Towards Peak Torque Minimization for Modular Self-Folding Robots

Modular self-folding robots are versatile systems that can change their own shape from two-dimensional patterns at instant commands. This reconfigurability is commonly restrained by power limitation in autonomous environments, The robotic systems with insufficient torque may lead to inaccurate movements and even transformation failures. This paper presents methodology for optimized reconfiguration planning with torque limitation in modular self-folding robots. We determine reconfiguration schemes with optimal initial pattern and robotic base that result in minimal peak torque by minimizing robotic inertia of the modular architecture. We present minimal bounding box and capacitated spanning tree heuristic algorithms to generate optimal initial patterns and propose 3 heuristic rules for robotic base selection. Our approach is demonstrated in simulation by applying the algorithms to the robotic concept of Mori, a modular origami robot. The simulation results show that the proposed algorithms yield reconfiguration schemes with low peak torque, thereby appropriate for real-time applications in modular robotic systems.

Towards Peak Torque Minimization for Modular Self-folding Robots

Christoph Heinrich Belke, Jamie Paik, Meibao Yao

Modular self-folding robots are versatile systems that can change their own shape from two-dimensional patterns at instant commands. This reconfigurability is commonly restrained by power limitation in autonomous environments. The robotic systems with insufficient torque may lead inaccurate movements and even transformation failures. This paper presents methodology for optimized reconfiguration plan ning with torque limitation in modular self-folding robots. We determine reconfiguration schemes with optimal initial pattern and robotic base that result in minimal peak torque by mini-mizing robotic inertia of the modular architecture. We present minimal bounding box and capacitated spanning tree heuristic algorithms to generate optimal initial patterns and proposes 3 heuristic rules for robotic base selection. Our approach is demonstrated in simulation by applying the algorithms the robotic concept of Mori, a modular origami robot. The simulation results show that the proposed algorithms yield reconfiguration schemes with low peak torque, thereby appropriate for real-time applications in modular robotic systems.

IEEE2018

Optimal Distribution of Active Modules in Modular Robots

Christoph Heinrich Belke, Jamie Paik, Meibao Yao

Reconfigurability in versatile systems of modular robots is achieved by appropriate actuation of their modular units. Optimized distribution of active modules in the modular architecture can significantly reduce cost and energy in the reconfiguration tasks. This paper presents methodology for distribution planning in modular robots that results in minimum number of active modules, while guaranteeing their capability to reconfigure. We discuss the optimal distribution problem in layout-based and target-based planning, so that the modular robots can respond to instant commands for reconfiguration with an initial planar pattern or a target three dimensional configuration as input. We propose heuristic algorithms for the corresponding solution in different scenarios. Our approach is demonstrated by applying the algorithms to Mori, a modular origami robot. The simulation results show that the algorithms yields distribution schemes with high quality, thereby appropriate for real-time applications in modular robotic systems.

2018

Model-based control of fast parallel robots

A global approach to the problem of model-based control of fast parallel robots is proposed in this work. Fundamental differences between the well-known serial arms and parallel manipulators are first explained. A formalism inspired from Denavit-Hartenberg's makes it possible to parametrize any parallel manipulator by handling it as two tree robots connected through six standard links. Then, it is shown that kinematics and dynamics modeling is greatly simplified when the robot's state is represented both by the variables associated to the actuated joints and the variables specifying the end-effector's position in operational space. The inverse dynamics model of any parallel manipulator can then be put under a standard form called "in the two spaces". A Newton-Euler based algorithm is proposed for the real-time computation of the model in the two spaces its complexity is shown not to be much larger than for serial arms. Through Lagrangian mechanics, the model in the two spaces allows analysis of the robot's dynamics properties, such as passivity. These properties are shown to be equivalent to those of serial arms, except that parallel robots offer good performances only in a restricted workspace in which their Jacobian matrix remains bounded. Various control strategies for the trajectory tracking problem for fast robots are then examined. The advantages of model-based approaches combined with robust feedback laws in operational space are described. It is shown that a control loop in operational space requires less computations than in joint space for a parallel robot. Moreover, such a scheme can be very efficiently be implemented on a multiprocessor control unit that exploits the intrinsically parallel and pipeline structure of the required algorithms. Finally, the proposed approach is applied to the Delta parallel manipulator. A systematic approach leads to the kinematics and dynamics models of this robot, which are expressed under a very compact form. The analysis of the Delta's Jacobian matrix as well as some simulation results reveal the advantages and weak points of this manipulator. The implementation of a model-based control law for the Delta on a control unit with four Transputers is described. Some results obtained on a Delta with a crank belt reduction are presented and discussed.

EPFL1994

Model-based control of fast parallel robots

EPFL1994