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Abstract

We physically demonstrate a reactive sensorimotor architecture for mobile robots whose behaviors are generated by motivation dynamics. Motivation dynamics uses a continuous dynamical system to reactively compose low-level control vector fields using valuation functions which capture the potentially competing influences of external stimuli relative to the system's own internal state. We show that motivation dynamics 1) naturally accommodates external stimuli through standard signal processing tools, and 2) can effectively encode a repetitive higher-level task by composing several low-level controllers to achieve a limit cycle in which the robot repeatedly navigates towards two alternatively valuable goal locations in a commensurately alternating order. We show that these behaviors are robust to perturbations including imperfect models of robot kinematics, sensor noise, and disturbances resulting from the need to traverse difficult terrain. We argue that motivation dynamics can provide a useful alternative to controllers based on hybrid automata in situations where the control operates at a low level close to the physical hardware.

For more information: Kod*lab

Sponsor Acknowledgements

This work was funded in part by AFRL grant FA8650-15-D-1845 (subcontracts 669737-1 and 669737-6). We wish to acknowledge the support of Ghost Robotics in this work.

Document Type

Journal Article

Subject Area

GRASP, Kodlab

Date of this Version

8-2021

Publication Source

IEEE Transactions on Robotics (T-RO)

Volume

37

Issue

4

Copyright/Permission Statement

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Keywords

Reactive and Sensor-Based Planning; Motion and Path Planning; Planning, Scheduling and Coordination; Hybrid Logical/Dynamical Planning and Verification

Bib Tex

@article{Reverdy_Vasilopoulos_Koditschek_2020, author = {P. B. Reverdy and V. Vasilopoulos and D. E. Koditschek}, title = {{Motivation dynamics for autonomous composition of navigation tasks}}, journal = {{IEEE Transactions on Robotics (T-RO)}}, volume = {37}, number = {4}, year = {2021}}

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Date Posted: 14 December 2020

This document has been peer reviewed.