NDLI: Complementarity-based dynamic simulation for kinodynamic motion planning

Content Provider	IEEE Xplore Digital Library
Author	Chakraborty, N. Akella, S. Trinkle, J.
Copyright Year	2009
Description	Author affiliation: Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA (Chakraborty, N.) \|\| Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York 12180, USA (Trinkle, J.) \|\| Department of Computer Science, University of North Carolina, Charlotte, 28223, USA (Akella, S.)
Abstract	In this paper, we present the use of complementarity-based dynamic simulation algorithms for kinodynamic motion planning. Dynamic simulation algorithms are used as local planning methods in sampling-based motion planning algorithms to find inputs that ensure the resulting trajectory satisfies the dynamics constraints. However, the inputs are not guaranteed to give collision-free path segments. The inputs, chosen either by random sampling or from a discretization of the available inputs, are rejected if the path segment is not collision free. In cluttered environments, finding a feasible input is difficult and sensitive to the duration Δt of application of the input, and to the discretization resolution of the input set. When the collision constraints (or any inequality constraints on the state of the robot) are modeled as a set of complementarity constraints, the dynamic simulation algorithm gives a path segment that touches the obstacles and a set of contact forces whenever the robot makes contact with the obstacles. The sum of the chosen input forces and the contact forces transformed to the input space gives a control input that guarantees a collision-free path segment (provided it is within the actuator bounds). Thus in cluttered environments, using a complementarity-based dynamic simulation algorithm, we can find a feasible input that is relatively insensitive to the choice of Δt and the discretization resolution of the input set. We present simple simulation examples showing the advantages of our algorithm in cluttered environments.
Starting Page	787
Ending Page	794
File Size	307370
Page Count	8
File Format	PDF
ISBN	9781424438037
DOI	10.1109/IROS.2009.5354274
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heuristic algorithms Computational modeling Orbital robotics Intelligent robots Computer simulation Motion planning Computer science Trajectory Sampling methods Actuators
Content Type	Text
Resource Type	Article

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