A learnheuristic approach for the team orienteering problem with aerial drone motion constraints
This work proposes a learnheuristic approach (combination of heuristics with machine learning) to solve an aerial-drone team orienteering problem. The goal is to maximise the total reward collected from information gathering or surveillance observations of a set of known targets within a fixed amoun...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:296841 |
| Acceso en línea: | https://ddd.uab.cat/record/296841 https://dx.doi.org/urn:doi:10.1016/j.asoc.2020.106280 |
| Access Level: | acceso abierto |
| Palabra clave: | Aerial drones Learnheuristics Machine learning Metaheuristics Route-dependent edge times Team orienteering problem |
| Sumario: | This work proposes a learnheuristic approach (combination of heuristics with machine learning) to solve an aerial-drone team orienteering problem. The goal is to maximise the total reward collected from information gathering or surveillance observations of a set of known targets within a fixed amount of time. The aerial drone team orienteering problem has the complicating feature that the travel times between targets depend on a drone's flight path between previous targets. This path-dependence is caused by the aerial surveillance drones flying under the influence of air-resistance, gravity, and the laws of motion. Sharp turns slow drones down and the angle of ascent and air-resistance influence the acceleration a drone is capable of. The route dependence of inter-target travel times motivates the consideration of a learnheuristic approach, in which the prediction of travel times is outsourced to a machine learning algorithm. This work proposes an instance-based learning algorithm with interpolated predictions as the learning module. We show that a learnheuristic approach can lead to higher quality solutions in a shorter amount of time than those generated from an equivalent metaheuristic algorithm, an effect attributed to the search-diversity enhancing consequence of the online learning process. |
|---|