An Improved Artificial Potential Field Method Based on DWA and Path Optimization

Hu, Jinwen; Cheng, Changwei; Zhao, Chunhui; Liu, Zhenbo

doi:10.1109/icus48101.2019.8996014

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…In case of detecting neighboring obstacles, it adjusts the group repulsion parameter with the sign of the largest obstacle prevailing (Algorithm 5). The next set of states establishes the system homotopic equations to be solved (Algorithm 6); the representative functions already include the obstacles present in the environment (14). Once this system is obtained, the hyperspherical path tracking process begins; obtaining the resulting path as a collection of points and storing them in RAM (Algorithm 7).…”

Section: Fpga Implementationmentioning

confidence: 99%

“…Moreover, under certain conditions, it is possible that local minima is generated during the trajectory calculation, which the robot would interpret as having reached the desired final position because attraction and repulsion forces are in equilibrium. An important advantage of this algorithm is the possibility to work in continuous real time domains [7,14].…”

Section: Introductionmentioning

confidence: 99%

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FPGA Implementation of Homotopic Path Planning Method with Automatic Assignment of Repulsion Parameter

et al. 2020

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In recent times, autonomous robots have become more relevant, aiming not only to be an extension of mobility and human performance but also allowing them to independently solve specific problems such as finding free-collision paths within some defined environments. In order to achieve this, several techniques have been developed, like action-reaction algorithms, sampling-based algorithms, and deterministic algorithms such as the Homotopy Path Planning Method (HPPM). This work presents, for the first time, a complete deterministic collision-free path planning scheme implemented in FPGA, which is mounted on a Scribbler 2 robot from Parallax. Then, an automatic algorithm of the repulsion parameter for the HPPM method is presented, using as a reference the minimum distance between the center of each obstacle with respect to the homotopic ideal path; furthermore, an algorithm is proposed for discriminating dead-end routes and collision risk trajectories, which allows us to obtain a feasible free-collision path that takes into account the robot dimensions. Besides, comparative performance tests have been carried out against other path-finding methods from the low degrees of freedom (low DoF) and sampling-based planners. Our proposal exhibits path calculation times which are 5 to 10 times faster on FPGA implementation, compared to the other methods and 10 to 100 times faster on PC implementation also compared to the rest. Similar results are obtained with regards to memory consumption, namely 20 to 200 times lower on FPGA implementation and 10 to 100 times lower on PC implementation.

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Section: Fpga Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Homotopic Path Planning Method with Automatic Assignment of Repulsion Parameter

et al. 2020

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“…Furthermore, local motion planners often try to solve the global planning problem, which is computationally inefficient or infeasible for long paths [ 10 ]. Alternatively, local motion planners which do not solve the global path planning problem can cause the platform to get stuck in local minima and fail to reach the goal position [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

Andreasson

Larsson

Lowry

2022

Sensors

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This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.

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Path Planning Method for Mobile Robot Based on a Hybrid Algorithm

Jiang,

Wang,

Sun

et al. 2023

J Intell Robot Syst

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An Improved Artificial Potential Field Method Based on DWA and Path Optimization

Cited by 8 publications

References 8 publications

FPGA Implementation of Homotopic Path Planning Method with Automatic Assignment of Repulsion Parameter

FPGA Implementation of Homotopic Path Planning Method with Automatic Assignment of Repulsion Parameter

A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

Path Planning Method for Mobile Robot Based on a Hybrid Algorithm

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