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arXiv · 2111.13969

Reinforcement Learning-based Switching Controller for a Milliscale Robot in a Constrained Environment

Abbas Tariverdi·Ulysse Côté-Allard·Kim Mathiassen·Ole J. Elle·Håvard Kalvøy·Ørjan G. Martinsen·Jim Tørresen
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Abstract

This work presents a reinforcement learning-based switching control mechanism to autonomously move a ferromagnetic object (representing a milliscale robot) around obstacles within a constrained environment in the presence of disturbances. This mechanism can be used to navigate objects (e.g., capsule endoscopy, swarms of drug particles) through complex environments when active control is a necessity but where direct manipulation can be hazardous. The proposed control scheme consists of a switching control architecture implemented by two sub-controllers. The first sub-controller is designed to employ the robot's inverse kinematic solutions to do an environment search for the to-be-carried ferromagnetic particle while being robust to disturbances. The second sub-controller uses a customized rainbow algorithm to control a robotic arm, i.e., the UR5 robot, to carry a ferromagnetic particle to a desired position through a constrained environment. For the customized Rainbow algorithm, Quantile Huber loss from the Implicit Quantile Networks (IQN) algorithm and ResNet are employed. The proposed controller is first trained and tested in a real-time physics simulation engine (PyBullet). Afterward, the trained controller is transferred to a UR5 robot to remotely transport a ferromagnetic particle in a real-world scenario, achieving a 98.86% success rate over 30 episodes for randomly generated trajectories, demonstrating the viability of the proposed approach for real-life applications. In addition, two classical pathfinding approaches, Attractor Dynamics and the execution extended Rapidly-Exploring Random Trees (ERRT), are also investigated and compared to the RL-based method. The proposed RL-based algorithm is shown to achieve performance comparable to that of the tested classical path planners whilst being more robust to deploy in dynamical environments.

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BibTeXRIS

Abbas Tariverdi, Ulysse Côté-Allard, Kim Mathiassen, Ole J. Elle, Håvard Kalvøy, Ørjan G. Martinsen, Jim Tørresen. 2021-11-27. Reinforcement Learning-based Switching Controller for a Milliscale Robot in a Constrained Environment. https://doi.org/10.1109/tase.2023.3259905

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