Author: commandia

Title: Robotic motion coordination based on a geometric deformation measure

Authors: Miguel Aranda, Jose Sanchez, Juan Antonio Corrales Ramon and Youcef Mezouar

Journal: IEEE Systems Journal, doi: 10.1109/JSYST.2021.3107779

Abstract: This article describes a novel approach to achieve motion coordination in a multirobot system based on the concept of deformation. Our main novel contribution is to link these two elements (namely, coordination and deformation). In particular, the core idea of our approach is that the robots’ motions minimize a global measure of the deformation of their positions relative to a prescribed shape. Based on this idea we propose a linear shape controller, that also incorporates a term modeling an affine deformation. We show that the affine term is particularly useful when the deformation to be controlled is large. We also propose controls for the other variables (centroid, rotation, size) that define the geometric configuration of the team. Importantly, these additional controls are completely decoupled from the shape control. The overall approach is simple and robust, and it creates closely coordinated robot motions. Being based on deformation, it is useful in several scenarios involving manipulation tasks: e.g., handling of a highly deformable object, control of an object’s shape, or regulation of the shape formed by the fingertips of a robotic hand. We present simulation and experimental results to validate the proposed approach.

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Title: Collision-free Transport of 2D Deformable Objects

Authors: Rafael Herguedas, Gonzalo Lopez-Nicolas, Carlos Sagues

Conference: International Conference on Control, Automation, and Systems (ICCAS 2021), Jeju, Korea, October 12-15, 2021

Abstract: We propose a novel system to transport 2D cloth-like deformable objects with mobile manipulators and without collisions along a known path. First, a new deformation model that allows for real-time shape prediction, based on the paradigm of deformable bounding box, is presented. The transport task is next defined as an optimization problem, which includes a set of linear and nonlinear constraints. These constraints allow to limit the object’s deformations and rotations and to avoid obstacles, respectively. Simulation results are reported to demonstrate the validity of our method.

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Title: Multi-scale Laplacian-based FMM for shape control

Authors: Ignacio Cuiral-Zueco and Gonzalo Lopez-Nicolas

Conference: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). September 27 – October 1, 2021. Prague, Czech Republic

Abstract: Shape control has become a prominent research field as it enables the automation of tasks in many applications. Overall, deforming an object to a desired target shape by using few grippers is a major challenge. The limited information about the object dynamics, the need to combine small and large deformations in order to achieve certain target shapes and the non-linear nature of most deformable objects are factors that significantly hamper shape control performance. In this paper, we propose a shape control method for multi-robot manipulation of large-strain deformable objects. Our approach is based on multi-scale Laplacian descriptors that feed an FMM (Fast Marching Method) for elastic shape contour matching. The FMM’s resulting path and the Laplacian operator are used to define a control strategy for the robot grippers. Simulation experiments carried out with an ARAP (As Rigid As Possible) deformation model provide satisfactory results.

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Title: Enclosing a moving target with an optimally rotated and scaled multiagent pattern

Authors: M. Aranda, Y. Mezouar, G. López-Nicolás, C. Sagüés

Journal: International Journal of Control, vol. 94, no. 3, pp. 601-611, 2021

Abstract: We propose a novel control method to enclose a moving target in a two-dimensional setting with a team of agents forming a prescribed geometric pattern. The approach optimises a measure of the overall agent motion costs, via the minimisation of a suitably defined cost function encapsulating the pattern rotation and scaling. We propose two control laws which use global information and make the agents exponentially converge to the prescribed formation with an optimal scale that remains constant, while the team’s centroid tracks the target. One control law results in a multiagent pattern that keeps a constant orientation in the workspace; for the other, the pattern rotates with constant speed. These behaviors, whose optimality and steadiness are very relevant for the task addressed, occur independently from the target’s velocity. Moreover, the methodology does not require distance measurements, common coordinate references, or communications. We also present formal guarantees of collision avoidance for the proposed approach. Illustrative simulation examples are provided.

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Title: Distributed Linear Control of Multirobot Formations Organized in Triads

Authors: M. Aranda, G. López-Nicolás and Y. Mezouar

Journal: IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 310-317, Oct. 2021

Abstract: This letter addresses the problem of controlling multiple robots to form a prescribed team shape in two-dimensional space. We consider a team organization in interlaced triads (i.e., groups of three robots). For each triad we define a measure of geometric deformation relative to its prescribed shape. Our main contribution is a novel distributed control law, defined as the gradient descent on the sum of these triangular deformation measures. We show that this geometrically motivated control law is linear, and bears analogies with existing formulations. Moreover, in comparisonwith these formulations our controller is simpler and more flexible to design, converges to the globally optimal shape by construction, and allows analysis of the team size dynamics. We illustrate the proposed approach in simulation.

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