Aerial Field Robotics

Aerial field robotics research represents the domain of study that aims to equip unmanned aerial vehicles - and as it pertains to this chapter, specifically Micro Aerial Vehicles (MAVs)- with the ability to operate in real-life environments that present challenges to safe navigation. We present the key elements of autonomy for MAVs that are resilient to collisions and sensing degradation, while operating under constrained computational resources. We overview aspects of the state of the art, outline bottlenecks to resilient navigation autonomy, and overview the field-readiness of MAVs. We conclude with notable contributions and discuss considerations for future research that are essential for resilience in aerial robotics.Comment: Accepted in the Encyclopedia of Robotics, Springe

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

Radar-Based Perception For Visually Degraded Environments

Autonomous mobile robots are being deployed in ever more varied roles and environments. For instance, a robotic explorer was deployed just this year to Jezero Crater, the most challenging area of Mars yet explored. This might lead one to believe autonomous mobile robots are able to operate in almost any environment. But in fact there exists a broad range of environments that are inaccessible to autonomous robots. Environments with smoke, fog, darkness, and other visually challenging conditions, collectively referred to as visually-degraded environments (VDEs), are among the biggest hurdles to deploying autonomous mobile robots in the field. But a robot is likely to encounter such conditions almost anywhere outside of a well-lit indoor environment. So if autonomous mobile robots are to be truly useful in a broad range of environments, the problem of perception in VDEs must be addressed. In theory, one could avoid visual challenges simply by using a different type of sensor. Millimeter wave radar, for instance, is unaffected by most kinds of airborne particulates and it does not depend on ambient light. But using radar for robotic perception comes with its own challenges. Popular visual and lidar-based methods for perception and state estimation do not adapt well to radar. Additionally, radar measurements are subject to forms of noise unknown in other sensors. This dissertation will cover a number of novel developments in perception and state estimation using millimeter wave radar that address these issues including i) a radar-inertial method for state estimation in smoke and fog, ii) a radar-only occupancy mapping method, iii) a unique dataset for radar-based state estimation and perception, iv) an end-to-end learned method for 3D radar image alignment, and v) a new radar-only detector for moving obstacles in road environments.</p