Collision avoidance method of autonomous vehicle based on improved artificial potential field algorithm

2021 ◽  
pp. 095440702110143
Author(s):  
Song Feng ◽  
Yubin Qian ◽  
Yan Wang
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Autonomous Vehicle ◽  
Dynamic Environment ◽  
Artificial Potential Field ◽  
Fuzzy Pid Control ◽  
Emergency Braking ◽  
Safety Distance ◽  
On The Road ◽  
Safe Path

Both emergency braking and active steering are possible choices for collision avoidance manoeuvres, and any obstacle avoidance strategy aims to design a control algorithm preventing accidents. However, the real-time path needs to consider the motion state of surrounding participants on the road. This work presents a collision avoidance algorithm containing the path-planning and the tracking controller. Firstly, the lateral lane-changing spacing model and the longitudinal braking distance model are presented, describing the vehicle to reactively process dynamic scenarios in real environments. Then, we introduce the safety distance into the artificial potential field algorithm (APF), thereby generating a safe path in a simulated traffic scene. Redesigning the influence range of obstacles based on the collision areas and corresponding safety distance compared with the classic APF. Besides, based on the threat level, the repulsion is divided into the force of the position repulsion and the speed repulsion. The former is related to the relative position and prevents the vehicle from approaching the obstacle. The latter is opposite to the relative speed vector and decelerates the ego vehicle. Simultaneously, the attraction is improved to apply a dynamic environment. Finally, we design a model predictive control (MPC) to track the lateral motion through steering angle and a Fuzzy-PID control to track the longitudinal speed, turning the planned path into an actual trajectory with stable vehicle dynamics. To verify the performance of the proposed method, three cases are simulated to obtain the vehicle responding curves. The simulation results prove that the active collision avoidance algorithm can generate a safe path with comfort and stability.

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.

A method for avoiding obstacles of a self-driving carin the dynamic environment based on model predictive control

2021 ◽  
Author(s):  
N.P. Demenkov ◽  
Kai Zou
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Urban Environment ◽  
Predictive Control ◽  
Potential Field ◽  
Dynamic Environment ◽  
Field Method ◽  
Artificial Potential Field ◽  
Physical Constraints ◽  
Local Path

The paper discusses the problem of obstacle avoidance of a self-driving car in urban road conditions. The artificial potential field method is used to simulate traffic lanes and cars in a road environment. The characteristics of the urban environment, as well as the features and disadvantages of existing methods based on the structure of planning-tracking, are analyzed. A method of local path planning is developed, based on the idea of an artificial potential field and model predictive control in order to unify the process of path planning and tracking to effectively cope with the dynamic urban environment. The potential field functions are introduced into the path planning task as constraints. Based on model predictive control, a path planning controller is developed, combined with the physical constraints of the vehicle, to avoid obstacles and execute the expected commands from the top level as the target for the task. A joint simulation was performed using MATLAB and CarSim programs to test the feasibility of the proposed path planning method. The results show the effectiveness of the proposed method.

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.

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.

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