Vision-aided planning for robust autonomous navigation of small-Scale quadruped robots

Abstract

Robust path planning in non-flat and non-rigid terrain poses a significant challenge for small-scale legged robots. For a quadruped robot to reliably operate autonomously in complex environments, it must be able to continuously determine sequences of feasible body positions that lead it towards a goal while maintaining balance and avoiding obstacles. Current solutions to the problem of motion planning have several shortcoming such as not exploiting the full flexibility of legged robots and not scaling well with environment size or complexity. In this thesis, we address the problem of navigation of quadruped robots by proposing and implementing a vision-aided planning framework on top of existing motion controllers that combines terrain awareness with graph-based search techniques. In particular, the proposed approach exploits the distinctive obstacle-negotiation capabilities of legged robots while keeping the computational complexity low enough to enable planning over considerable distances in real-time. We showcase the effectiveness of our approach both in simulated environments and on actual hardware using the MIT Mini-Cheetah Vision robotic platform.