Robotic Global Path-Planning Based Modified Genetic Algorithm and A* Algorithm

Zeng, Cen; Zhang, Qiang; Wei, Xiaopeng

doi:10.1109/icmtma.2011.613

Cited by 19 publications

(16 citation statements)

References 5 publications

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“…An obstacle free path planning algorithm [35] was adopted to deal with spatial constraints. It produces a feasible path that satisfies the conditions that the waypoints should be located outside the obstacles, in the sampling space, and the local path should not intersect with the obstacles.…”

Section: Fig 7 Chromosome and Waypoint Array A) Ga Chromosome; B) mentioning

confidence: 99%

Multi-Strategy Navigation for a Mobile Data Acquisition Platform Using Genetic Algorithms

Halal

Zaremba

2017

JAMRIS

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Section: Fig 7 Chromosome and Waypoint Array A) Ga Chromosome; B) mentioning

confidence: 99%

Multi-Strategy Navigation for a Mobile Data Acquisition Platform Using Genetic Algorithms

Halal

Zaremba

2017

JAMRIS

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“…Over the past decade, many different optimization algorithms have been proposed for path planning, including genetic algorithm [16,17], neural networks [18], particle swarming algorithm [15,25], ant colony optimization [19,20], A* algorithm [21,22], Dijkstra's algorithm [23,24]. In [15], a method for planning the path of modular robots based on a genetic algorithm was proposed.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Method in the Formation of the Configuration of a Multifunctional Modular Robot Using a Swarm Control Strategy

Petrenko¹,

Тебуева²,

Pavlov³

et al. 2019

Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)

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Multifunctional modular robots consist of a set of modules that can form a kinematic structure in accordance with the current task. When operating in a non-deterministic environment, the adaptive kinematic structure of the robot allows you to change the configuration and adapt to changing conditions and the limitations of the environment. However, the formation of the required configuration can take a lot of time, which is a problem when it is needed to perform the objective function in real time. Based on this, the purpose of this work is to reduce the time required for the formation of the modular robot configuration. The article proposes a method for path planning of moving modules when forming the configuration of a multifunctional modular robot using a swarm control strategy. This approach allows real-time information exchange between robot modules, and path planning accomplishes decentralized for each module, regardless of their number. The use of analytical geometry methods allows reducing the computational complexity of the method and avoiding the energy consumption of the onboard energy resources of the robot. The results of modeling the developed method for a robot consisting of 5-50 modules are presented.When functioning in a non-deterministic environment, the adaptive kinematic structure of the MSRR allows changing the configuration and adapting to changing conditions and environmental constraints.

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“…There are several graph exploration algorithms [14] such as A* which finds the lowest cost path using the heuristic function as main factor of its efficiency [15], using a search tree to obtain de correct node sequence of the optimum path. The heuristic optimization function has been used on path planning applications [16], unmanned aerial vehicles [17] and other applications [18,19].…”

Section: Introductionmentioning

confidence: 99%

Path planning for mobile robots based on visibility graphs and A* algorithm

2015

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One of most worked issues in the last years in robotics has been the study of strategies to path planning for mobile robots in static and observable conditions. This is an open problem without pre-defined rules (non-heuristic), which needs to measure the state of the environment, finds useful information, and uses an algorithm to select the best path. This paper proposes a simple and efficient geometric path planning strategy supported in digital image processing. The image of the environment is processed in order to identify obstacles, and thus the free space for navigation. Then, using visibility graphs, the possible navigation paths guided by the vertices of obstacles are produced. Finally the A* algorithm is used to find a best possible path. The alternative proposed is evaluated by simulation on a large set of test environments, showing in all cases its ability to find a free collision plausible path.

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Robotic Global Path-Planning Based Modified Genetic Algorithm and A* Algorithm

Cited by 19 publications

References 5 publications

Multi-Strategy Navigation for a Mobile Data Acquisition Platform Using Genetic Algorithms

Multi-Strategy Navigation for a Mobile Data Acquisition Platform Using Genetic Algorithms

Path Planning Method in the Formation of the Configuration of a Multifunctional Modular Robot Using a Swarm Control Strategy

Path planning for mobile robots based on visibility graphs and A* algorithm

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