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

Prithvi Krishna Chittoor (), Bhanu Priya Dandumahanti, Abishegan M., Sriniketh Konduri, S. M. Bhagya P. Samarakoon and Mohan Rajesh Elara
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Prithvi Krishna Chittoor: Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
Bhanu Priya Dandumahanti: Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
Abishegan M.: Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
Sriniketh Konduri: Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
S. M. Bhagya P. Samarakoon: Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
Mohan Rajesh Elara: Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore

Mathematics, 2025, vol. 13, issue 6, 1-26

Abstract: 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.

Keywords: A*; Dijkstra; fumigation; greedy distance first; path planning; pest control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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