Abstract: We propose a framework for resilient autonomous navigation in perceptually
challenging unknown environments with mobility-stressing elements such as
uneven surfaces with rocks and boulders, steep slopes, negative obstacles like
cliffs and holes, and narrow passages. Environments are GPS-denied and
perceptually-degraded with variable lighting from dark to lit and obscurants
(dust, fog, smoke). Lack of prior maps and degraded communication eliminates
the possibility of prior or off-board computation or operator intervention.
This necessitates real-time on-board computation using noisy sensor data. To
address these challenges, we propose a resilient architecture that exploits
redundancy and heterogeneity in sensing modalities. Further resilience is
achieved by triggering recovery behaviors upon failure. We propose a fast
settling algorithm to generate robust multi-fidelity traversability estimates
in real-time. The proposed approach was deployed on multiple physical systems
including skid-steer and tracked robots, a high-speed RC car and legged robots,
as a part of Team CoSTAR's effort to the DARPA Subterranean Challenge, where
the team won 2nd and 1st place in the Tunnel and Urban Circuits, respectively.