To achieve a realistic task by a recent complicated robot, a practical motion planning method is important. Especially in this decade, sampling-based motion planning methods have become popular thanks to recent high performance computers. In sampling-based motion planning, a graph that covers the state space is constructed based on reachability between node pairs, and the motion is planned using the graph. However, it requires an explicit model of a controlled target. In this research, we propose a motion planning method in which a system model is estimated by using Gaussian process regression. We apply our method to the control of an actual robot. Experimental results show that the control of the robot can be achieved by the proposed motion planning method.
Recently, robots are expected to support our daily lives in real environments. In such environments, however, there are a lot of obstacles and the motion of the robot is affected by them. In this research, we develop a musculoskeletal robotic arm and a system identification method for coping with external forces while learning the dynamics of complicated situations, based on Gaussian process regression (GPR). The musculoskeletal robot has the ability to cope with external forces by utilizing a bioinspired mechanism. GPR is an easy-to-implement method, but can handle complicated prediction tasks. The experimental results show that the behavior of the robot while interacting with its surroundings can be predicted by our method.
This paper presents a shoulder joint for a human-like robotic arm. This joint mechanism is composed of two ball joints laying back to back and the range of motion is larger than that of a usual ball joint. Since the joint is driven by the mutually inter-connected air cylinders, the control signal can be operated equally with a normal ball joint shoulder. The prototype of the joint with 6 air cylinders and its kinematic model were developed to confirm its range of motion. We also achieved an overhand throwing motion with a robotic arm using the proposed shoulder mechanism thanks to the large moving range.
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