Potential field based formation control in trajectory tracking and obstacle avoidance tasks

2012 ◽  
Author(s):  
Diana D. Tsankova ◽  
Nayden Isapov
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Tracking ◽  
Potential Field ◽  
Formation Control

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.

Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles

2020 ◽  
Vol 124 (1282) ◽  
pp. 1979-2000
Author(s):  
A. Mirzaee Kahagh ◽  
F. Pazooki ◽  
S. Etemadi Haghighi
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Functional Limitations ◽  
Formation Flight ◽  
Artificial Potential Field ◽  
Aerial Vehicles ◽  
Shape Formation ◽  
Vector Approach

ABSTRACTA formation control and obstacle avoidance algorithm has been introduced in this paper for the V-shape formation flight of fixed-wing UAVs (Unmanned Aerial Vehicles) using the potential functions method. An innovative vector approach has been suggested to fix the conventional challenge in employing the artificial potential field (APF) approach (the creation of local minimums). A method called variable repulsive circles (VRC) has been then presented aimed at designing proper flight paths tailored with functional limitations of fixed-wing UAVs in facing obstacles. Finally, the efficiency of the designed algorithm has been examined and evaluated for different flight scenarios.

scholarly journals Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the Driver’s Operation Characteristics

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4821
Author(s):  
Qinyu Sun ◽  
Yingshi Guo ◽  
Rui Fu ◽  
Chang Wang ◽  
Wei Yuan
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
Trajectory Tracking ◽  
Potential Field ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Planning Model ◽  
Repulsive Potential ◽  
Tracking Model ◽  
Operation Characteristics

Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.

Multi-Robot Obstacle Avoidance Based on the Improved Artificial Potential Field and PID Adaptive Tracking Control Algorithm

Robotica ◽  
2019 ◽  
Vol 37 (11) ◽  
pp. 1883-1903 ◽  
Author(s):  
Zhenhua Pan ◽  
Dongfang Li ◽  
Kun Yang ◽  
Hongbin Deng
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Tracking Control ◽  
Formation Control ◽  
Control Algorithm ◽  
Situational Awareness ◽  
Artificial Potential Field ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Multi Robot

SummaryAs for the obstacle avoidance and formation control for the multi-robot systems, this paper presents an obstacle-avoidance method based on the improved artificial potential field (IAPF) and PID adaptive tracking control algorithm. In order to analyze the dynamics and kinematics of the robot, the mathematical model of the robot is built. Then we construct the motion situational awareness map (MSAM), which can map the environment information around the robot on the MSAM. Based on the MSAM, the IAPF functions are established. We employ the rotating potential field to solve the local minima and oscillations. As for collisions between robots, we build the repulsive potential function and priority model among the robots. Afterwards, the PID adaptive tracking algorithm is utilized to multi-robot formation control. To demonstrate the validity of the proposed method, a series of simulation results confirm that the approaches proposed in this paper can successfully address the obstacle- and collision-avoidance problem while reaching formation.

scholarly journals UAV formation obstacle avoidance control method based on artificial potential field and consistency

2021 ◽  
Vol 2083 (4) ◽  
pp. 042029
Author(s):  
Boyu Wei
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Control Method ◽  
Artificial Potential Field ◽  
Time Varying ◽  
Complex Environments ◽  
Control Scheme ◽  
Avoidance Control ◽  
Multi Agent

Abstract As a typical multi-agent formation, UAV formation is playing an increasingly powerful role in the civilian and military fields. Obstacle avoidance, as an important technology in controlling formation, determines the application prospects of UAVs. This paper studies the time-varying formation of UAVs with interactive topology to avoid obstacles, aiming to improve the ability of UAV formations to deal with complex environments while traveling. Firstly, a repulsive force field is reasonably introduced based on the existing control scheme, and an improved distributed time-varying formation control scheme based on artificial potential field is proposed. Then combined with the basic idea of model predictive control, an obstacle avoidance strategy in which UAV obstacle avoidance and formation shaping are carried out simultaneously is proposed. Finally, a time-varying formation simulation experiment containing four UAVs was carried out to verify the validity of the results.

Multi-Robot Formation Control Based on the Artificial Potential Field Method

2014 ◽  
Vol 519-520 ◽  
pp. 1360-1363 ◽  
Author(s):  
Xi Na Gao ◽  
Li Juan Wu
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Field Method ◽  
Artificial Potential Field ◽  
Control Methods ◽  
Potential Field Method ◽  
Artificial Potential Field Method ◽  
Multi Robot

The artificial potential field method is one of multi-robot formation control methods. In this paper we make a study on multi-robot formation control based on the artificial potential field method and the leader-follower method. The robots are set leader robot and follower robots respectively. According to the known ideal distance between the leader and follower, we adjust the repulsiveness or attractiveness to maintain multi-robot formation. Multi-robots obstacle avoidance is adopted the artificial potential field method. In this paper the triangle formation is taken as an example. At last the simulation result proves the validity of this algorithm.

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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