Arne-Christoph Hildebrandt

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This paper introduces a new state estimator for biped robots fusing encoder, inertial and force torque measurements. The estimator is implemented as a Kalman filter that uses the dynamical model of the linear inverted pendulum with the center of mass (CoM) state as output. In order to compensate for disturbances and model errors we extend the model by a(More)
In this paper we present a step-planner embedded in a framework which enables a humanoid robot to navigate among obstacles, exploiting its overall capacities. The system allows the robot to react to changes of user input or changes in a dynamic environment in real-time while walking at reasonable speeds. The proposed method relies neither on external(More)
In order to achieve fully autonomous humanoid navigation, environment perception must be both fast enough for real-time planning in dynamic environments and robust against previously unknown scenarios. We present an open source, flexible and efficient vision system that represents dynamic environments using simple geometries. Based only on onboard sensing(More)
Pushing, soft ground contact and other unknown large disturbances can cause humanoid robots to fall. In order to face such problems, an accurate and fast model of the robot is necessary to estimate the state at a certain time in the future using only current measurements. We approximate the robot by a three-mass model with two degrees of freedom. Unilateral(More)
The ability to avoid collisions is crucial for locomotion in cluttered environments. It is not enough to plan collision-free movements in advance when the environment is dynamic and not precisely known. We developed a new method which generates locally optimized trajectories online during the feedback control in order to dynamically avoid obstacles. This(More)
A well known strategy in biped locomotion to prevent falling in the presence of large disturbances is to modify next footstep positions of the robot. Solving this complex control problem for the overall model of the robot is a challenging task. Published methods employ either linear models or heuristics to determine those positions. This paper introduces a(More)
A well known strategy in bipedal locomotion to prevent falling in the presence of large disturbances is to modify drastically future motion. This is an important capability of a walking control system in order to bring humanoid robots from controlled laboratory conditions to real environment situations. This paper presents a predictive stabilization method(More)
Common industrial automation approaches consist on heavy and fixed robotic manipulators working in separated and closed production lines. Recent advances in sensing and control yield flexible and versatile robotic manipulator platforms, which could work in barrier-free production areas and might be the next advance in industrial production. In our(More)
Classic biped walking controllers assume a perfectly flat, rigid surface on which the robot walks. While walking over unknown terrain, robots need to sense and estimate the ground location. Errors in this estimation result in an unexpected early or late ground contact of the swing foot. In this paper, we analyze how these errors affect walking stability.(More)
Autonomous navigation in dynamic and unknown environments requires real-time path planning. Solving the path planning problem for bipedal locomotion quickly and robustly is one of the main challenges in making humanoid robots competitive against mobile platforms. In this letter, we propose strategies to use mobile platform planners for improving the(More)