Abstract-Collision-free walking in cluttered environments is still an open issue for humanoids. Most current approaches use heuristics with large safety margins to plan the robot's motion. That way, the chance of collisions can be greatly reduced but the robot movements are limited artificially. In this context, we extend our framework for motion generation and whole-body collision-avoidance by an online predictive kinematic parameter evaluation and optimization: We propose to evaluate the initial parameter set describing the walking pattern by integrating the full kinematic model of the robot. In the model our local optimization technique for collision avoidance is taken into account. Initial parameter sets, which are kinematically infeasible due to kinematic limits or collisions can be identified and adapted before the motion is executed. Additionally, the parameter set is optimized according to a chosen cost function using a gradient method and the step time is adapted according to a desired mean velocity. The optimization method is applicable to different representations of the walking pattern. The method is presented with simulation results obtained with our multi-body simulation. The method is suitable for real-time control, since the optimization can be stopped if it exceeds a predetermined time budget. In that case, an executable but suboptimal result is used. The proposed procedure is executed before each step which makes it very reactive to changes in the environment or in the user input. We have also validated the real-time performance in experiments with our humanoid Lola.

Abstract-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 method successfully combines a local potential field method with a heuristic based on height and width of an obstacle to avoid collisions. The program's main feature is the integration of obstacles into the framework designed for self-collision avoidance presented in [1] and the collisions avoidance in taskspace. We show experimental results validating the method.

Abstract-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 in real-time while walking at reasonable speeds. The proposed method relies neither on external sensors nor on color coding or textured surfaces. The key idea is to use a fast collision model based on swept-sphere-volumes (SSVs) for real-time generation of collision-free footsteps and whole-body trajectories. Using a SSV-based 3D approximation in all control modules enables the robot to avoid collisions with itself and the environment. Obstacles are detected with an on-board RGB-D sensor while the robot navigates through an environment which is not known in advance. A step-planner reacts to high-level user commands like desired velocity and direction within less than a step. Instead of investigating only the footholds an articulated 3D approximation of the lower leg and the foot is considered to find feasible and optimal footstep locations. Additionally, it provides an initial solution for the swing-foot movement. Final, Collision-free swing-foot trajectories are created in real-time in the feedback control layer using all the foot's degrees of freedom. We validated this approach in experiments with our robot Lola.

Autonomous navigation in complex environments featuring obstacles, varying ground compositions, and external disturbances requires real-time motion generation and stabilization simultaneously. In this paper, we present and evaluate a strategy for rejection of external disturbances and real-time motion generation in the presence of obstacles and non-flat ground. We propose different solutions for combining the associated algorithms and analyze them in simulations. The promising method is validated in experiments with our robot Lola. We found a hierarchical approach to be effective for solving these complex motion generation problems, because it allows us to decompose the problem into sub-problems that can be tackled separately at different levels. This makes the approach suitable for real-time applications and robust against perturbations and errors. Our results show that real-time motion planning and disturbance rejection can be combined to improve the autonomy of legged robots.

No abstract