TY - GEN
T1 - Sampling-based motion and symbolic action planning with geometric and differential constraints
AU - Plaku, Erion
AU - Hager, Gregory D.
PY - 2010
Y1 - 2010
N2 - To compute collision-free and dynamically-feasibile trajectories that satisfy high-level specifications given in a planning-domain definition language, this paper proposes to combine sampling-based motion planning with symbolic action planning. The proposed approach, Sampling-based Motion and Symbolic Action Planner (SMAP), leverages from sampling-based motion planning the underlying idea of searching for a solution trajectory by selectively sampling and exploring the continuous space of collision-free and dynamically-feasible motions. Drawing from AI, SMAP uses symbolic action planning to identify actions and regions of the continuous space that sampling-based motion planning can further explore to significantly advance the search. The planning layers interact with each-other through estimates on the utility of each action, which are computed based on information gathered during the search. Simulation experiments with dynamical models of vehicles carrying out tasks given by high-level STRIPS specifications provide promising initial validation, showing that SMAP efficiently solves challenging problems.
AB - To compute collision-free and dynamically-feasibile trajectories that satisfy high-level specifications given in a planning-domain definition language, this paper proposes to combine sampling-based motion planning with symbolic action planning. The proposed approach, Sampling-based Motion and Symbolic Action Planner (SMAP), leverages from sampling-based motion planning the underlying idea of searching for a solution trajectory by selectively sampling and exploring the continuous space of collision-free and dynamically-feasible motions. Drawing from AI, SMAP uses symbolic action planning to identify actions and regions of the continuous space that sampling-based motion planning can further explore to significantly advance the search. The planning layers interact with each-other through estimates on the utility of each action, which are computed based on information gathered during the search. Simulation experiments with dynamical models of vehicles carrying out tasks given by high-level STRIPS specifications provide promising initial validation, showing that SMAP efficiently solves challenging problems.
UR - http://www.scopus.com/inward/record.url?scp=77955776567&partnerID=8YFLogxK
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U2 - 10.1109/ROBOT.2010.5509563
DO - 10.1109/ROBOT.2010.5509563
M3 - Conference contribution
AN - SCOPUS:77955776567
SN - 9781424450381
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5002
EP - 5008
BT - 2010 IEEE International Conference on Robotics and Automation, ICRA 2010
T2 - 2010 IEEE International Conference on Robotics and Automation, ICRA 2010
Y2 - 3 May 2010 through 7 May 2010
ER -