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In this paper, dynamic stair climbing and descending are experimentally realized for a biped humanoid robot, HUBO. Currently, in addition to biped walking on the ground, other types of biped walking such as running, jogging, and stair walking (climbing and descending) have been also studied since the end of 1990. In spite of many years of research works on stair walking, it is still a challengeable topic that requires high performance of control technique. For dynamic stair walking, we designed stair climbing and descending patterns according to a known stair configuration. Next, we defined stair climbing and descending stages for a switching control strategy. In each stage, we designed and adopted several online controllers to maintain the balance. For the simplicity and easy application, the online controllers only use the force and torque signals of the force/torque sensors of the feet. Finally, the effectiveness and performance of the proposed strategy are verified through stair climbing and descending experiments of HUBO.

Inspired by quadruped animals we developed the hybrid legged-wheeled robot ASGUARD. We showed already that this robot is able to cope with a variety of stairs, very rough terrain, and is able to move very fast on flat ground. We will describe a versatile adaptive control approach for such a system which is based only on proprioceptive data. An additional inclination and roll feedback is used to make the same controller more robust in terms of stair-climbing capabilities. At the same time, high velocities can be reached on flat ground without changing the configuration of the system. By using twenty compliant legs, which are mounted around four individually rotating hip-shafts, we abstract from the biological system. For the locomotion control we use an abstract model of bio-inspired Central Pattern Generators (CPG) which can be found in biological systems from humans to insects. In contrast to existing work, ASGUARD uses the sensed feedback of the environment to adapt the walking pattern in real time.

Stair climbing is one of the key issues for walking robots involved in urban search and rescue missions. This article presents a parametrized stair climbing strategy for a six legged walking robot. By the parameters we mean the height and depth of the steps provided on-line. These data connected with the information about the robot dimensions allows automatic stair negotiation. The strategy is a closed-loop control based on the position of the robot on stairs. Additionally, to allow the correction of the horizontal position and orientation of the robot on the stairs the information about the distance from the side-walls and about orientation of the robot w.r.t.the stair-steps should be provided. The strategy was preliminary tested on the robot simulator. The validation on the real robot is in progress.

This paper discusses the problem of multisensor perception for autonomous stair climbing. The perception system is mounted on the Messor six-legged walking robot. The robot, due to its static stability while walking, is able to traverse obstacles in urban space, especially stairs. Messor while climbing stairs uses an adaptive algorithm, which exploits on-line perception of the stair geometry and robot pose with regard to the stair. The ascent procedure consist of three main parts. The first – preparation – measurements are performed in order to obtain information about the geometry of the stairs. The second – climbing – ascending each stair with correction of the robot orientation and horizontal position on the stairs. The third – landing – detection of the last stair and the end of the stair climbing procedure. The paper is focused on the multisensor system and the perception algorithms.

This paper presents the design of a quadrupedal robot that can automatically adapt its gait to, and climb, staircases of different configurations. This is accomplished by endowing the robot with a parameterized gait for stair climbing: First, a gait plan is synthesized that allows the robot to climb a stair of known dimensions. Second, the robot approaches a previously unseen stair and perceives its height and width by using an onboard vision system. Third, the synthesized gait plan is parameterized by the perceived estimates of height and width of the stair. Fourth, the robot executes the parameterized gait to climb the staircase; this thereby eliminates the need for a complex control system to achieve the same purpose. Whereas quadruped robots have previously demonstrated stair climbing, to the best of our knowledge, none have so far been capable of climbing stairs of variable height while simultaneously carrying all the needed perception, processing, and power modules on-board. Our work is one of the first successful attempts toward the above goal. Results with the robot climbing a variety of stair configurations demonstrate the effectiveness of our approach.