Energy-optimal 3D UAV trajectory planning and multi-ant-colony cooperative coverage path planning

Abstract

Energy consumption, rather than path length, is the binding constraint for drones operating over dense urban terrain. This paper treats flight energy as the sum of three physically distinct terms: horizontal cruise, vertical climb, and attitude adjustment during turns. Three simplifying assumptions—constant cruise speed, negligible wind, and constant angular velocity in maneuvers—delimit where the model holds. On that foundation the paper develops an ant colony optimization variant whose heuristic scores the reciprocal of segment energy rather than distance, with Min-Max normalization across the factors and pheromone kept within bounds. Collision checking samples the segment interior at 5m intervals instead of testing endpoints only. For area coverage, the planning problem is split into a macro layer that sequences sub-regions and a micro layer that designs the scan pattern inside each one; transition costs between sub-regions are computed offline once and stored in a lookup table. Tests over Shenzhen Longhua District, partitioned into 30 sub-regions, cut total flight energy by 18.8 percent against baseline ACO while finishing 60.5 percent faster.