No abstract

Autonomous reconnaissance missions are called for in extreme environments, as well as in potentially hazardous (e.g., the theatre, disaster-stricken areas, etc.) or inaccessible operational areas (e.g., planetary surfaces, space). Such future missions will require increasing degrees of operational autonomy, especially when following up on transient events. Operational autonomy encompasses: (1) Automatic characterization of operational areas from different vantages (i.e., spaceborne, airborne, surface, subsurface); (2) automatic sensor deployment and data gathering; (3) automatic feature extraction including anomaly detection and region-of-interest identification; (4) automatic target prediction and prioritization; (5) and subsequent automatic (re-)deployment and navigation of robotic agents. This paper reports on progress towards several aspects of autonomous C 4 ISR systems, including: Caltech-patented and NASA award-winning multi-tiered mission paradigm, robotic platform development (air, ground, water-based), robotic behavior motifs as the building blocks for autonomous telecommanding, and autonomous decision making based on a Caltech-patented framework comprising sensor-data-fusion (feature-vectors), anomaly detection (clustering and principal component analysis), and target prioritization (hypothetical probing).

Anomaly detection and target prioritization in planetary imagery via the automated global feature analyzer (AGFA): Progress towards a driver for autonomous C4ISR missions,"

Planetary missions are typically confined to navigationally safe environments, leaving areas of interest in rugged and/or hazardous terrain largely unexplored. Identifying and avoiding possible hazards requires dedicated path planning and limits the effectiveness of (semi-)autonomous systems. An adaptable, fully autonomous design is ideal for investigating more dangerous routes, increasing robotic exploratory capabilities, and improving overall mission efficiency from a science return perspective. We introduce hierarchical Lidar-based behavior motifs encompassing actions, such as velocity control, obstacle avoidance, deepest path navigation/exploration, and ratio constraint, etc., which can be combined and prioritized to form more complex behaviors, such as free roaming, object tracking, etc., as a robust framework for designing autonomous exploratory systems. Moreover, we introduce a dynamic Lidar environment visualization tool. Developing foundational behaviors as fundamental motifs (1) clarifies response priority in complex situations, and (2) streamlines the creation of new behavioral models by building a highly generalizable core for basic navigational autonomy. Implementation details for creating new prototypes of complex behavior patterns on top of behavior motifs are shown as a proof of concept for earthly applications. This paper emphasizes the need for autonomous navigation capabilities in the context of space exploration as well as the exploration of other extreme or hazardous environments, and demonstrates the benefits of constructing more complex behaviors from reusable standalone motifs. It also discusses the integration of behavioral motifs into multi-tiered mission architectures, such as Tier-Scalable Reconnaissance.

No abstract