Humanoid full-body motion generation based on human gait using evolutionary pareto multi-objective optimization - Ghotoorlar, Saied Mokaram (2012)
Designing and realizing artificial systems in human image have always been a fascinating idea for researchers. Humanoid robots with human-like expression are capable of executing tasks in complex environments within the living space of humans. The first and the most important motion for humanoid robot is the walking in a complicated and dynamically balanced manner which differentiates it from other robots. The primary motivation behind this work is to propose a more realistic full-body motion generation method based on learning and optimization in order to translate the recorded human motion to a dynamically feasible motion for a bipedal humanoid robot. Following the objective of this work, high quality captured human motions are used to show the trajectory sequence of robot joints movements. Evolutionary pareto multi-objective optimization method is used in this work in order to optimize an artificial neural network weights which is responsible of applying appropriate modifications on the reference motion lower-body based on the robot real-time sensory feedbacks. Evolutionary pareto multi-objective optimization method is applied to find an optimized artificial neural network based solution for translating the recorded rough walking motion to a dynamically balanced one with maximum similarity to the human way of walking. Because of the numerous advantages of computer simulation, the simulated Sony QRIO humanoid in USARSim simulator is utilized in this work as a proper platform formimicking human motions. According to the communication protocols in USARSim and by importing multithreading from Java to Matlab, a powerful Mobile Robots Communication and Control Framework (MCCF) is developed. It offers faster and easier communication process with the USARSim server within Matlab code. It takes the advantages of other analysis and control methods that have been provided in Matlab tool-boxes. Finally, a full-body motion generation method was introduced which is able to translate the original human motion data to a dynamically stable motion for a specific robot.