Semi-Autonomous Robot Teleoperation with Obstacle Avoidance Via Model Predictive Control

Rubagotti, Matteo; Taunyazov, Tasbolat; Omarali, Bukeikhan; Shintemirov, Almas

doi:10.15607/rss.2019.xv.081

Cited by 12 publications

(13 citation statements)

References 27 publications

(35 reference statements)

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“…Besides, SC can be designed to improve the user's ability to remotely operate complex machines while simultaneously avoiding unsafe regions [43]: to this end, obstacles avoidance can be performed by autonomously overriding the user's commands leveraging reactive techniques such as artificial potentials fields [16] or model predictive control [44].…”

Section: B Remote Interactionmentioning

confidence: 99%

Autonomy in Physical Human-Robot Interaction: A Brief Survey

Selvaggio

Cognetti²,

Nikolaidis³

et al. 2021

IEEE Robot. Autom. Lett.

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Sharing the control of a robotic system with an autonomous controller allows a human to reduce his/her cognitive and physical workload during the execution of a task. In recent years, the development of inference and learning techniques has widened the spectrum of applications of shared control (SC) approaches, leading to robotic systems that are capable of seamless adaptation of their autonomy level. In this perspective, shared autonomy (SA) can be defined as the design paradigm that enables this adapting behavior of the robotic system.This letter collects the latest results achieved by the research community in the field of SC and SA with special emphasis on physical human-robot interaction (pHRI). Architectures and methods developed for SC and SA are discussed throughout the paper, highlighting the key aspects of each methodology. A discussion about open issues concludes this letter.

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Section: B Remote Interactionmentioning

confidence: 99%

Autonomy in Physical Human-Robot Interaction: A Brief Survey

Selvaggio

Cognetti²,

Nikolaidis³

et al. 2021

IEEE Robot. Autom. Lett.

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“…Based on the linear relationship on the robot link speed, Zanchettin et al [21] derived a set of inequalities to determine whether a certain point in the collaborative workspace is safe and visualized the danger filed generated by the robot. Rubagotti et al [22] investigated a predictive model for obstacle-avoidance robot teleoperation.…”

Section: Safe Hrc Manufacturing Systemmentioning

confidence: 99%

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Liu

Xiong

et al. 2021

Advanced Engineering Informatics

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Aiming at human-robot collaboration in manufacturing, the operator's safety is the primary issue during the manufacturing operations. This paper presents a deep reinforcement learning approach to realize the real-time collision-free motion planning of an industrial robot for human-robot collaboration. Firstly, the safe human-robot collaboration manufacturing problem is formulated into a Markov decision process, and the mathematical expression of the reward function design problem is given. The goal is that the robot can autonomously learn a policy to reduce the accumulated risk and assure the task completion time during human-robot collaboration. To transform our optimization object into a reward function to guide the robot to learn the expected behaviour, a reward function optimizing approach based on the deterministic policy gradient is proposed to learn a parameterized intrinsic reward function. The reward function for the agent to learn the policy is the sum of the intrinsic reward function and the extrinsic reward function. Then, a deep reinforcement learning algorithm intrinsic reward-deep deterministic policy gradient (IRDDPG), which is the combination of the DDPG algorithm and the reward function optimizing approach, is proposed to learn the expected collision avoidance policy. Finally, the proposed algorithm is tested in a simulation environment, and the results show that the industrial robot can learn the expected policy to achieve the safety assurance for industrial human-robot collaboration without missing the original target. Moreover, the reward function optimizing approach can help make up for the designed reward function and improve policy performance.

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“…The UR5 robot was equipped with a Robotiq 3-finger adaptive gripper (www.robotiq.com/products/industrial-robot-hand) and controlled using a newly formulated nonlinear model predictive control (NMPC) system that allowed the manipulator end-effector to track the operator's wrist pose in real time and, at the same time satisfying joint velocity and acceleration limits, avoiding singular configurations and workspace constraints, including obstacles. The detailed formulation of the NMPC control framework and its implementation for the case study of the UR5 robot semi-autonomous teleoperation with obstacle avoidance is reported in [31]. The gripper control was realized using a low-cost force sensitive resistor sensor attached to the operator's glove and interfaced with the wrist IMU sensor module of the system prototype for sensor signal transmission to a control PC, via the module's integrated Wixel communication board.…”

Section: System Application In Robotics Researchmentioning

confidence: 99%

“…Evolution of the arm motion-tracking system designs: (a) the 4-DOF system (Phase 1)[22]; (b,c) the 7-DOF double-arm system used in[30] (Phase 2); (d) the final 7-DOF system (Phase 3) used in[31].…”

mentioning

confidence: 99%

An Open-Source 7-DOF Wireless Human Arm Motion-Tracking System for Use in Robotics Research

Shintemirov

Taunyazov

Omarali

et al. 2020

Sensors

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To extend the choice of inertial motion-tracking systems freely available to researchers and educators, this paper presents an alternative open-source design of a wearable 7-DOF wireless human arm motion-tracking system. Unlike traditional inertial motion-capture systems, the presented system employs a hybrid combination of two inertial measurement units and one potentiometer for tracking a single arm. The sequence of three design phases described in the paper demonstrates how the general concept of a portable human arm motion-tracking system was transformed into an actual prototype, by employing a modular approach with independent wireless data transmission to a control PC for signal processing and visualization. Experimental results, together with an application case study on real-time robot-manipulator teleoperation, confirm the applicability of the developed arm motion-tracking system for facilitating robotics research. The presented arm-tracking system also has potential to be employed in mechatronic system design education and related research activities. The system CAD design models and program codes are publicly available online and can be used by robotics researchers and educators as a design platform to build their own arm-tracking solutions for research and educational purposes.

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Semi-Autonomous Robot Teleoperation with Obstacle Avoidance Via Model Predictive Control

Cited by 12 publications

References 27 publications

Autonomy in Physical Human-Robot Interaction: A Brief Survey

Autonomy in Physical Human-Robot Interaction: A Brief Survey

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

An Open-Source 7-DOF Wireless Human Arm Motion-Tracking System for Use in Robotics Research

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