Deploying multi-satellite constellations for Earth observation requires coordinating potentially hundreds of spacecraft. With increasing onboard capability for autonomy, we can view the constellation as a multi-agent system (MAS) and employ decentralized scheduling solutions. We analyze the multi-satellite constellation observation scheduling problem (COSP) and formulate it as a distributed constraint optimization problem (DCOP). COSP requires scalable inter-agent communication and computation and consists of millions of variables which, coupled with the assumptions and structure, make existing DCOP algorithms inadequate for this application. We develop a scheduling approach that employs a carefully constructed heuristic, referred to as the Geometric Neighborhood Decomposition (GND) heuristic, to decompose the global DCOP into sub-problems to enable the application of DCOP techniques. We present the Neighborhood Stochastic Search (NSS) algorithm, a decentralized algorithm to effectively solve COSP and other large-scale distributed problems, using decomposition. The experiments confirm the efficacy of the approach against baseline algorithms, and we discuss the generality of NSS, GND, and properties of COSP to other domains.

Deploying multi-satellite constellations for Earth observation requires coordinating potentially hundreds of spacecraft. With increasing on-board capability for autonomy, we can view the constellation as a multi-agent system (MAS) and employ decentralized scheduling solutions. We formulate the problem as a distributed constraint optimization problem (DCOP) and desire scalable inter-agent communication. The problem consists of millions of variables which, coupled with the structure, make existing DCOP algorithms inadequate for this application. We develop a scheduling approach that employs a well-coordinated heuristic, referred to as the Geometric Neighborhood Decomposition (GND) heuristic, to decompose the global DCOP into sub-problems as to enable the application of DCOP algorithms. We present the Neighborhood Stochastic Search (NSS) algorithm, a decentralized algorithm to effectively solve the multi-satellite constellation observation scheduling problem using decomposition. In full, we identify the roadblocks of deploying DCOP solvers to a large-scale, real-world problem, propose a decomposition-based scheduling approach that is effective at tackling large scale DCOPs, empirically evaluate the approach against other baseline algorithms to demonstrate the effectiveness, and discuss the generality of the approach.

Dynamic targeting (DT) is an emerging concept for improving science yield on Earth-observing missions limited by power-constrained sensors. DT uses a lookahead sensor together with on-board decision making to save resources for valuable observations in the future. Previous work has focused on developing DT mission use cases, such as storm hunting and cloud avoidance, that have relatively straightforward observation goals (i.e., look for storms, avoid clouds). However, DT has the potential to improve the science return of more complex missions and studies. To demonstrate this, we present and develop a new DT mission scenario to study the Planetary Boundary Layer (PBL). This paper describes the elements of our PBL mission scenario, which not only only involves multiple spacecraft, but also more sophisticated instruments, science models, and on-board decision making.

Recent developments in New Space companies have led to a dramatic increase in capabilities in Earth Observation. These advances in edge computing, low latency communications, and many new on orbit assets represent a unique opportunity for Earth Observation. NASA’s New Observation System (NOS) program aims to leverage these new capabilities to achieve global reach of science events such as volcanic eruption, wildfires, flooding, not by wide swath instruments but rather by intelligent, directed sensing, onboard analysis, and dissemination of knowledge rather than data using low latency communications links. We describe ongoing efforts to deploy NOS capabilities to the CogniSAT-6/HAMMER satellite launched in March 2024, with a currently projected flight demonstration of late summer or early fall 2024.