IN-PASS: Intelligent Navigation, Planning, and Autonomy for Swarm Systems

Orbit Logic is teamed with the University of Colorado Boulder to develop the Intelligent Navigation, Planning, and Autonomy for Swarm Systems (IN-PASS) Solution, which builds on Orbit Logics proven Autonomous Planning System (APS) decentralized planning framework to enable the configuration and execution of collaborative mission concepts. Assessments can be performed completely virtually within an open simulation environment, or can be deployed to physical assets in a testbed or operational environment. We apply IN-PASS to heterogeneous swarms of Lunar orbital and surface assets. For example, the satellite constellation overhead plans sensor collections in support of multiple objectives surface asset localization and surface chemistry detection. APS plans the delivery of data products to a surface asset with high computing capacity, where algorithms are invoked and output the location of rovers and areas of interest (AOIs) for contact science. Location measurements allow Decentralized Data Fusion to maintain shared team awareness - critical to the teams ability to autonomously coordinate. AOI events are trigger events for rovers to navigate to the location. A formal methods approach to onboard planning is employed on the rover assets that utilizes a Markov Data Process to balance performance, resource usage and safety. This is particularly important for inter-asset communications or localization - operational functions that utilize significant stored energy. Astronauts can participate in-the-loop with these swarms using devices running interactive user interfaces that allow them to a) specify mission goals, b) receive feedback on the satisfaction of their requests as the team performs the associated tasks, c) receive and display the end data associated with their requests, and d) actually collaborate with the autonomous robots by electing to assume tasks they are well suited to perform. IN-PASS improves the effectiveness of heterogeneous swarms of collaborating lunar orbital and surface assets. Assets can be surface rovers, satellites, and static surface nodes providing localization, communication and data processing services. We employ formal-methods-based algorithms to determine asset plans that balance performance, resource use, and safety. Autonomous Planning System (APS) decentralized mission autonomy plans, deconflicts and coordinates collection tasks and data orchestration across the swarm. Event-Trigged Decentralized Data Fusion (ET-DDF) helps swarm assets maintain a high-degree of team state knowledge with minimal data exchange. Earth-based operators or astronauts participating in-the-loop with these swarms. Our research explores the most effective interaction between humans and swarm elements including specification of goals, interactive feedback on viability of the human’s requests, and delivery of the resulting science to the human. The theme of our Phase II research is TRL-elevation of IN-PASS via testing and validation in a hardware testbed. We will accomplish this by building out a cell in CU’s ASPEN robotics laboratory.   Technical Objectives: Verify IN-PASS satisfies mission-relevant use cases when deployed to LVC Testbed Extend Markov Decision Process (MDP) and ET-DDF algorithms for realities of a physical system Enhance APS data collection collaboratively fulfill large surface order areas Formally incorporate ET-DDF and MDP Planner algorithms in APS product architecture Demonstrate effective human-robot teaming strategies via HMI user interface in Testbed Work Plan Summary: Evolve IN-PASS Architecture Definitions and Documentation for Phase II Testbed Incorporate timing error models in MDP Planner logic Enhance DDF for event-triggering threshold synthesis Extend APS Decentralized Satellite Collection Planning Logic Develop roadmap for formal incorporation of flight-suited CU Algorithms in APS Evolve Astronaut-in-the-Loop prototype UIs for use in Testbed Integrate, Test and Demo IN-PASS in CU’s Aspen Laboratory Proposed Deliverables: LVC Simulation elements supporting modeling of Lunar Swarm Missions Enhanced Human Machine Interfaces (HMIs) Prototype UIs TRL-elevated MDP Planning algorithm for enhanced rover resource scheduling Enhanced APS modules for ET-DDF-enabled communication and planning

IN-PASS applies to missions with autonomous control, coordination, and localization of heterogenous assets operating in dynamic environments: planetary surface exploration, survey, sampling, and characterization; surface collaborative infrastructure construction/repair; planetary orbital asset collaboration for optimized/event-based space-ground sensor collection/processing; convoys of spacecraft en-route to solar system destinations; coordinating science team behaviors for faults/anomalies. IN-PASS is suitable for small or large swarms. Collaborative Earth observing satellite constellations, coordinated space/ground sensor systems supporting enhanced space situational awareness, coordination of data chain orchestration for data analytics, collaborative autonomous maritime (surface and underwater) missions, coordination of teams of ground orbits and/or air vehicles for science, fire detection/mitigation, search/rescue.

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