Solving the potential field local minimum problem using internal agent states

Mabrouk, Mohamed; McInnes, Colin R.

doi:10.1016/j.robot.2008.09.006

Cited by 80 publications

(36 citation statements)

References 22 publications

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“…Pay attention to the initial status that has a local minimum because the vehicle, Ob1 and the target T are placed on a straight line. Generally this kind of scenarios is intractable for other methods, such as artificial potential field [13]. The time is 28s after Ob1 has been evaded, as shown in Fig.…”

Section: A Simulation Resultsmentioning

confidence: 99%

Relative Coordination 3D Trajectory Generation Based on the Trimmed ACO

Chen

Zhao

Zhang

et al. 2010

2010 International Conference on Electrical and Control Engineering

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Relative coordination has predominant advantage in uncertain environment path planning problem because it denies much of redundant information of the dynamic environment while avoids the rotation transformation which is a big burden in the traditional methods. Based on relative coordination, this paper proposes a trimmed Ant Colony Optimization (ACO) algorithm for three dimensional trajectory generation, i.e., path planning for target-pursuing and obstacle-avoiding (TPOA). We construct the city-map directly on the acceleration of the pursuing vehicle, an aerial robot. Then the robot's kinematics and the dynamics constraints will be embedded conveniently into the ACO formulation. As a result, the searching space in the ACO formulation shrinks greatly and the robot's movement is real and more feasible for application. This novel approach is verified by various simulations and the results show the good performance of efficiency.Index Terms -three dimensional path planning, aerial vehicle, constraints, dynamic environment, ACO.

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Section: A Simulation Resultsmentioning

confidence: 99%

Relative Coordination 3D Trajectory Generation Based on the Trimmed ACO

Chen

Zhao

Zhang

et al. 2010

2010 International Conference on Electrical and Control Engineering

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“…The VFH (vector field histogram) method was developed to improve the practical application of the potential field method and to solve the local minimum problem [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Autonomous Mobile Robot Based on Safe Space

Park¹,

Huh²

2016

Journal of Electrical Engineering and Technology

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-Path planning is essential for the autonomous navigation of mobile robots. Researchers have been working on ensuring the safe navigation of mobile robots; however, it is impossible to secure the absolute safety of a mobile robot without environmental information. Nevertheless, passive safety of a mobile robot can be secured. With the aim of ensuring safe path planning of a mobile robot, a safety space is proposed in this work by using the parameters of stopping distance and hazard point. Mobile robots should formulate path plans to bypass crossroads or corner areas where their field of view is limited, and they should also be capable of reducing their movement speed to secure safe driving. We demonstrate through extensive simulations that the developed SGPP (safe global path planning) method outperforms the classical A* and PRM (probabilistic roadmap) algorithms. It improves the navigation time and length of the robot path in comparison to the PRM algorithm and reduces the navigation time by up to 26% compared to the classical A* algorithm for safe navigation. In addition, results of an experiment conducted on a real robot show that the SGPP method finds a safe path with limited velocity for safe navigation.

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“…The main concern is it may fail to find even one solution as it is very sensitive to local minimum traps caused by concave-shaped obstacles or stagnation points [2]. Many attempts have been made to propose a local-minimum free APF [3], [4], [5], [6], however, the local minimum issue still remains.…”

Section: Introductionmentioning

confidence: 99%

Grid-based GA path planning with improved cost function for an over-actuated hover-capable AUV

Tanakitkorn

Wilson

Turnock

et al. 2014

2014 IEEE/OES Autonomous Underwater Vehicles (AUV)

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Abstract-For an AUV to perform a long-range mission with its maximum endurance, its energy consumption during transit must be kept to a minimum. This paper presents an improved cost function for a grid-based genetic algorithm (GA) path planning in 2D static environments. The proposed function consists of energy consumption terms that are estimated according to dynamics of a hover-capable AUV -notably Delphin2 AUV. It seeks for a path that requires least effort for the vehicle to move along. A simulation was written in Matlab and the outcomes of the GA with the improved cost function are compared with the ones of a GA with an optimal distance approach as well as an A* approach. It is found that outcomes of an improved cost function require less energy compared with the other techniques.

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Solving the potential field local minimum problem using internal agent states

Cited by 80 publications

References 22 publications

Relative Coordination 3D Trajectory Generation Based on the Trimmed ACO

Relative Coordination 3D Trajectory Generation Based on the Trimmed ACO

Path Planning for Autonomous Mobile Robot Based on Safe Space

Grid-based GA path planning with improved cost function for an over-actuated hover-capable AUV

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