Stability analysis and motion planning strategies with fuzzy coordinator and learning for mobile base robotic part macro/micro-assembly tasks

Abstract This paper addresses problems of a mobile base robot’s part assembly. This process can be broken down into two phases. First, a macro-assembly, bringing a part to an assembly hole or a receptacle (target) for a purpose of a part mating. For the macro-manipulation task, a stability analysis of a mobile base robot subject to disturbances, such as an external impact force and torque as well as a tipping movement, is discussed. The mobile robotic system is stabilized by balancing the system moment through a fuzzy coordinator. Simulations are performed by applying external forces and torque to the system and adding disturbances to the mobile base’s tipping movement. Second, a micro-assembly, mating a part with a target. For the micro-manipulation task, two learning methodologies are presented. First, a learning strategy to minimize the entropy, uncertainty, and eliminate unneeded events in the plan related to avoiding jamming is described. An entropy function, which is a useful measure of the variability and the information in terms of uncertainty, is introduced to measure its overall performance of a task execution related to the part mating. Next, a fuzzy stochastic learning method, based on the probability of a fuzzy set and a modified distance metric, to update the probability of a plan composed of fuzzy events used for the part mating task is introduced. The degree of uncertainty associated with the fuzzy event of plan is used as an optimality criterion, or cost function, e.g. minimum Hamming distance, for a specific task execution. The above techniques are applicable to a wide range of mobile robotic tasks including pick and place operations, maneuvering around workspace, manufacturing, part mating, or complex assembly tasks.