3 research outputs found
Flexible Supervised Autonomy for Exploration in Subterranean Environments
While the capabilities of autonomous systems have been steadily improving in
recent years, these systems still struggle to rapidly explore previously
unknown environments without the aid of GPS-assisted navigation. The DARPA
Subterranean (SubT) Challenge aimed to fast track the development of autonomous
exploration systems by evaluating their performance in real-world underground
search-and-rescue scenarios. Subterranean environments present a plethora of
challenges for robotic systems, such as limited communications, complex
topology, visually-degraded sensing, and harsh terrain. The presented solution
enables long-term autonomy with minimal human supervision by combining a
powerful and independent single-agent autonomy stack, with higher level mission
management operating over a flexible mesh network. The autonomy suite deployed
on quadruped and wheeled robots was fully independent, freeing the human
supervision to loosely supervise the mission and make high-impact strategic
decisions. We also discuss lessons learned from fielding our system at the SubT
Final Event, relating to vehicle versatility, system adaptability, and
re-configurable communications.Comment: Field Robotics special issue: DARPA Subterranean Challenge,
Advancement and Lessons Learned from the Final
Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness
Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for efficiently processing depth measurements to achieve fast navigation of unknown subterranean environments. Animals developed efficient sensorimotor convergence approaches, allowing for rapid processing of large numbers of spatially distributed measurements into signals relevant for different behavioral responses necessary to their survival. Using a spatial inner-product to model this sensorimotor convergence principle, environmentally relative states critical to navigation are extracted from spatially distributed depth measurements using derived weighting functions. These states are then applied as feedback to control a simulated quadrotor platform, enabling autonomous navigation in subterranean environments. The resulting outer-loop velocity controller is demonstrated in both a generalized subterranean environment, represented by an infinite cylinder, and nongeneralized environments like tunnels and caves
Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness
Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for efficiently processing depth measurements to achieve fast navigation of unknown subterranean environments. Animals developed efficient sensorimotor convergence approaches, allowing for rapid processing of large numbers of spatially distributed measurements into signals relevant for different behavioral responses necessary to their survival. Using a spatial inner-product to model this sensorimotor convergence principle, environmentally relative states critical to navigation are extracted from spatially distributed depth measurements using derived weighting functions. These states are then applied as feedback to control a simulated quadrotor platform, enabling autonomous navigation in subterranean environments. The resulting outer-loop velocity controller is demonstrated in both a generalized subterranean environment, represented by an infinite cylinder, and nongeneralized environments like tunnels and caves