Optimal sensor planning for workspace detectionin robotic environments is hindered due to sensor occlusions.These occlusions are often dynamic. Probabilistic optimizationframeworks, which generally deal with the uncertain nature ofthese occlusions suffer from unreliability and/or unavailability ofprobability distribution functions. This paper proposes and analyses a robust optimization approach (minimax) which generatessensor configurations based on occlusion scenarios that causemaximum obstruction of the robotic workspace. The optimalsolution is independent of probability distribution functions andprovides a guaranteed level of workspace visibility regardlessof occluder positions, thus accounting for random occlusions.The method also allows the user to determine the impact of theworst-case occlusion scenarios leading to a broader perspective onsensor planning. Evaluation of the approach for a mobile medicalX-ray robotic system in a simulation healthcare environmentshows the effectiveness of the proposed method


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    Title :

    Robust optimal sensor planning for occlusion handling in dynamic robotic environments


    Contributors:

    Publication date :

    2019-06-01


    Remarks:

    Mohan , R & de Jager , B 2019 , ' Robust optimal sensor planning for occlusion handling in dynamic robotic environments ' , IEEE Sensors Journal , vol. 19 , no. 11 , 8643386 , pp. 4259-4270 . https://doi.org/10.1109/JSEN.2019.2899929



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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