2018
DOI: 10.1177/0278364918802006
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Shared planning and control for mobile robots with integral haptic feedback

Abstract: This paper presents a novel bilateral shared framework for online trajectory generation for mobile robots. The robot navigates along a dynamic path, represented as a B-spline, whose parameters are jointly controlled by a human supervisor and by an autonomous algorithm. The human steers the reference (ideal) path by acting on the path parameters which are also affected, at the same time, by the autonomous algorithm in order to ensure: i) collision avoidance, ii) path regularity and iii) proximity to some points… Show more

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Cited by 25 publications
(17 citation statements)
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“…It is indeed of fundamental importance, for safety and regulatory reasons, to let a human operator remain in the loop while the robotic system acts on the environment in an autonomous or semiautonomous way [28]. The majority of the presented works on aerial teleoperation at date focused on the contact-free motion control of the vehicles (see, e.g., [126], [153] and references therein). Bilateral (e.g., with haptic feedback) teleoperation methods have been presented to help humans controlling single [153] and multiple aerial vehicles navigating in cluttered environments.…”
Section: Teleoperationmentioning
confidence: 99%
“…It is indeed of fundamental importance, for safety and regulatory reasons, to let a human operator remain in the loop while the robotic system acts on the environment in an autonomous or semiautonomous way [28]. The majority of the presented works on aerial teleoperation at date focused on the contact-free motion control of the vehicles (see, e.g., [126], [153] and references therein). Bilateral (e.g., with haptic feedback) teleoperation methods have been presented to help humans controlling single [153] and multiple aerial vehicles navigating in cluttered environments.…”
Section: Teleoperationmentioning
confidence: 99%
“…, s ) are constant parameters, with = N ≥ α. B s (s) is the set of basis functions and B α j is the j-th basis function evaluated at s, obtained by the classical Cox-De Boor recursion formula in case of open B-Spline, or by a slightly modified version in case of closed B-Spline as shown in [6]. In the following we will then let q γ (x c , s) and u γ (x c , s) represent the state q and inputs u obtained (via the flatness) as a function of the B-Spline γ(x c , s).…”
Section: Preliminariesmentioning
confidence: 99%
“…We chose B-Spline curves (widely used in the literature, e.g., [6], [12]) to avoid an infinite-dimensional optimization problem, which would be intractable at runtime. B-Spline allowed us to formulate a finite-dimensional (thus numerically tractable at runtime) optimization problem, where the control points of the B-Spline become the optimization variables.…”
Section: B Optimization Problemmentioning
confidence: 99%
See 1 more Smart Citation
“…Remarkably, Masone et al [15] present a shared control framework for online trajectory generation of mobile robots. While they evaluate different interactive methods for direct path manipulation with haptic feedback, they suggest to online retain feasibility during UAV navigation [16]. However, these works discuss manipulation of a given curve based on extensive geometry-constraint models.…”
Section: Related Workmentioning
confidence: 99%