Theory and applications of modular reconfigurable robotic systems

configuration design;nonemodular robots;reconfigurable robots;Task-based optimization

Chen, I-Ming ; Burdick, Joel Wakeman

University:California Institute of Technology

Department:Engineering and Applied Science

关键词: configuration design;    nonemodular robots;    reconfigurable robots;    Task-based optimization;   

Others  :  https://thesis.library.caltech.edu/4189/1/Chen_im_1994.pdf

美国|英语

来源: Caltech THESIS

【 摘 要 】

A modular reconfigurable robotic system consists of various link and joint units with standardized connecting interfaces that can be easily separated and reassembled into different configurations. Compared to a fixed configuration robot, which is usually a compromised design for a limited set of tasks, a modular robot can accomplish a large class of tasks through reconfiguration of a small inventory of modules. This thesis studies how to find an optimal module assembly configuration constructed from a given inventory of module components for a specific task. A set of generalized module models that bear features found in many real implementations is introduced. The modular robot assembly configuration is represented by a novel Assembly Incidence Matrix (AIM). Equivalence relations based on module geometry symmetries and graph isomorphisms are defined on the AIMs. An enumeration algorithm to generate non-isomorphic assembly configurations based on this equivalence relation is proposed. Examples demonstrate that this method is a significant improvement over a brute force enumeration process. Configuration independent kinematic models for modular robots are developed, and they are essential for solving the task-optimal configuration problem. A task-oriented objective function is defined on the set of non-isomorphic module assembly configurations. Task requirements and kinematic constraints on the robot assembly are treated as parameters to this objective function. The task-optimal configuration problem is formulated as a combinatorial optimization problem to which genetic algorithms are employed for solutions. Examples of finding task-optimal serial revolute-jointed robot configurations are demonstrated. In addition, the applications of modular robots to planning multifinger grasping and manipulation are developed. Planning two-finger grasps is done through finding antipodal point grasps on smooth shaped objects. Planning n-finger grasps is achieved by defining a qualitative force-closure test function on the n-finger grasps on an object. Applications of this test function to manipulation task and finger gaiting are illustrated.