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Article

Globally Optimal Redundancy Resolution with Dynamic Programming for Robot Planning: A ROS Implementation

1
Department of Computer Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy
2
Robotic Exploration, ALTEC S.p.A., 10146 Torino, Italy
*
Author to whom correspondence should be addressed.
Robotics 2021, 10(1), 42; https://doi.org/10.3390/robotics10010042
Received: 2 February 2021 / Revised: 27 February 2021 / Accepted: 1 March 2021 / Published: 4 March 2021
(This article belongs to the Special Issue Advances in Industrial Robotics and Intelligent Systems)
Dynamic programming techniques have proven much more flexible than calculus of variations and other techniques in performing redundancy resolution through global optimization of performance indices. When the state and input spaces are discrete, and the time horizon is finite, they can easily accommodate generic constraints and objective functions and find Pareto-optimal sets. Several implementations have been proposed in previous works, but either they do not ensure the achievement of the globally optimal solution, or they have not been demonstrated on robots of practical relevance. In this communication, recent advances in dynamic programming redundancy resolution, so far only demonstrated on simple planar robots, are extended to be used with generic kinematic structures. This is done by expanding the Robot Operating System (ROS) and proposing a novel architecture meeting the requirements of maintainability, re-usability, modularity and flexibility that are usually required to robotic software libraries. The proposed ROS extension integrates seamlessly with the other software components of the ROS ecosystem, so as to encourage the reuse of the available visualization and analysis tools. The new architecture is demonstrated on a 7-DOF robot with a six-dimensional task, and topological analyses are carried out on both its state space and resulting joint-space solution. View Full-Text
Keywords: dynamic programming; redundancy resolution; redundant robot; inverse kinematics; ROS; industrial manipulator dynamic programming; redundancy resolution; redundant robot; inverse kinematics; ROS; industrial manipulator
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MDPI and ACS Style

Ferrentino, E.; Salvioli, F.; Chiacchio, P. Globally Optimal Redundancy Resolution with Dynamic Programming for Robot Planning: A ROS Implementation. Robotics 2021, 10, 42. https://doi.org/10.3390/robotics10010042

AMA Style

Ferrentino E, Salvioli F, Chiacchio P. Globally Optimal Redundancy Resolution with Dynamic Programming for Robot Planning: A ROS Implementation. Robotics. 2021; 10(1):42. https://doi.org/10.3390/robotics10010042

Chicago/Turabian Style

Ferrentino, Enrico, Federico Salvioli, and Pasquale Chiacchio. 2021. "Globally Optimal Redundancy Resolution with Dynamic Programming for Robot Planning: A ROS Implementation" Robotics 10, no. 1: 42. https://doi.org/10.3390/robotics10010042

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