A novel framework for generalizing dynamic movement primitives under kinematic constraints

In this work, we propose a novel framework for generalizing a desired trajectory pattern, encoded using Dynamic Movement Primitives (DMP), subject to kinematic constraints. DMP have been extensively used in robotics for encoding and reproducing kinematic behaviours, thanks to their generalization, s...

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Bibliographic Details
Published inAutonomous robots Vol. 47; no. 1; pp. 37 - 50
Main Authors Sidiropoulos, Antonis, Papageorgiou, Dimitrios, Doulgeri, Zoe
Format Journal Article
LanguageEnglish
Published New York Springer US 2023
Springer Nature B.V
Subjects
Artificial Intelligence
Barriers
Computer Imaging
Constraint modelling
Control
Design optimization
Engineering
Kinematics
Mechatronics
Optimization
Pattern Recognition and Graphics
Robotics
Robotics and Automation
Robots
Trajectories
Velocity
Vision
Dynamic movement primitives
Constrained motion generation
Online trajectory adaptation
Constrained optimization
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Summary:In this work, we propose a novel framework for generalizing a desired trajectory pattern, encoded using Dynamic Movement Primitives (DMP), subject to kinematic constraints. DMP have been extensively used in robotics for encoding and reproducing kinematic behaviours, thanks to their generalization, stability and robustness properties. However, incorporating kinematic constraints has not yet been fully addressed. To this end, we design an optimization framework, based on the DMP formulation from our previous work, for generalizing trajectory patterns, encoded with DMP subject to kinematic constraints, considering also time-varying target and time duration, via-point and obstacle constraints. Simulations highlight these properties and comparisons are drawn with other approaches for enforcing constraints on DMP. The usefulness and applicability of the proposed framework is showcased in experimental scenarios, including a handover, where the target and time duration vary, and placing scenarios, where obstacles are dynamically introduced in the scene.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-022-10067-4