Improved Potential Field Method for Robot Path Planning with Path Pruning

Sabudin, Elia Nadira; Omar, Rosli; Joret, Ariffudin; Ponniran, Asmarashid; Sulong, Muhammad Suhaimi; Kadir, Herdawatie Abdul; Debnath, Sanjoy

doi:10.1007/978-981-15-5281-6_9

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…al., 2015). Potential field has the ability to adapt toward unknown scenarios by understanding the current state of the environment (Sabudin et al, 2016). This method, however, suffers from a local minima problem where it cause the agent stuck or not move at all.…”

Section: Introductionmentioning

confidence: 99%

Agent Navigation based on Boundary Value Problem using Iterative Methods

Musli,

Saudi

2023

JSML

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This paper presents the simulation of numerical solutions to the navigational problem of an agent traveling safely in its environment. The approach is based on the numeric solutions of the boundary value problem (BVP) that generate harmonic potential fields through a differential equation whose gradient represents navigation routes to the destination. Two methods, namely KSOR and KAOR, were tested to solve the BVP. KSOR and KAOR are variants of the standard SOR and AOR methods, respectively. In this work, the KSOR and KAOR methods were used to solve the BVP by applying Laplace's equation to obtain harmonic functions. The generated harmonic functions are then utilized by the searching algorithm to find a smooth navigational route for an agent to travel in its environment without colliding with any obstacles. The numerical results from the solutions of BVP demonstrate that the KAOR provides a faster execution time with fewer iterations compared to the KSOR method.

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Section: Introductionmentioning

confidence: 99%

Agent Navigation based on Boundary Value Problem using Iterative Methods

Musli,

Saudi

2023

JSML

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“…In ref. [38], the APF was improved with path pruning to achieve an optimal path for a mobile robot in an unknown 2D environment. The fractional repulsive potential is introduced in ref.…”

Section: Introductionmentioning

confidence: 99%

Motion planning of unmanned aerial vehicles in dynamic 3D space: a potential force approach

et al. 2022

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This research focuses on a collision-free real-time motion planning system for unmanned aerial vehicles (UAVs) in complex three-dimensional (3D) dynamic environments based on generalized potential force functions. The UAV must survive in such a complex heterogeneous environment while tracking a dynamic target and avoiding multiple stationary or dynamic obstacles, especially at low hover flying conditions. The system framework consists of two parts. The first part is the target tracking part employing a generalized extended attractive potential force into 3D space. In contrast, the second part is the obstacle avoidance part employing a generalized extended repulsive potential force into 3D space. These forces depend on the relative position and relative velocity between the UAV and respective obstacles. As a result, the UAV is attracted to a moving or stationary target and repulsed away from moving or static obstacles simultaneously in 3D space. Accordingly, it changes its altitude and projected planner position concurrently. A real-time implementation for the system is conducted in the SPACE laboratory to perform motion planning in 3D space. The system performance is validated in real-time experiments using three platforms: two parrot bebop drones and one turtlebot robot. The pose information of the vehicles is estimated using six Vicon cameras during real-time flights. The demonstrated results show the motion planning system’s effectiveness. Also, we propose a successful mathematical solution of the local minima problem associated with the potential field method in both stationary and dynamic environments.

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“…Due to its comprehensive applications, many researchers focus on the study of different path planning algorithms. The artificial potential field algorithms (ARFs) find the feasible trajectory by following the direction of the steepest descent of the potential [6,7]. However, they often end up in a local minimum.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

2021

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In this paper, an AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. The modified RRT integrating multiple heuristics can search a safer, optimal and faster converge global path within a short time, and the improved TEB can optimize both path smoothness and path length. The method is composed of a global path planning procedure and a local path planning procedure, and the Receding Horizon Planning (RHP) strategy is adopted to fuse these two modules. Firstly, the MH-RRT is utilized to generate a state tree structure as prior knowledge, as well as the global path. Then, a receding horizon window is established to select the local goal point. On this basis, an improved two-step TEB is designed to optimize the local path if the current global path is feasible. Various simulations both on static and dynamic environments are conducted to clarify the performance of the proposed MH-RRT and the improved two-step TEB. Furthermore, real applicative experiments verified the effectiveness of the proposed approach.

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Improved Potential Field Method for Robot Path Planning with Path Pruning

Cited by 6 publications

References 11 publications

Agent Navigation based on Boundary Value Problem using Iterative Methods

Agent Navigation based on Boundary Value Problem using Iterative Methods

Motion planning of unmanned aerial vehicles in dynamic 3D space: a potential force approach

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

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