Multi-satellite Mission Planning Using a Self-Adaptive Multi-agent System

Abstract

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18173 Abstract-Mission planning for a constellation of Earth observation satellites is a complex problem raising significant technological challenges for tomorrow's space systems. The large numbers of customers requests and their dynamic introduction in the planning system result in a huge combinatorial search space with a potentially highly dynamical evolution requirements. The techniques used nowadays have several limitations, in particular, it is impossible to dynamically adapt the plan during its construction even for small modifications. Satellites of a constellation are planned in a chronological way instead of a more collective planning which can provide additional load balancing.In this paper, we propose to solve this difficult and highly dynamic problem using adaptive multi-agent systems, taking advantage from their self-adaptation and self-organization mechanisms. In the proposed system, the agents, through their local interactions, allow to dynamically reach a good solution, while ensuring a controlled distribution of tasks within the constellation of satellites. Finally, a comparison with a classical chronological greedy algorithm, commonly used in the spatial domain, highlights the advantages of the presented system.

Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

“…Since the appearance of the first Earth‐observing constellations and distributed missions, researchers have focused on specialized MPS targeted for DSS . Two remarkable examples of those are the MPS for the Sentinel‐S1 constellation and the one for TerraSAR‐X/TanDEM‐X.…”

“…On the other hand, the planners presented in Refs. , , , , , have been designed for complex satellite systems encompassing, in most of them, tens or hundreds of heterogeneous satellites in multiple configurations (i.e., GEO, LEO, several orbit planes, etc.). Because of that, these works tend to simplify the modeling of subsystems and instruments.…”

“…The Distributed Agents for Autonomy study (DAFA) demonstrated how MAS‐oriented frameworks can improve several system qualities and presented negotiation‐based self‐organizing mechanisms as means to improve the autonomous capabilities of a system. Aligned with this vision, some works have addressed MPS for DSS that could be regarded as MAS . Whereas traditional heuristic approaches are fine‐tuned and optimized for a known number of tasks and satellites, multiagent approaches allow for an adaptability and scalability that is not comparable .…”

“…The system proposed in Ref. defines nine types of agents that allow to model as many elements of the architecture as the mission requires. The authors have designed three cooperative agents that interact to find a solution to the scheduling problem: the satellite , request , and mesh agents.…”

“…The algorithms used in Refs. , , , , , all solve optimization problems, usually with multiple objectives. The Capacity Analysis and Mission Planning Tool developed by DEIMOS calculates the most optimum sequence of observations and allows the end‐user to decide the type of figure of merit that feeds the sequencer in order to fully suit the mission needs.…”