Two-dimensional obstacle avoidance control algorithm for snake-like robot in water based on immersed boundary-lattice Boltzmann method and improved artificial potential field method

Li, Dongfang; Pan, Zhenhua; Deng, Hongbin

doi:10.1177/0142331219897992

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…The first innovation of the improved algorithm is to add a vortex flow field based on the repulsion field of the obstacle, which has the same action range as the repulsion field 25 (Fig. 4).…”

Section: Algorithm Designmentioning

confidence: 99%

Obstacle avoidance of mobile robots using modified artificial potential field algorithm based on vortex and PID adjustment

Sheng

Wang²

2022

International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022)

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In recent years, autonomous mobile robot has become a key research direction in academia due to their good application prospects. The ability to autonomously avoid obstacles and reach a preset target point is the basic requirement of an intelligent mobile robot. As a common obstacle avoidance algorithm for mobile robots, artificial potential field algorithm (APF) has defects such as local minima problem and GNRON problem. Aiming at the shortcomings of the traditional APF algorithm, this paper proposes an improved artificial potential field algorithm based on point vortex and PID adjustment. The implementation of the improved algorithm is based on the two following main points. Firstly, an irrotational point vortex flow field is added to the original repulsion field of the obstacle to form a composite potential field. Properties of point vortex are used to provide additional virtual deflection force to the robot to avoid getting stuck. Secondly, aiming at GNRON problem, the strength of vortex and the range of action of the vortex repulsive composite potential field are adjusted in real time by means of PID adjustment. Finally, in order to verify the feasibility of the improved algorithm proposed in this paper, a comparison with the traditional APF algorithm was performed in seven environments with different obstacle layouts. The results show that the improved algorithm can be effectively applied to a variety of obstacle environments. Moreover, the local minimal value problem and GNRON problem of the traditional APF algorithm are successfully solved. The mobile robot can flexibly and efficiently avoid obstacles and reach the end point.

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Section: Algorithm Designmentioning

confidence: 99%

Obstacle avoidance of mobile robots using modified artificial potential field algorithm based on vortex and PID adjustment

Sheng

Wang²

2022

International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022)

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“…The potential field vector direction points from the obstacle to the UAV, and the potential energy is inversely proportional to the distance to the obstacle. The vortex field is tangent to the repulsion field [17,18]. As shown in Figure 2, the position coordinate of UAV i is p i = ðx i , y i Þði = 1, 2, 3, ⋯, NÞ, and N is the UAV number in the area.…”

Section: Search Algorithm For Optimal Resolution Strategy Based On Apf-gamentioning

confidence: 99%

Two-Layer Optimization Algorithm for Multi-UAV Conflict Resolution considering Individual Fairness

Gan

Zhang

et al. 2021

International Journal of Aerospace Engineering

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In order to solve the unfair individual payment costs problem in the low-altitude unmanned aerial vehicle (UAV) conflict resolution process, a multi-UAV conflict resolution algorithm based on the cooperative game concept “coalition complaint value” is proposed. Firstly, based on the low-altitude multi-UAV conflict scene characteristics, according to the “coalition complaint value” concept, the UAV conflict resolution payment matrix is established. Secondly, combined with the advantages of the artificial potential field (APF) method and the genetic algorithm (GA), a hybrid solution strategy for conflict resolution based on APF-GA is proposed. The final simulation results show that the APF-GA hybrid solution strategy has the best efficiency by combining the three evaluation indicators of calculation time, feasibility, and system efficiency. The reliability of the proposed algorithm is verified based on the Monte Carlo algorithm. The solution strategy based on the cooperative game “coalition complaint value” can improve individual fairness to a certain extent. At the same time, it can achieve the rapid planning goal with priority drones at the expense of a small amount of overall benefits.

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“…The principles of the APF method are setting the target point with attraction and the obstacles with repulsion to UAV artificially, which can realize the obstacle avoidance and navigation of formation. However, the main shortages of the artificial field method are existed the non-reachable point and local minima area (Li et al, 2020; Lin et al, 2021; Pan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

Fang

Cheng

et al. 2022

Transactions of the Institute of Measurement and Control

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To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.

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Two-dimensional obstacle avoidance control algorithm for snake-like robot in water based on immersed boundary-lattice Boltzmann method and improved artificial potential field method

Cited by 12 publications

References 27 publications

Obstacle avoidance of mobile robots using modified artificial potential field algorithm based on vortex and PID adjustment

Obstacle avoidance of mobile robots using modified artificial potential field algorithm based on vortex and PID adjustment

Two-Layer Optimization Algorithm for Multi-UAV Conflict Resolution considering Individual Fairness

Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

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