Enhanced genetic path planning for autonomous flight

Ragusa, Vincent R.; Mathias, H. David; Kazakova, Vera A.; Wu, Annie S.

doi:10.1145/3071178.3071293

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…In contrast to this, we are using customized operators involving the weather and the speed of the vessel when changing a solution to achieve better improvements than with general operators. Ragusa et al (2017) also investigate a GA for "micro aerial vehicles". The algorithm is similar to the approach presented by this paper for finding routes for an initial population for our GA.…”

Section: Related Problemsmentioning

confidence: 99%

“…The algorithm is similar to the approach presented by this paper for finding routes for an initial population for our GA. However, for the problem of weather routing, intersections are infeasible, which is different than in the approach of Ragusa et al (2017). In their approach, intersections are allowed and the algorithm focuses on minimizing the degree of intersection with obstacles.…”

Section: Related Problemsmentioning

confidence: 99%

“…In their approach, intersections are allowed and the algorithm focuses on minimizing the degree of intersection with obstacles. Furthermore, the routes used by Ragusa et al (2017) have fewer waypoints than in the case of ship routing, because the routes are much shorter and need fewer waypoints.…”

Section: Related Problemsmentioning

confidence: 99%

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A genetic algorithm for finding realistic sea routes considering the weather

Kuhlemann

Tierney

2020

J Heuristics

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The weather has a major impact on the profitability, safety, and environmental sustainability of the routes sailed by seagoing vessels. The prevailing weather strongly influences the course of routes, affecting not only the safety of the crew, but also the fuel consumption and therefore the emissions of the vessel. Effective decision support is required to plan the route and the speed of the vessel considering the forecasted weather. We implement a genetic algorithm to minimize the fuel consumption of a vessel taking into account the two most important influences of weather on a ship: the wind and the waves. Our approach assists route planners in finding cost minimal routes that consider the weather, avoid specified areas, and meet arrival time constraints. Furthermore, it supports ship speed control to avoid areas with weather conditions that would result in high fuel costs or risk the safety of the vessel. The algorithm is evaluated for a variety of instances to show the impact of weather routing on the routes and the fuel and travel time savings that can be achieved with our approach. Including weather into the routing leads to a savings potential of over 10% of the fuel consumption. We show that ignoring the weather when constructing routes can lead to routes that cannot be sailed in practice. Furthermore, we evaluate our algorithm with stochastic weather data to show that it can provide high-quality routes under real conditions even with uncertain weather forecasts.

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Section: Related Problemsmentioning

confidence: 99%

Section: Related Problemsmentioning

confidence: 99%

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A genetic algorithm for finding realistic sea routes considering the weather

Kuhlemann

Tierney

2020

J Heuristics

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“…GAs are a type of optimization algorithms inspired by the mechanisms of natural selection, such as survival of the fittest, genetic mutations, and inheritance 0.05 1.00 0.50 0.20 0.10 0.06 0.04 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 through gene recombination. GAs have been successfully applied toward a variety of complex optimization problems, such as evolving atom positions within metallic nano-cluster formations (Kazakova et al, 2013), flying drone path planning (Ragusa et al, 2017), and even the evolution of neural network topologies (Stanley & Miikkulainen, 2002). GAs are a subset of evolutionary computation approaches.…”

Section: Evolving the S − θ Relationshipmentioning

confidence: 99%

Adaptable and stable decentralized task allocation for hierarchical domains

Kazakova

Sukthankar

2020

The Knowledge Engineering Review

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Many real-world domains can benefit from adaptable decentralized task allocation through emergent specialization, especially in large teams of non-communicating agents. We begin with an existing bio-inspired response threshold reinforcement approach for decentralized task allocation and extend it to handle hierarchical task domains. We test the extension on self-deployment of a large team of non-communicating agents to patrolling a hierarchically defined set of areas. Results show near-ideal performance across all areas, while minimizing wasteful task switching through the development of specializations and subsequent respecializations when area demands change. A genetic algorithm is then used to evolve even more adaptable and stable task allocation behavior, by incorporating weight and power coefficients into agents’ response threshold reinforcement action probability calculations.

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“…Metaheuristics have been applied to other aviation optimisation problems including gate assignment [8], runway sequencing [7], and flight path planning [31]. The Rolling Window or Receding Horizon approach has been demonstrated to be useful in handling dynamic problems in aviation [6,7,13,21,22,37] and a wide variety of other application domains (e.g.…”

Section: Related Workmentioning

confidence: 99%

A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing

Brownlee

Woodward

Weiszer

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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With increasing demand for air travel and overloaded airport facilities, inefficient airport taxiing operations are a significant contributor to unnecessary fuel burn and a substantial source of pollution. Although taxiing is only a small part of a flight, aircraft engines are not optimised for taxiing speed and so contribute disproportionately to the overall fuel burn. Delays in taxiing also waste scarce airport resources and frustrate passengers. Consequently, reducing the time spent taxiing is an important investment. An exact algorithm for finding shortest paths based on A* allocates routes to aircraft that maintains aircraft at a safe distance apart, has been shown to yield efficient taxi routes. However, this approach depends on the order in which aircraft are chosen for allocating routes. Finding the right order in which to allocate routes to the aircraft is a combinatorial optimization problem in itself.We apply a rolling window approach incorporating a genetic algorithm for permutations to this problem, for real-world scenarios at three busy airports. This is compared to an exhaustive approach over small rolling windows, and the conventional first-come-firstserved ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches.

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Enhanced genetic path planning for autonomous flight

Cited by 7 publications

References 13 publications

A genetic algorithm for finding realistic sea routes considering the weather

A genetic algorithm for finding realistic sea routes considering the weather

Adaptable and stable decentralized task allocation for hierarchical domains

A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing

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