Planning a robust path for mobile robots in dynamic environment

Naderan-Tahan, Mahmood; Shalmani, Mohammad Taghi Manzuri

doi:10.1109/csicc.2009.5349624

Cited by 2 publications

(1 citation statement)

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“…Evolutionary methods are also commonly applied to path planning [13], [14], [15]. This work differs from ours in that it involves dynamic creation of a path to a known target whereas our evolved function encodes the logic for immediate target choice.…”

Section: Application Domain and Related Workmentioning

confidence: 99%

Evolving decision-making functions in an autonomous robotic exploration strategy using grammatical evolution

Ibrahim

Alexander

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Customising navigational control for autonomous robotic mapping platforms is still a challenging task. Control software must simultaneously maximise the area explored whilst maintaining safety and working within the constraints of the platform. Scoring functions to assess navigational options are typically written by hand and manually refined. As navigational tasks become more complex this manual approach is unlikely to yield the best results. In this paper we explore the automatic derivation of a scoring function for a ground based exploration platform. We show that it is possible to derive the entire structure of a scoring function and that allowing structure to evolve yields significant performance advantages over the evolution of embedded constants alone.

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Section: Application Domain and Related Workmentioning

confidence: 99%

Evolving decision-making functions in an autonomous robotic exploration strategy using grammatical evolution

Ibrahim

Alexander

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Evolving a path planner for a multi-robot exploration system using grammatical evolution

Ibrahim

Alexander

2011

2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing

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Area exploration and mapping with teams of robots is a challenging application. As the complexity of this application increases so does the challenge of designing effective coordinated control. One potential solution to this problem is to explore some relevant parts of the design space automatically. In this paper, we present an approach which uses Grammatical Evolution to design a control function for coordinated path planning of teams of mobile robots. Simulation results are promising with evolved control functions showing performance better than handwritten control in term of amount of explored area.

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Planning a robust path for mobile robots in dynamic environment

Cited by 2 publications

References 15 publications

Evolving decision-making functions in an autonomous robotic exploration strategy using grammatical evolution

Evolving decision-making functions in an autonomous robotic exploration strategy using grammatical evolution

Evolving a path planner for a multi-robot exploration system using grammatical evolution

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