Benchmarking the utility of maps of dynamics for human-aware motion planning

Swaminathan, Chittaranjan Srinivas, Kucner, Tomasz, Magnusson, Martin , Palmieri, Luigi, Molina Mellado, Sergio, Mannucci, Anna, Pecora, Federico, Lilienthal, Achim, , and , (2022) Benchmarking the utility of maps of dynamics for human-aware motion planning. Frontiers in Robotics and AI, 9 . ISSN 2296-9144

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Benchmarking the utility of maps of dynamics for human-aware motion planning
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Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

Keywords:human-aware motion planning, maps of dynamics, Dynamic environments, Benchmarking, human-populated environments
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:52305
Deposited On:20 Dec 2022 16:42

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