An embedded system architecture based on genetic algorithms for mission and safety planning with UAV

Arantes, Jesimar da Silva; Arantes, Márcio da Silva; Toledo, Cláudio Fabiano Motta; Júnior, Onofre Trindade; Williams, Brian

doi:10.1145/3071178.3071302

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Each task previously mentioned will have multiple attributes that will need to be optimised in order to meet a single or multiple objectives. For instance, the path planning task will optimise attributes such as fuel, energy and time to achieve a goal(s) of reaching the destination, avoiding obstacles and/or flight under certain altitude [ 88 , 89 ].…”

Section: Definition and Categorisation Of Onboard Unmanned Aircrafmentioning

confidence: 99%

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%

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

Mejias

Diguet²,

Dezan

et al. 2021

Sensors

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“…In addition to external sources, IFA also considers that some internal resources can be used. For example, critical conditions may obligate an emergency landing, such as an imminent failure in an internal system, where an internal re-planning algorithm can define an emergency landing route as proposed in [25] [26].…”

Section: Related Workmentioning

confidence: 99%

“…Emergency landings also demand reroute, since it requires a safe place to land. In this case, the genetic algorithm (GA) proposed in [25] [26] was used for emergency landings with fitness function given in Equation (19). This approach deals with uncertainty for aircraft position, so a variance of 10 m is set in its covariance.…”

Section: ) Air Collision H1mentioning

confidence: 99%

Unmanned Aerial Vehicles Flight Safety Improvement Using In-Flight fd

Mattei¹,

Orbital²,

Toledo³

et al. 2021

IIM

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This paper presents a novel onboard system called In-Flight Awareness Augmentation System (IFA 2 S) to improve flight safety. IFA 2 S is designed to semi-automatically (with human supervision) avoid hazards and accidents due to either internal or external causal factors. The requirements were defined in an innovative way using Systems-Theoretic Process Analysis (STPA) method and applied next to model the system. IFA 2 S increases aircraft awareness regarding both itself and its environment and, at the same time, recognizes platform and operational constraints to act in accordance to predefined decision algorithms. Results are presented through simulations and flight tests using state machines designed to allow the adoption of appropriate actions for the identified hazards. The different decision algorithms are evaluated over as many as possible hazard situations by simulations conducted with software Labview and XPlane flight simulator. Flight tests are performed in a small fixed wing aircraft and make use of a limited version IFA 2 S, partially attending identified requirements. Results support the conclusion that IFA 2 S is capable of improving flight safety.

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RETRACTED CHAPTER: Software Project Planning Through Comparison of Bio-inspired Algorithms

Silva

Varela

Molina

et al. 2020

Communications in Computer and Information Science

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Currently many organizations have adopted the development of software projects with agile methodologies, particularly Scrum, which has more than 20 years of development. In these methodologies, software is developed iteratively and delivered to the client in increments called releases. In the releases, the goal is to develop system functionality that quickly adds value to the client's business. At the beginning of the project, one or more releases are planned. For solving the problem of replanning in the context of releases, a model is proposed considering the characteristics of agile development using Scrum. The results obtained show that the algorithm takes a little less than 7 min for solutions that propose replanning composed by 16 sprints, which is equivalent to 240 days of project. They show that applying a repair operator increases the hypervolume quality indicator in the resulting population.

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An embedded system architecture based on genetic algorithms for mission and safety planning with UAV

Cited by 6 publications

References 12 publications

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

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

Unmanned Aerial Vehicles Flight Safety Improvement Using In-Flight fd

RETRACTED CHAPTER: Software Project Planning Through Comparison of Bio-inspired Algorithms

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