A Survey on Model-Based Mission Planning and Execution for Autonomous Spacecraft

Tipaldi, Massimo; Glielmo, Luigi

doi:10.1109/jsyst.2017.2720682

Cited by 42 publications

(30 citation statements)

References 48 publications

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“…Still, all of them serve the same purpose: allocate tasks and control the actions of the spacecraft. In line with previous works, the authors of this paper propose that autonomous reasoning, node coordination, and planning of activities has to be designed as part of the mission planning framework.…”

Section: Autonomymentioning

confidence: 75%

“…A thorough review of autonomous monolithic satellite missions can be read in Ref. , where the authors explore autonomy as an on‐board functionality that can address both intelligent sensing, mission planning and execution, fault management, and distributed decision making. Their work suggests to take the definition of autonomy levels proposed in the European standard ECSS‐E‐ST‐70‐11C as a starting reference, and refer to the recent progress in FDIR, On‐Board Control Procedures (OBCP) in European missions, and autonomous spacecraft reconfiguration schemes based on Markov Decision Processes.…”

Section: Autonomymentioning

confidence: 99%

“…As suggested in Refs. autonomy is key to operate spacecraft under uncertainty and to cope with unexpected situations. Vamvoudakis et al .…”

Section: Autonomymentioning

confidence: 99%

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

Araguz

Bou-Balust

Alarcón

2018

Systems Engineering

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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 high-performance 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. 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 frameworks (MPS) are then presented as a key component to * Corresponding author. E-mail addresses: {carles.araguz, elisenda.bou, eduard.alarcon}@upc.edu 1 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 tradeoffs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.

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Section: Autonomymentioning

confidence: 75%

Section: Autonomymentioning

confidence: 99%

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

Araguz

Bou-Balust

Alarcón

2018

Systems Engineering

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“…The first one is a well-known extension of the BN, named Decision Networks [9]. The second model is the Markov Decision Process model that is a very common model [25] used in robotics. The MDP model can be useful to specify to more complex missing.…”

Section: Methodology For the Bn Integration At The Mission Manager Levelmentioning

confidence: 99%

Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles

Dezan

Zermani²,

Hireche

2020

Algorithms

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Bayesian Networks (BN) are probabilistic models that are commonly used for the diagnosis in numerous domains (medicine, finance, transport, robotics, …). In the case of autonomous vehicles, they can contribute to elaborate intelligent monitors that can take the environmental context into account. We show in this paper some main abilities of BN that can help in the elaboration of fault detection isolation and recovery (FDIR) modules. One of the main difficulty with the BN model is generally to elaborate these ones according to the case of study. Then, we propose some automatic generation techniques from failure mode and effects analysis (FMEA)-like tables using the pattern design approach. Once defined, these modules have to operate online for autonomous vehicles. In a second part, we propose a design methodology to embed the real-time and non-intrusive implementations of the BN modules using FPGA-SoC support. We show that the FPGA implementation can offer an interesting speed-up with very limited energy cost. Lastly, we show how these BN modules can be incorporated into the decision-making model for the mission planning of unmanned aerial vehicles (UAVs). We illustrate the integration by means of two models: the Decision Network model that is a straightforward extension of the BN model, and the BFM model that is an extension of the Markov Decision Process (MDP) decision-making model incorporating a BN. We illustrate the different proposals with realistic examples and show that the hybrid implementation on FPGA-SoC can offer some benefits.

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“…Environment uncertainties modelling was the subject of numerous works. A very complete and interesting survey may be found in [6]. Although the survey field is the spacecraft modelling and mission planning, the models are basically the same for the UAV mission planning.…”

Section: Related Workmentioning

confidence: 99%

Context/Resource-Aware Mission Planning Based on BNs and Concurrent MDPs for Autonomous UAVs

Hireche

Dezan

Mocanu

et al. 2018

Sensors

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This paper presents a scalable approach to model uncertainties within a UAV (Unmanned Aerial Vehicle) embedded mission manager. It proposes a concurrent version of BFM models, which are Bayesian Networks built from FMEA (Failure Mode and Effects Analysis) and used by MDPs (Markov Decision Processes). The models can separately handle different applications during the mission; they consider the context of the mission including external constraints (luminosity, climate, etc.), the health of the UAV (Energy, Sensor) as well as the computing resource availability including CPU (Central Processing Unit) load, FPGA (Field Programmable Gate Array) use and timing performances. The proposed solution integrates the constraints into a mission specification by means of FMEA tables in order to facilitate their specifications by non-experts. Decision-making processes are elaborated following a “just enough” quality management by automatically providing adequate implementation of the embedded applications in order to achieve the mission goals, in the context given by the sensors and the on-board monitors. We illustrate the concurrent BFM approach with a case study of a typical tracking UAV mission. This case also considers a FPGA-SoC (FPGA-System on Chip) platform into consideration and demonstrates the benefits to tune the quality of the embedded applications according to the environmental context.

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A Survey on Model-Based Mission Planning and Execution for Autonomous Spacecraft

Cited by 42 publications

References 48 publications

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

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

Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles

Context/Resource-Aware Mission Planning Based on BNs and Concurrent MDPs for Autonomous UAVs

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