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A general framework for task-constrained motion planning with moving obstacles

Published online by Cambridge University Press:  30 October 2018

Massimo Cefalo  and
Giuseppe Oriolo Open the ORCID record for Giuseppe Oriolo [Opens in a new window]
Massimo Cefalo
Affiliation:
Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Via Ariosto 25, Rome 00185, Italy. E-mail: cefalo@diag.uniroma1.it
Giuseppe Oriolo*
Affiliation:
Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Via Ariosto 25, Rome 00185, Italy. E-mail: cefalo@diag.uniroma1.it
*
*Corresponding author. E-mail: oriolo@diag.uniroma1.it
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Summary

Consider the practically relevant situation in which a robotic system is assigned a task to be executed in an environment that contains moving obstacles. Generating collision-free motions that allow the robot to execute the task while complying with its control input limitations is a challenging problem, whose solution must be sought in the robot state space extended with time. We describe a general planning framework which can be tailored to robots described by either kinematic or dynamic models. The main component is a control-based scheme for producing configuration space subtrajectories along which the task constraint is continuously satisfied. The geometric motion and time history along each subtrajectory are generated separately in order to guarantee feasibility of the latter and at the same time make the scheme intrinsically more flexible. A randomized algorithm then explores the search space by repeatedly invoking the motion generation scheme and checking the produced subtrajectories for collisions. The proposed framework is shown to provide a probabilistically complete planner both in the kinematic and the dynamic case. Modified versions of the planners based on the exploration–exploitation approach are also devised to improve search efficiency or optimize a performance criterion along the solution. We present results in various scenarios involving non-holonomic mobile robots and fixed-based manipulators to show the performance of the planner.

Keywords

Motion planningTask constraintsMoving obstaclesRandomized plannersRedundant robots
Type
Articles
Information
Robotica , Volume 37 , Issue 3 , March 2019 , pp. 575 - 598
Copyright
Copyright © Cambridge University Press 2018 

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