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In order to achieve real autonomy, robots have to be able to navigate in completely unknown environments. Due to the complexity of computer vision algorithms, almost every approach for robotic navigation is either based on previous knowledge of the environment, such as markers or as resulting from learning methods, or makes strong simplifying assumptions about it (height-map representations, static scenarios). While showing impressive success in certain applications, these approaches limit the potential of legged robots to achieve the amazing flexibility of humans in more complex environments. In this work, we present a strategy for full 3D vision processing that is able to handle changing, dynamic environments. These are modeled using 3D geometries that are processed in real-time by the motion planner of our biped robot Lola for avoiding moving obstacles and walking over platforms. In order to allow for a more intuitive development of such systems in the future, we present tools for visualization including two mixed reality applications using both an external camera and Microsoft’s HoloLens. We validate our system in simulations and experiments with our full-size humanoid robot Lola and publish our framework open source for the benefit of the community.

This paper presents our newest findings in planning a dynamically and kinematically feasible center of mass motion for bipedal walking robots. We use a simplified robot model to incorporate multi-body dynamics and kinematic limits, while still being able to meet hard real-time requirements. The vertical center of mass motion is obtained through interpolation of a quintic spline whose control points are projected onto the kinematically feasible region. Subsequently, the horizontal motion is computed from multi-body dynamics which we approximate by solving an overdetermined boundary value problem via spline collocation based on quintic polynomials. The proposed algorithm is an improvement of our previous method, which used a parametric torso height optimization for vertical and cubic spline collocation for horizontal components. The novel center of mass motion improves stability, especially for stepping up and down platforms. Moreover, the new method leads to a less complex overall algorithm since it removes the necessity of manually tuned parameters and strongly simplifies the incorporation of boundary values. Lastly, the new approach is more efficient, which leads to a significantly reduced total runtime. The proposed method is validated through successfully conducted simulations and experiments on our humanoid robot platform, LOLA.

Bipedal robots can be better alternatives to other robots in certain applications, but their full potential can only be used if their entire kinematic range is cleverly exploited. Generating motions that are not only dynamically feasible but also take into account the kinematic limits as well as collisions in real time is one of the main challenges towards that goal. We present an approach to generate adaptable torso height trajectories to exploit the full kinematic range in bipedal locomotion. A simplified 2D model approximates the robot's full kinematic model for multiple steps ahead. It is used to optimize the torso height trajectories while taking future motion kinematics into account. The method significantly improves the robot's motion not only while walking in uneven terrain, but also during normal walking. Furthermore, we integrated the method in our framework for autonomous walking and we validated its real-time character in successfully conducted experiments.