Path Planning Based on Artificial Potential Field and Fuzzy Control

Shang, Mingming; Chu, Ming; Grethler, Michael

doi:10.1109/icicas51530.2020.00069

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…The current local path planning algorithms mainly include fuzzy logic algorithm [26][27], artificial potential field method [28][29][30][31], velocity obstacle method [32][33] and so on. Guan et al proposed a ship domain model based on fuzzy logic aimed at providing early warning of ship collision risk and a reasonable reference that can be used in combination with the International Regulation for Preventing Collisions at Sea [26].…”

Section: Introductionmentioning

confidence: 99%

“…Guan et al proposed a ship domain model based on fuzzy logic aimed at providing early warning of ship collision risk and a reasonable reference that can be used in combination with the International Regulation for Preventing Collisions at Sea [26]. Shang et al designed a fuzzy controller to improve the problem of trajectory oscillation, which outputs the environment danger factor to adjust the step size of robot and enhance the trajectory smoothness of robot in complex environment [27]. Yang studied the development process of the obstacle avoidance system for autonomous vehicles and propose an optimization scheme for the obstacle avoidance algorithm [28].…”

Section: Introductionmentioning

confidence: 99%

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Ship Formation Algorithm Based on the Leader–Follower Method

Meng

Chen³

et al. 2023

IEEE Access

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In recent years, marine engineers have increasingly relied on multiship collaborative work to complete complex tasks at sea and improve maritime transport efficiency. Ship formation significantly contributes to the maintenance of stability during multiship-coordinated operations, but establishing the formation structure while ensuring the safety of each ship remains challenging. Therefore, this study proposes an improved leader-follower-based formation algorithm as a solution to this issue. First, the fast marching square method was used for global static path planning. The leader-follower formation control method then enabled the path tracking of the follower ship to the leader ship. Finally, an improved artificial potential field method was used for local collision avoidance, and virtual obstacles were added to improve the situation in which the ship probably entered a local minimum point. The experimental results revealed that the proposed algorithm could plan a route with time, smoothness, and safety advantages for the formation leader and quickly form a stable formation. Notably, the ship can quickly initiate collision avoidance operations when it encounters an obstacle posing a collision risk.INDEX TERMS Artificial potential field method, fast marching square method, formation of ships, leaderfollower method, local collision, path planning.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ship Formation Algorithm Based on the Leader–Follower Method

Meng

Chen³

et al. 2023

IEEE Access

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“…Li et al proposed an improved APF that optimized the angle of repulsive force and the function of attraction field, which enabled the robot to avoid local minimum [14]. Shang et al combined improved APF with fuzzy logic that when the AMR falls into the local minimum, set one or more virtual targets to guide the robot out of the dead zone, which mitigated the occurrence of local minimum and oscillation in the trajectory [15]. Ji et al converted Cartesian coordinates to ellipsoidal coordinates and converted traditional APF into ellipsoidal two-dimensional APF by solving the Laplace equation.…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Trajectory Optimization Based on Improved APF and Multi-Target

Yang,

Zhang,

Ning

2023

IEEE Access

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Aiming at the shortcomings of the path generated by the artificial potential field (APF) method, such as local minimum, target unreachability, and low path smoothness, an improved artificial potential field method is proposed. First, to reduce the collision risk and planning difficulty, based on known environmental information such as the location of obstacles and targets, the area with fewer obstacles is selected as the priority area for path planning. Second, to improve the path smoothness and reduce the computation amount, an adaptive step-size adjustment method based on the distance and angle relationship with obstacles within the prediction range is proposed. Third, in view of the effect on each other between obstacle, local minimum, and unsmooth path, a multi-target model considering the size and influence range of obstacles and an improved potential field function are proposed on the basis of the identified planning priority area. Finally, in order that the path is smooth enough to be tracked by autonomous mobile robots, a safe driving corridor without collision with obstacles is constructed on the planned path, and a trajectory fully constrained to the safe driving corridor is generated using the quadratic programming method. Compared with the traditional APF algorithm by using matlab simulation software, the improved APF algorithm can effectively solve the problems of local minimum and target unreachability, generate collision-free trajectories with better smoothness, and have better real-time performance.

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“…In order to mitigate local minima and oscillations in trajectories, an improved APF solution is combined with fuzzy logic in [13]. When the UAV falls into the local minimum position, one or more virtual targets are set to guide the robot out of the dead zone.…”

Section: Introductionmentioning

confidence: 99%

Modified Artificial Potential Field for the Path Planning of Aircraft Swarms in Three-Dimensional Environments

Souza

Lima

Morais

et al. 2022

Sensors

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Path planning techniques are of major importance for the motion of autonomous systems. In addition, the chosen path, safety, and computational burden are essential for ensuring the successful application of such strategies in the presence of obstacles. In this context, this work introduces a modified potential field method that is capable of providing obstacle avoidance, as well as eliminating local minima problems and oscillations in the influence threshold of repulsive fields. A three-dimensional (3D) vortex field is introduced for this purpose so that each robot can choose the best direction of the vortex field rotation automatically and independently according to its position with respect to each object in the workspace. A scenario that addresses swarm flight with sequential cooperation and the pursuit of moving targets in dynamic environments is proposed. Experimental results are presented and thoroughly discussed using a Crazyflie 2.0 aircraft associated with the loco positioning system for state estimation. It is effectively demonstrated that the proposed algorithm can generate feasible paths while taking into account the aforementioned problems in real-time applications.

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Path Planning Based on Artificial Potential Field and Fuzzy Control

Cited by 4 publications

References 6 publications

Ship Formation Algorithm Based on the Leader–Follower Method

Ship Formation Algorithm Based on the Leader–Follower Method

Path Planning and Trajectory Optimization Based on Improved APF and Multi-Target

Modified Artificial Potential Field for the Path Planning of Aircraft Swarms in Three-Dimensional Environments

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