BFM: a Scalable and Resource-Aware Method for Adaptive Mission Planning of UAVs

Hireche, Chabha; Dezan, Catherine; Diguet, Jean-Philippe; Mejias, Luís

doi:10.1109/icra.2018.8460944

Cited by 11 publications

(9 citation statements)

References 5 publications

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“…Mission deals with high-level tasks the UAS is responsible for beyond flying operations. It includes autonomous tasks such as mission planning [ 32 ], health monitoring [ 33 ], decision making [ 34 ] and resources management [ 35 ]. Finally, safety refers to the tasks a UAS must execute to ensure the safety of people and assets.…”

Section: Definition and Categorisation Of Onboard Unmanned Aircrafmentioning

confidence: 99%

“…Early attempts to develop health management systems for UAS were focused on teams for persistent surveillance operations [ 83 ]. Approaches based on Bayesian networks for aircraft health-management have also been proposed in the literature [ 34 , 84 ]. Current challenges include efficient algorithms, embedded computing for real-time processing and simulation, validation and verification [ 85 ].…”

Section: Definition and Categorisation Of Onboard Unmanned Aircrafmentioning

confidence: 99%

“…Many factors can impact the inner decision making that allows intelligent task scheduling such as energy consumption, mission goals, aircraft internal state and requirements to handle onboard data acquisition and processing. Therefore, there is an implicit inter-task communication process between computational resource management, energy management, storage management and mission management [ 34 ].…”

Section: Definition and Categorisation Of Onboard Unmanned Aircrafmentioning

confidence: 99%

“…We assign an integer value in the range [1,10] to each of the three aspects of the ALFUS model, HI, MC and EC, for each application (see Figure 4 and Table 2). Based on experience [26,34,76], we propose a number of tasks required to ensure feasibility of each application. We then provide three ways to estimate the UAS AL metric, mode, median and mean (arithmetic) of the three values for HI, MC and EC.…”

mentioning

confidence: 99%

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Embedded Computation Architectures for Autonomy in Unmanned Aircraft Systems (UAS)

Mejias

Diguet²,

Dezan

et al. 2021

Sensors

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This paper addresses the challenge of embedded computing resources required by future autonomous Unmanned Aircraft Systems (UAS). Based on an analysis of the required onboard functions that will lead to higher levels of autonomy, we look at most common UAS tasks to first propose a classification of UAS tasks considering categories such as flight, navigation, safety, mission and executing entities such as human, offline machine, embedded system. We then analyse how a given combination of tasks can lead to higher levels of autonomy by defining an autonomy level. We link UAS applications, the tasks required by those applications, the autonomy level and the implications on computing resources to achieve that autonomy level. We provide insights on how to define a given autonomy level for a given application based on a number of tasks. Our study relies on the state-of-the-art hardware and software implementations of the most common tasks currently used by UAS, also expected tasks according to the nature of their future missions. We conclude that current computing architectures are unlikely to meet the autonomy requirements of future UAS. Our proposed approach is based on dynamically reconfigurable hardware that offers benefits in computational performance and energy usage. We believe that UAS designers must now consider the embedded system as a masterpiece of the system.

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Section: Definition and Categorisation Of Onboard Unmanned Aircrafmentioning

confidence: 99%

Section: Definition and Categorisation Of Onboard Unmanned Aircrafmentioning

confidence: 99%

Section: Definition and Categorisation Of Onboard Unmanned Aircrafmentioning

confidence: 99%

mentioning

confidence: 99%

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Embedded Computation Architectures for Autonomy in Unmanned Aircraft Systems (UAS)

Mejias

Diguet²,

Dezan

et al. 2021

Sensors

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“…In this section, we propose to integrate BN to Markov Decision Process (MDP) [28] to elaborate a more complex mission planning. This model is named BFM cmodel (Bayesian network built from FMEA table to feed the MDP model) [29]. The BFM model relies on an architecture based on two principal modules: the diagnosis module based on BN and the decision-making module based on MDP.…”

Section: Markov Decision Process (Mdp)mentioning

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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