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Perceptive Locomotion on Bipedal Humanoid Robots for Traversing Unknown and Challenging Environments
Dissertation   Open access

Perceptive Locomotion on Bipedal Humanoid Robots for Traversing Unknown and Challenging Environments

Bhavyansh Mishra
University of West Florida Libraries
Doctor of Philosophy (PHD), University of West Florida
2024

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Abstract

Humanoid robots possess one of the most favorable morphologies for performing complex tasks in various environments. Disaster response, space exploration, and manufacturing are only some of the tasks that humanoid robots can potentially accomplish. Bipedal humanoid robots rely on legged locomotion involving terrain perception, contact planning, and whole-body control. However, the underactuated dynamics, high dimensionality, and inherent instability render the simplest tasks extremely challenging for bipedal robots, especially in complex environments. Yet, these same complexities afford them the versatility needed to accomplish challenging tasks. In this work, various perception, planning, and locomotion approaches for humanoid robots operating in complex terrain types are designed, implemented, and evaluated. Firstly, novel perception algorithms for graphics processing units (GPUs) to extract planar regions and elevation maps are presented. Extended mapping algorithms for generating persistent models of the environment are then built upon such instantaneous perception algorithms, bringing benefits in footstep planning due to extended planning horizons. Next, a novel Monte-Carlo Footstep Planner (MCFP) that offers graph-reuse and GPU-acceleration capabilities is presented. Perception and planning algorithms are evaluated separately against state-of-the-art bipedal humanoid systems and later combined within continuous locomotion frameworks. Experimental results for the proposed perceptive-locomotion pipeline demonstrate significant speed improvements in traversing rough terrain compared to existing approaches and reach 0.5x human traversal speeds. A terrain complexity metric is also proposed to help benchmark the presented work against previous approaches. Lastly, high-level behaviors are built upon the locomotion system including a dynamic target-following behavior that enables humanoids to detect, track, and follow dynamic entities over rough terrain. Reaching human-level speeds for traversing rough terrain remains a grand challenge for bipedal robots as it spans multiple areas of active research and involves complex software and hardware systems. This thesis presents efforts to close this gap between human and humanoid capabilities.
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