Imagen de Portada

Payload- and Energy-Aware Tactical Allocation Loop-Based Path-Planning Algorithm for Urban Fumigation Robots

Guardado en:
Publicado en:Mathematics vol. 13, no. 6 (2025), p. 950
Autor principal: Chittoor, Prithvi Krishna
Otros Autores: Bhanu Priya Dandumahanti, Abishegan, M, Konduri, Sriniketh, S M Bhagya P Samarakoon, Mohan, Rajesh Elara
Publicado:
MDPI AG
Materias:
Energy management
Accuracy
Cost function
Optimization
Robots
Localization
Energy consumption
Comparative studies
Cameras
Genetic algorithms
Network management systems
Energy costs
Pests
Pest control
Neural networks
Sensors
Automobile safety
Searching
Health risk assessment
Public health
Energy efficiency
Chemicals
Spraying
Methods
Algorithms
Permutations
Mosquitoes
Real time
Cost control
Path planning
Fumigation
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:Fumigation effectively manages pests, yet manual spraying poses long-term health risks to operators, making autonomous fumigation robots safer and more efficient. Path planning is a crucial aspect of deploying autonomous robots; it primarily focuses on minimizing energy consumption and maximizing operational time. The Payload and Energy-aware Tactical Allocation Loop (PETAL) algorithm integrates a genetic algorithm to search for waypoint permutations, applies a 2-OPT (two-edge exchange) local search to refine those routes, and leverages an energy cost function that reflects payload weight changes during spraying. This combined strategy minimizes travel distance and reduces energy consumption across extended fumigation missions. To evaluate its effectiveness, a comparative study was performed between PETAL and prominent algorithms such as A*, a hybrid Dijkstra with A*, random search, and a greedy distance-first approach, using both randomly generated environments and a real-time map from an actual deployment site. The PETAL algorithm consistently performed better than baseline algorithms in simulations, demonstrating significant savings in energy usage and distance traveled. On a randomly generated map, the PETAL algorithm achieved 6.05% higher energy efficiency and 23.58% shorter travel distance than the baseline path-planning algorithm. It achieved 15.69% and 31.66% in energy efficiency and distance traveled saved on a real-time map, respectively. Such improvements can diminish operator exposure, extend mission durations, and foster safer, more efficient urban pest control.
ISSN:2227-7390
DOI:10.3390/math13060950
Fuente:Engineering Database