With the development of biped robots, systems became able to navigate in a 3 dimensional world, walking up and down stairs, or climbing over small obstacles. We present a method for obtaining a labeled 2.5D grid map of the robot's surroundings. Each cell is marked either as floor or obstacle and contains a value telling the height of the floor or obstacle. Such height maps are useful for path planning and collision avoidance. The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values. We evaluate our approach using stereo vision on the humanoid robot QRIO and show the advantages over previous methods. Experimental results from navigation runs on an obstacle course demonstrate the ability of the method to generate detailed maps for autonomous navigation.

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.

For the fully autonomous navigation in a 3 dimensional world, a humanoid robot must be capable of stepping up and down staircases and other obstacle where a sufficient large flat surface can support the robot's feet. This paper presents methods for the recognition of stairs and a control architecture that enable the humanoid robot QRIO to safely climb up and down in its environment The approach is based on data captured by a stereo vision system and segmented into planar surfaces. From the segmented planes, stairs that can be climbed by the robot are extracted and fed to a control system which decides the action to be taken next. Experimental results on a staircase as well as climbing up and down a sill are presented.

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.

We propose a motion planning method for cooperative transportation of a large object by multiple mobile robots in a 3 dimensional environment. This task has various kinds of problems, such as path planning, manipulation and so on. All of these problems can't be solved at once, since computational time is exploded. Accordingly, we divide a motion planner into a local manipulation planner and a global path (motion) planner, and design these two planners respectively, and we integrate two planners. Namely, we aim at integrating a gross motion planner and a fine motion planner. As to the local manipulation planner, we build a manipulation technique, which is suitable for mobile robots by position control. We compute conditions, in which the object becomes unstable during manipulation, and generate each robot's motion considering the robots' motion errors and indefinite factors from the planning stage. As to the global path planner, we reduce the dimensions of the configuration space (C-space) using the feature of transportation by mobile robots. We can find a solution with searching in this smaller dimensional C-space using the potential field defined in the C-space, and constraints of the object manipulation are considered as the potential function. We verify the effectiveness of our proposed motion planning method through simulations and experiments.

In this paper, we propose a method of an object manipulation by multiple mobile robots using sticks as tools. In the conventional cooperative work by multiple mobile robots, manipulation technique based on force-control has been proposed. However, mobile robots are moving by position-control, and motion errors can easily arise. Then, we build the manipulation technique, which is suitable for mobile robots by position-control. We propose the manipulation method without using sensor information, and consider the motion errors of mobile robots and the indefinite element of environment from the planning stage. We compute the conditions in which the object gets unstable during manipulation, and generate the motion of each mobile robot with these analyses. We verify the effectiveness of our proposed motion planning method through simulations and experiments.