Sensor-Based Reactive Symbolic Planning in Partially Known Environments

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Departmental Papers (ESE)
General Robotics, Automation, Sensing and Perception Laboratory
Kod*lab
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GRASP
Kodlab
reactive and sensor-based planning
task planning
collision avoidance
Electrical and Computer Engineering
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Systems Engineering
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This work was supported in part by the ARL/GDRS RCTA project, Coop. Agreement #W911NF-1020016 and in part by AFRL grant FA865015D1845 (subcontract 6697371).
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Vega-Brown, William
Arslan, Omur
Roy, Nicholas
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Abstract

This paper considers the problem of completing assemblies of passive objects in nonconvex environments, cluttered with convex obstacles of unknown position, shape and size that satisfy a specific separation assumption. A differential drive robot equipped with a gripper and a LIDAR sensor, capable of perceiving its environment only locally, is used to position the passive objects in a desired configuration. The method combines the virtues of a deliberative planner generating high-level, symbolic commands, with the formal guarantees of convergence and obstacle avoidance of a reactive planner that requires little onboard computation and is used online. The validity of the proposed method is verified both with formal proofs and numerical simulations. For more information: Kod*lab

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2018-05-01
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Departmental Papers (ESE)
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2023-05-17T19:49:04.000
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This work was supported in part by the ARL/GDRS RCTA project, Coop. Agreement #W911NF-1020016 and in part by AFRL grant FA865015D1845 (subcontract 6697371).
Recommended citation
@InProceedings{vasilopoulos_2018, Title = {{Sensor-Based Reactive Symbolic Planning in Partially Known Environments}}, Author = {V. Vasilopoulos and W. Vega-Brown and O. Arslan and N. Roy and D. E. Koditschek}, Booktitle = {IEEE International Conference on Robotics and Automation}, Year = {2018}, Pages = {5683-5690}}
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