In this paper we address the problem of stabilization and local positioning of a four-rotor rotorcraft using computer vision. Our approaches to estimate the orientation and position of the rotorcraft combine the measurements from an Inertial Measurement Unit (IMU) and a vision system composed of a single camera. In the first stage, the vision system is used to estimate the position and yaw angle of the rotorcraft, while in the second stage the vision system is used to estimate the translational velocity of the flying robot. In both cases the IMU gives the pitch and roll angles at a higher rate. The technique used to estimate the position of the rotorcraft in the first stage combines the homogeneous transformation approach for the camera calibration process with the plane-based pose method for estimating the position. In the second stage, a navigation system using the optical flow is also developed to estimate the translational velocity of the aircraft. We present real-time experiments of stabilization and location of a four-rotor rotorcraft.

This paper proposes a dynamic localization algorithm for a variable speed mobile robot. In order to localize a mobile robot with active beacon sensors, a relatively long time is needed, since the distance to the beacon is measured using the time-of-flight of the ultrasonic signal. The measurement time does not cause a high error rate when the mobile robot moves slowly. However, with an increase of the mobile robot's speed, the localization error becomes too high to use for accurate mobile robot navigation. Therefore, in this research into high-speed mobile robot operations, instead of using two active beacons for localization, an active beacon and the predicted path are utilized to localize the mobile robot. This new approach resolves the high-localization error caused by the speed of mobile robot. The performance of this method was verified by comparing it to the conventional method through real-world experiments.

The isotropy of the generalized inertia ellipsoid is an evaluation index that can measure dynamic performance of a robot. This has significance in motion planning and design of a jumping robot. The generalized inertia of a jumping robot is analyzed. The generalized inertia tensor and the generalized inertia ellipsoid (GIE) are derived from the kinetic energy of the robot mechanism. From the viewpoint of geometrical shape change of the GIE, nonlinear characteristics of a jumping robot are analyzed. With the goal of minimizing nonlinear effects during its movement, the mechanism parameters of a jumping robot are optimized by adopting isotropy of the generalized inertia ellipsoid as its objective function. Example results demonstrate the efficiency and validity of the proposed optimization method.