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 of interest. These goals are achieved by combining a gradient descent-like control action with an automatic algorithm that re-initializes the traveled path (replanning) in cluttered environments in order to mitigate the effects of local minima. The control actions of both the human and the autonomous algorithm are fused via a filter that preserves a set of local geometrical properties of the path in order to ease the tracking task of the mobile robot. The bilateral component of the interaction is implemented via a force feedback that accounts for both human and autonomous control actions along the whole path, thus providing information about the mismatch between the reference and traveled path in an integral sense. The proposed framework is validated by means of realistic simulations and actual experiments deploying a quadrotor UAV supervised by a human operator acting via a force-feedback haptic interface. Finally, a user study is presented in order to validate the effectiveness of the proposed framework and the usefulness of the provided force cues.

Past, Present, and Future of Aerial Robotic Manipulators

“…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.…”

Perception-Aware Human-Assisted Navigation of Mobile Robots on Persistent Trajectories

“…, 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).…”

“…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.…”

“…The mobile robot is equipped with onboard sensors and has enough autonomy for implementing lower-level control actions for addressing some 'local' constraints/requirements that would otherwise be hard to handle by the human operator. For instance, obstacle avoidance or attraction towards regions of interest are typical low-level behaviors, as well as dealing with limited actuation/energy, constrained dynamics or limited sensing [6]. The human operator is, instead, in charge of higherlevel behaviors such as steering the mobile robot (either the robot itself or the whole trajectory followed by the robot) towards areas of interest or the next waypoint.…”

Force Field-Based Indirect Manipulation Of UAV Flight Trajectories

“…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.…”