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

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

Efficient Inter-Team Task Allocation in RoboCup Rescue

The coordination of cooperative agents involved in rescue missions is an important open research problem. We consider the RoboCup Rescue Simulation (RCS) challenge, where teams of agents perform urban rescue operations. Previous approaches typically cast such problem as separate single-team allocation problems. However, different teams have complementary capabilities, and therefore some kind of inter-team coordination is desirable for high-quality solutions. Our contribution considers inter-team coordination using Max-Sum. We present a methodology that allows teams in RCS to efficiently assess joint allocations. Furthermore, we show how to reduce the algorithm's computational complexity from exponential to polynomial time by using Tractable High Order Potentials. To the best of our knowledge this is the first time where it has been shown that MS can be run in polynomial time in the RCS challenge without relaxing the problem. Experiments with fire brigades and police agents show that teams employing inter-team coordination are significantly more effective than uncoordinated teams. Moreover, the evaluation shows that our BMS and THOPs method achieves up to 2.5 times better results than other state-of-the-art methods. Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems.Work funded by projects DAMAS (TIN2013-45732-C4-4-P), COR (TIN2012-38876-C02-01), the Generalitat of Catalunya grant 2009-SGR-1434, and the Ministry of Economy and Competitivity grant BES-2010-030466.Peer reviewe

Binary max-sum for multi-team task allocation in RoboCup Rescue

Coordination of agents involved in rescue missions is an important open research problem. We focus on the RoboCup Rescue Simulation (RCS) challenge, where different teams of agents perform urban rescue operations. Previous approaches typically cast such coordination problem as separate single-team allocation problems, and solve them separately. Our first key contribution is to focus on the max-sum approach, which has been successfully applied in this setting. We show that it is possible to reduce the computational complexity associated to max- sum from exponential to polynomial time. Our empirical evaluation shows that, by using our approach, the fire brigades team obtains significantly better results when compared to state-of- the-art approaches. Our second key contribution is a methodology that allows teams in RCS to make joint allocations. Specifically, our approach supports a modular design, where teams are independently modeled and subsequently connected via well-defined coordination points. To the best of our knowledge, this is the first task-assignment approach in the literature that enables teams in RCS to make simultaneous joint allocations. Experiments with fire brigades and police agents show that teams employing inter-team coordination are significantly more effective than uncoordinated teams.Work funded by projects RECEDIT (TIN2009-13591-C02- 02), AT (CSD2007-0022), COR (TIN2012-38876-C02-01), MECER (201250E053), the Generalitat of Catalunya grant 2009-SGR-1434, and the Ministry of Economy and Competitivity grant BES-2010-030466Peer Reviewe

Decentralised Coordination in RoboCup Rescue

Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of effort in order to be completed by its deadline. Third, new tasks may continually appear or disappear from the environment, thus requiring the responders to quickly recompute their allocation of resources. Fourth, forming teams or coalitions of multiple agents from different agencies is vital since no single agency will have all the resources needed to save victims, unblock roads, and extinguish the ?res which might erupt in the disaster space. Given this, coalitions have to be efficiently selected and scheduled to work across the disaster space so as to maximise the number of lives and the portion of the infrastructure saved. In particular, it is important that the selection of such coalitions should be performed in a decentralised fashion in order to avoid a single point of failure in the system. Moreover, it is critical that responders communicate only locally given they are likely to have limited battery power or minimal access to long range communication devices. Against this background, we provide a novel decentralised solution to the coalition formation process that pervades disaster management. More specifically, we model the emergency management scenario defined in the RoboCup Rescue disaster simulation platform as a Coalition Formation with Spatial and Temporal constraints (CFST) problem where agents form coalitions in order to complete tasks, each with different demands. In order to design a decentralised algorithm for CFST we formulate it as a Distributed Constraint Optimisation problem and show how to solve it using the state-of-the-art Max-Sum algorithm that provides a completely decentralised message-passing solution. We then provide a novel algorithm (F-Max-Sum) that avoids sending redundant messages and efficiently adapts to changes in the environment. In empirical evaluations, our algorithm is shown to generate better solutions than other decentralised algorithms used for this problem