On Motion Planning And Locomotive Evolution For Modular Self-reconfigurable Robot

Motion planning and locomotive evolution for chain-styled MSRR (Modular Self-Reconfigurable Robot) are attended in this thesis.Based on the simulation environment constructed using MRDS(?)(MicrosoftRobotics Developer Studio) and SPL(?)(Simulation Programming Language) incollaboration with MATLAB? motion planning and gaits optimization are explored andthe simulated results are delivered to the physical HIT Cubic-crystallike Self-Reconfigurable Robot System, which verifies the effectiveness of the simulation resultsand proves the feasibility of simulation methods oriented to the actual hardware system.CPG (Central Pattern Generator) is used to generate actuation signals throughnumerical simulation for the motors of the modules and the predefined PD-control isemployed to ensure that the planned joint angular displacement trajectory can beexecuted reliably. Downhill Simplex Optimization Algorithm were used to evolve thelocomotion of the robot ensuring that it is possible to get access to the relatively moreoptimized locomotion gaits parameters after large enough numbers of iterativecalculations under a certain objective evaluation function.CPG is distributed, robust against external perturbations and local interaction,effective to produce rhythmic, oscillatory, and smoothly modulated signals for robots?locomotion. Here coupled dynamic nonlinear oscillators are used to implement CPG, andthe outputs of the CPGs are sent to actuate the servomotors of the modules. Firstderivative-free Downhill Simplex Algorithm was responsible to evolve the parameters ofthe CPG controller in order to achieve more optimized locomotion performance.Finally, the data from the simulation experiments are transferred to the Cubic-crystalMSRR system, proving the effectiveness of the motion planning and the locomotionoptimization method.