Time optimal control method for quadruped walking robots are developed and installed into a practical robot system. Each leg is modeled as a two link manipulator whose time optimal control theory has already been established by Bobrow. However, in legged robot systems, each leg supports their body weight, and the reaction forces from its ground must be inside of their friction corn. Moreover, the ZMP (zero moment point) of the robot is constrained for stable walk. Therefore, time optimal control inputs must be designed considering these constraints. SONY ERS-7 is used as a quadruped walking robot and a fundamental experiment is done. From the experimental results, the effectiveness of the developed control algorithm is verified

Though Monte Carlo localization is a popular method for mobile robot localization, it requires a method for recovery of large estimation error in itself. In this paper, a recovery method, which is named an expansion resetting method, is newly proposed. A blending of the expansion resetting method and another, which is called the sensor resetting method, is also proposed. We then compared our methods and others in a simulated RoboCup environment. Typical accidents for mobile robots were produced in the simulator during trials. We could grasp the characteristics of each method. Especially, the blending method was robust against the kidnapped robot problems.

Uncertainty of self-localization is one of the most serious problems related to navigation of autonomous mobile robots. There are some studies that deal with this problem. However, most of them assume that the extent of the uncertainty is known or has been measured previously, though it changes easily with some trivial changes of the environment. To avoid this assumption, we take the following approach: 1) a robot uses a self- localization method that is quite robust against sensing noise and the environment change, 2) the plan for robot behavior is based on the assumption that the robot can recognize the environment perfectly, 3) a novel decision-making algorithm computes proper behavior from the planning result and self-localization results in real time. These algorithms were implemented on a soccer robot of the RoboCup four-legged robot league, and their efficiency was verified with experiments.