Semi-Autonomous Robot Teleoperation With Obstacle Avoidance via Model Predictive Control

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

doi:10.1109/lra.2019.2917707

Cited by 49 publications

(15 citation statements)

References 31 publications

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“…Compared to the traditional obstacle avoidance methods [13] [28] [31], the resultant force and force feedback are more responsive, as the mobile robots can effectively account the influence of human control intention. To emphasize, when adding the EMG-based component, the mobile robot can update the attractive force and repulsive force according to the muscle activation and generate a corresponding force feedback to the human partner to achieve "active" collaboration with the human partner.…”

Section: Discussionmentioning

confidence: 99%

A Teleoperation Framework for Mobile Robots Based on Shared Control

Luo

Lin

et al. 2020

IEEE Robot. Autom. Lett.

112

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Section: Discussionmentioning

confidence: 99%

A Teleoperation Framework for Mobile Robots Based on Shared Control

Luo

Lin

et al. 2020

IEEE Robot. Autom. Lett.

112

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“…Recent literature introduces two different approaches to solve (1) for obstacle avoidance [6,27]. Both of these approaches minimize an objective function defined by cost(x, u; h)…”

Section: Problem Formulationmentioning

confidence: 99%

“…Broad et al [6] sample N ≫ 1 controls {u} N ∼ U, exclude samples such that x / ∈ X( e), and choose x, u with minimal cost. Rubagotti et al [27] use an off-the-shelf solver to compute a local minimizer. Both works adhere to the principle of minimal intervention -evident by the choice of cost function -i.e.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Other critical issues that the shared control literature addresses are, for instance, assisted guidance [26], human intention prediction [15,17], appropriate blending between human and autonomous policies [8], large time-delays [31], appropriate teleoperation spaces [22], obstacle avoidance [6,23,27], and representation and utilization of expert demonstrations [1]. However, these approaches tend to focus on cases where the intention of the operator consists of some distinct goal, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Skill-based Shared Control

Mower¹,

Moura²,

Vijayakumar³

2021

Robotics: Science and Systems XVII

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Performing a number of motion patterns -referred to as skills -(e.g., wave, spiral, sweeping motions) during teleoperation is an integral part of many industrial processes such as spraying, welding, and wiping (cleaning, polishing). Maintaining these motions whilst simultaneously avoiding obstacles and traversing complex terrain requires expert operators. In this work, we propose a novel skill-based shared control framework for incorporating the notion of skill assistance to aid novice operators to sustain these motion patterns whilst adhering to environmental constraints. Our shared control method uses streaming joystick data to estimate the model parameters that provide a description of the operator's intention. We introduce a novel parametrization for state and control that combines skill and underlying trajectory models, leveraging a special type of curve known as Clothoids. This new parameterization allows for efficient computation of skill-based short term horizon plans, enabling the use of a Model Predictive Control (MPC) loop. We perform experiments on a hardware mock-up, validating the effectiveness of our method to recognize a switch of intended skill, and showing an improved quality of output motion, even under dynamically changing obstacles. See our accompanying video here: https://youtu.be/TwhsgA6fw6M.

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“…Execution Classifier (E). Our execution classifier captures two important aspects of robot poses: reachability map [20] and kinematic singularity [21]:…”

Section: Algorithm 1 Grace-optmentioning

confidence: 99%

Towards Effective Tactile Identification of Textures using a Hybrid Touch Approach

Taunyazov

Koh

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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This paper addresses the multi-faceted problem of robot grasping, where multiple criteria may conflict and differ in importance. We introduce Grasp Ranking and Criteria Evaluation (GRaCE), a novel approach that employs hierarchical rule-based logic and a rank-preserving utility function to optimize grasps based on various criteria such as stability, kinematic constraints, and goal-oriented functionalities. Additionally, we propose GRaCE-OPT, a hybrid optimization strategy that combines gradient-based and gradient-free methods to effectively navigate the complex, non-convex utility function. Experimental results in both simulated and real-world scenarios show that GRaCE requires fewer samples to achieve comparable or superior performance relative to existing methods. The modular architecture of GRaCE allows for easy customization and adaptation to specific application needs.

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Semi-Autonomous Robot Teleoperation With Obstacle Avoidance via Model Predictive Control

Cited by 49 publications

References 31 publications

A Teleoperation Framework for Mobile Robots Based on Shared Control

A Teleoperation Framework for Mobile Robots Based on Shared Control

Skill-based Shared Control

Towards Effective Tactile Identification of Textures using a Hybrid Touch Approach

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