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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.