Collaborative Optimization of Low-Altitude Rescue Flight Missions and Trajectories for Forest Fires

Aiming at the problems of insufficient coordination between task scheduling and flight trajectory planning, as well as low operational efficiency, in general aviation aircraft rescue operations for forest fire scenarios, this paper proposes a multi-aircraft task scheduling and flight trajectory planning method integrating an improved Contract Net Protocol (CNP) and Ant Colony Optimization (ACO). Firstly, a dynamically updated acquaintance library mechanism is constructed to pre-screen bidding nodes based on aircraft attributes and task capabilities, reducing negotiation overhead. By introducing a task grid importance quantification model, the fire area is mapped into importance weights to guide scheduling priority decisions. Additionally, a public message blackboard mechanism is designed to enable rescue members to actively perceive global task states, breaking through the limitations of passive response. Secondly, a contract net-ant colony fusion algorithm is proposed. Specifically, after the contract net protocol is used to allocate firefighting task grids, ant colony optimization is employed to plan multi-aircraft trajectories. By incorporating improved state transition probabilities and pheromone update strategies, the algorithm generates optimal paths that minimize the total flight distance. Finally, a real wildfire scenario in western Sichuan is used for simulation validation, where the proposed improved contract net protocol-based task allocation and trajectory planning algorithm is compared against the Consensus-Based Bundle Algorithm (CBBA) and the Coevolutionary Backtracking Genetic Algorithm (CBGA). Experimental results demonstrate the effectiveness of the improved algorithm: the task grid assignment success rate of the optimized scheme reaches 99.0%, which is 14.75% and 13.25% higher than that of CBBA and CBGA, respectively. The average generation time of the scheduling scheme is reduced by 12.40% compared to CBBA and 15.40% compared to CBGA. The proposed algorithm effectively solves the coupling problem between task scheduling and trajectory planning, improving the overall efficiency of multi-aircraft rescue task scheduling and resource utilization in forest fires, and thus has important practical significance.