This paper analyzes numerically the minimum turning radius of a quad-plane UAV in high-speed flights based on its dynamic model. The smallest turning radius that satisfies the flight conditions for a steady level turn is searched for by numerical optimization at various flight speeds. The turning radius is significantly reduced by utilizing the four rotors mounted on the UAV for rotary-wing mode, although they are not used in the conventional fixed-wing mode of flight. The four rotors allow the UAV to increase its lift and to control its moment in addition to the aerodynamic lift and moment. This enhanced maneuverability of the UAV due to the rotors leads to higher turning performance. The numerical results also show that the reduction in turning radius depends on the flight speed.

This paper reveals that the turning radius of a tiltrotor UAV during high-speed flights can be significantly reduced by utilizing the redundancy in control inputs. Based on a dynamic model of the UAV, the flight conditions of steady level turns are analyzed to numerically search for the minimum turning radius when all the actuators installed in the UAV are utilized. Numerical results indicate that adding lift with sub-rotors installed for rotary-wing mode makes it possible to increase the roll angle during level turns while reducing the turning radius. The tilt angle of main rotor can also be chosen within a certain range to reduce the turning radius. The feasibility of such steep turns is checked by numerical simulations using a feedback control.

We propose neural models for segmenting the area of a body from visual scene based on predictability. Neuroscience has shown that a prediction model in brain, which predicts sensory-feedback from motor command, can divide the sensory-feedback into the self-motion derived feedback and other derived feedback. The prediction model is important for prediction control of the body. Previous studies in robotics of the prediction model assumed that a robot can recognize the position of its body (e.g. its hand) and that the view contains only that body part. In our models, motor commands and visual feedback (pixel image that includes not only a hand but also object and background) are input into a neural network model and then the body area is segmented and prediction model of body is acquired. Our model contains two parts: 1) An object detection model obtains a conversion system between object positions and the pixel image. 2) A movement prediction model predicts hand-object positions from motor commands and identifies the body. We confirmed that our models can segment the body/object area based on their pixel textures and discriminate between them by using prediction error.

This work presents methods for path planning and obstacle avoidance for the humanoid robot QRIO, allowing the robot to autonomously walk around in a home environment. For an autonomous robot, obstacle detection and localization as well as representing them in a map are crucial tasks for the success of the robot. Our approach is based on plane extraction from data captured by a stereo-vision system that has been developed specifically for QRIO. We briefly overview the general software architecture composed of perception, short and long term memory, behavior control, and motion control, and emphasize on our methods for obstacle detection by plane extraction, occupancy grid mapping, and path planning. Experimental results complete the description of our system.

This paper describes a method for identifying an environment a robot is operating in by comparing the geometry of landmarks of a map the robot is currently building with a set of previously created maps. Landmark maps are created using the stochastic map approach originally presented by Smith, Self and Cheeseman [1990]. The paper provides a method for measuring the similarity of such maps and presents a closed form solution for the special case that covariances are constant. Experiments carried out on a developer version of the AlBO robot and on a prototype of the humanoid SDR-4X robot show that the approach is applicable even for robots with poor odometry.