Dynamic path planning of mobile robot based on artificial potential field

He, Naifeng; Su, Yifan; Guo, jilu; Fan, Xiaoliang; Liu, Zihong; Wang, Bolun

doi:10.1109/ichci51889.2020.00063

Cited by 23 publications

(11 citation statements)

References 9 publications

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“…For the more obvious problem that the robot stops due to force equilibrium, a common method is to add random gravitational points to break the force equilibrium around the robot, thus solving the problem of the robot getting stuck. However, this method is very unstable due to its random nature, and may lead the robot to deviate from its original route, resulting in a significant reduction in the accuracy of the generated shortest path, and sometimes even blocking the robot's original planned path, resulting in the algorithm failing to operate properly.Based on this problem, Xiang Yu et al combined the A-star with the conventional APF, and through the improved A-star, the next node is generated and used as a virtual target node when the robot is caught in the force equilibrium, so as to break the equilibrium by using this node as a gravitational force point, and to make the robot move forward towards the newly added virtual target node [11].He et al They avoided that the gravitational force of the target node would be too great to cause the robot to ignore the obstacles and directly crash into them by using the traditional artificial potential field method, in which the the force of gravity of the target node corresponds to its distance [12].…”

Section: Artificial Potential Field Algorithm(apf)mentioning

confidence: 99%

Algorithmic implementation and optimisation of path planning

Chen

2024

ACE

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Path planning algorithms are widely used in the fields of robotics and robotic arms, unmanned devices, automatic navigation, etc., and are an important technical basis for promoting the development of automation as well as the popularisation of artificial intelligence technology. This paper will briefly introduce various path planning algorithms implemented by mathematical models or inspired by biological features or genetics from the aspects of geometric search algorithms, intelligent search algorithms, artificial intelligence algorithms, and hybrid algorithms, including the characteristics, advantages and disadvantages, and important improvements of typical path planning algorithms as well as hybrid algorithms which are made by imitating and improving each other of several algorithms. Meanwhile, the development trend of path planning algorithms is also summarised, and the outlook of its development is made to provide reference for related fields.

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Section: Artificial Potential Field Algorithm(apf)mentioning

confidence: 99%

Algorithmic implementation and optimisation of path planning

Chen

2024

ACE

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“…However, achieving such autonomy is still challenging; local minima, local oscillation, and gravity imbalance problems [36], all serve as potential inhibitors to APF path solutions. Furthermore, the availability and completeness of APF environment data can also hinder path-planning.…”

Section: Artificial Potential Fieldmentioning

confidence: 99%

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

2023

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Unmanned aerial vehicles (UAVs) have the potential to make a significant impact in a range of scenarios where it is too risky or too costly to rely on human labour. Fleets of autonomous UAVs, which complete tasks collaboratively while managing their basic flight and related tasks independently, present further opportunities along with research and regulatory challenges. Improvements in UAV construction and components, along with developments in embedded computing hardware, communication mechanisms and sensors which may be mounted on-board a UAV are nearing the point where commercial deployment of fleets of autonomous UAVs will be technically possible. To fulfil this potential, UAVs will need to operate safely and reliably in complex and potentially dynamically changing environments with path-planning, obstacle sensing and collision avoidance paramount. This survey presents an original environment complexity classification, and critically analyses the current state of the art in relation to UAV path-planning approaches. Moreover, it highlights the existing challenges in environment complexity modelling and representation, path-planning approaches, and outlines open research questions together with future directions.

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“…BFS runs faster than Dijkstra’s algorithm, but it cannot be guaranteed to find the shortest path [ 25 ]. The A* algorithm is a heuristic search algorithm that can perform global path planning in a static environment according to a defined evaluation function [ 17 ]. It responds quickly to the environment and searches the path directly, so it is widely used in path planning research.…”

Section: Trajectory Planningmentioning

confidence: 99%

“…The artificial potential field method has become a classic algorithm in numerical optimization methods due to its simple principle, its small amount of calculation, and its high execution efficiency, and is widely used in robot trajectory planning [ 17 ]. The artificial potential field method has a small amount of calculation and fast calculation speed; thus, it meets the high real-time requirements of vehicle trajectory planning, and supports the independent design of the attraction potential field generated by the target position, and the repulsive potential field generated by obstacles, to meet the requirements of different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

Pan

Jie

Zhao

et al. 2023

Sensors

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V2P (vehicle-to-pedestrian) communication can improve road traffic efficiency, solve traffic congestion, and improve traffic safety. It is an important direction for the development of smart transportation in the future. Existing V2P communication systems are limited to the early warning of vehicles and pedestrians, and do not plan the trajectory of vehicles to achieve active collision avoidance. In order to reduce the adverse effects on vehicle comfort and economy caused by switching the “stop–go” state, this paper uses a PF (particle filter) to preprocess GPS (Global Positioning System) data to solve the problem of poor positioning accuracy. An obstacle avoidance trajectory-planning algorithm that meets the needs of vehicle path planning is proposed, which considers the constraints of the road environment and pedestrian travel. The algorithm improves the obstacle repulsion model of the artificial potential field method, and combines it with the A* algorithm and model predictive control. At the same time, it controls the input and output based on the artificial potential field method and vehicle motion constraints, so as to obtain the planned trajectory of the vehicle’s active obstacle avoidance. The test results show that the vehicle trajectory planned by the algorithm is relatively smooth, and the acceleration and steering angle change ranges are small. Based on ensuring safety, stability, and comfort in vehicle driving, this trajectory can effectively prevent collisions between vehicles and pedestrians and improve traffic efficiency.

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Dynamic path planning of mobile robot based on artificial potential field

Cited by 23 publications

References 9 publications

Algorithmic implementation and optimisation of path planning

Algorithmic implementation and optimisation of path planning

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

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