Mohd Azizi Abdul Rahman ◽  
Yuichi Saito ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Potential Field ◽  
Autonomous Vehicle ◽  
Nonlinear Model Predictive Control ◽  
Tracking Performance ◽  
Artificial Potential Field ◽  
Nonlinear Controller

The collision avoidance (CA) system is a pivotal part of the autonomous vehicle. Ability to navigate the vehicle in various hazardous scenarios demands reliable actuator interventions. In a complex CA scenario, the increased nonlinearity requires a dependable control strategy. For example, during collisions with a sudden appearing obstacle (i.e. crossing pedestrian, vehicle), the abrupt increment of vehicle longitudinal and lateral forces summation during the CA maneuver demands a system with the ability to handle coupled nonlinear dynamics. Failure to address the aforementioned issues will result in collisions and near-miss incidents. Thus, to solve these issues, a nonlinear model predictive control (NMPC)-based path tracking strategy is proposed as the automated motion guidance for the host vehicle CA architecture. The system is integrated with the artificial potential field (APF) as the motion planning strategy. In a hazardous scenario, APF measures the collision risks and formulates the desired yaw rate and deceleration metrics for the path replanning. APF ensures an optimal replanned trajectory by including the vehicle dynamics into its optimization formulation. NMPC then acts as the coupled path and speed tracking controller to enable vehicle navigation. To accommodate vehicle comfort during the avoidance, NMPC is constrained. Due to its complexity as a nonlinear controller, NMPC can be time-consuming. Therefore, a move blocking strategy is assimilated within the architecture to decrease the system’s computational burden. The modular nature of the architecture allows each strategy to be tuned and developed independently without affecting each others’ performance. The system’s tracking performance is analyzed by computational simulations with several CA scenarios (crossing pedestrian, parked bus, and sudden appearing moving vehicle at an intersection). NMPC tracking performance is compared to the nominal MPC and linear controllers. The effect of move blocking strategies on NMPC performance are analyzed, and the results are compared in terms of mean squared error values. The inclusion of nonlinear tracking controllers in the architecture is shown to provide reliable CA actions in various hazardous scenarios. The work is important for the development of a reliable controller strategy for multi-scenario CA of the fully autonomous vehicle.

Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling

2016 ◽  
Vol 36 (3) ◽  
pp. 318-332 ◽  
Author(s):  
Zhenyu Wu ◽  
Guang Hu ◽  
Lin Feng ◽  
Jiping Wu ◽  
Shenglan Liu
Keyword(s):  
Mobile Robot ◽  
Collision Avoidance ◽  
Potential Field ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Artificial Potential Field ◽  
Content Type ◽  
Negative Pole ◽  
Avoidance Problem ◽  
Three Dimensional Space

Purpose This paper aims to investigate the collision avoidance problem for a mobile robot by constructing an artificial potential field (APF) based on geometrically modelling the obstacles with a new method named the obstacle envelope modelling (OEM). Design/methodology/approach The obstacles of arbitrary shapes are enveloped in OEM using the primitive, which is an ellipse in a two-dimensional plane or an ellipsoid in a three-dimensional space. As the surface details of obstacles are neglected elegantly in OEM, the workspace of a mobile robot is made simpler so as to increase the capability of APF in a clustered environment. Findings Further, a dipole is applied to the construction of APF produced by each obstacle, among which the positive pole pushes the robot away and the negative pole pulls the robot close. Originality/value As a whole, the dipole leads the robot to make a derivation around the obstacle smoothly, which greatly reduces the local minima and trajectory oscillations. Computer simulations are conducted to demonstrate the effectiveness of the proposed approach.

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