Autonomous drone fleets have immense potential in medical supply delivery during disaster incident response. However, coordinating multiple drones in such settings introduces compounding challenges: dynamic environmental hazards such as wind, obstacles, and intermittent network connectivity, constrained energy budgets, and the need to serve patient locations fairly under deadlines and triage-based priority while optimizing schedule utilization. In this paper, we present CEDA, a novel CTDE Deep Q-Network algorithm for cooperative multi-drone medical delivery, designed to jointly optimize triage-priority-aware routing, multi-agent coordination, and energy-efficient navigation under dynamic uncertainty. CEDA introduces a Priority-Preserving Fair Scheduling strategy, in which a structured reward function encodes both triage weights and complementary fairness mechanisms ensuring no patient class is starved of service. We evaluate CEDA in a simulated grid environment featuring dynamic hazard zones, stochastic action failures, and dynamically spawning patients across three triage priority levels, as well as in a PX4 SITL validation using two X500 quadrotors controlled via MAVSDK in offboard position mode. Simulation results demonstrate that CEDA achieves a delivery completion rate above 85%, reduces obstacle collisions by over 90% across training, and delivers an average of 6 patients per episode with a triage efficiency of 0.82. CEDA preserves clinical priority ordering, Critical patients are served first, while achieving near-zero mortality across lower-triage classes, confirming that priority-weighted routing does not condemn Stable or Urgent patients to neglect. PX4 SITL validation further demonstrates that the learned policy remains executable and triage-coherent under practical communication constraints and realistic multi-drone coordination in disaster response settings.
Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication, and scalable control. Truck-drone collaboration has emerged as a networked cyber-physical delivery paradigm that combines the payload capacity and range efficiency of trucks with the agility of drones in congested or access-limited urban environments. This paper proposes a layered planning and coordination framework that structures truck-drone collaborative delivery (TDCD) from a systems and control perspective. The framework consists of five interrelated layers: spatial-demand alignment, collaborative delivery configuration, resource and workflow orchestration, performance evaluation, and scalability analysis, providing a unified view of coordination, control, and system-level performance in networked delivery operations. The proposed framework is evaluated using a realistic urban last-mile delivery scenario derived from the 2021 Amazon Last Mile Routing Research Challenge dataset. The case study demonstrates how coordinated truck-drone operation, enabled by structured task orchestration and inter-agent synchronization, improves end-to-end system efficiency under operational constraints. Results show a 42.4% reduction in total delivery time and a 44.2% reduction in energy consumption compared to a conventional truck-only delivery model. The scalability analysis further highlights how coordination gains persist as system size increases, and shows the importance of efficient control and communication in heterogeneous delivery networks.
Autonomous multi-drone navigation requires fleets to service distributed objectives in cluttered environments under tight computational budgets. Efficient coordination depends on task bundling, where each drone visits multiple objectives along its route. Separate solvers for cost estimation, assignment, and execution incur redundant graph search and produce long, abrupt paths. We propose FlockDiffusion, a learned framework combining a scene graph encoder, an explicit allocation head, an assignment conditioned diffusion transformer, and a closed form trajectory decoder. An autoregressive teacher provides offline supervision for parallel fleet trajectory generation. PyBullet ablations show that bundling increases task completion from 50% to 100%, while our complete teacher further reduces route cost by 8.4% relative to MAGNNET with bundling. In the optimized scalability benchmark, evaluated on 100 scenes per density with ten drones, FlockDiffusion achieves 6.2 to 7.6 times faster inference and approximately 37% shorter routes than the classical pipeline. As nominal task counts increase from 20 to 40, latency rises from 7.8 to 11.1 ms, compared with 48.0 to 75.8 ms for the baseline. In a separate evaluation across five Gazebo environments, FlockDiffusion achieves 100% planner coverage and reduces planned route cost by 15.4% relative to the baseline with bundling. These results demonstrate efficient planning under increasing task density in configurations that are demanding to reproduce with physical drone fleets.
Multi-Unmanned Aerial Vehicle (UAV) disaster-response systems require coordinated task assignment and local trajectory control, yet the individual and combined contributions of these coordination layers to mission efficiency and operational safety remain insufficiently characterised under controlled experimental conditions. TriSAR is evaluated as a five-UAV coordination system operating in a physics-based Gazebo simulation of an earthquake-damaged urban environment. A 2 x 2 factorial design compares two task-allocation strategies (Genetic Algorithm and greedy fitness-based allocation) with reactive collision avoidance enabled or disabled. Each of the four configurations was evaluated over 30 stochastic episodes in a common scenario of five UAVs and eight targets. Under greedy allocation, enabling repulsion eliminated recorded collision-threshold violations, confirmed by a Mann-Whitney test (U = 885, p = 4.03 x 10^-12, rank-biserial r = 0.97). Under GA allocation, the same protective effect was confirmed (U = 675, p = 1.26 x 10^-5, rank-biserial r = 0.50). For mission-efficiency metrics, GA-based allocation showed no statistically detectable advantage over greedy allocation when repulsion was enabled, but a significant advantage in steps, path length, and energy when repulsion was disabled (Welch's t-tests, |g| between 0.92 and 1.76). These results show that reactive repulsion provides a substantial, allocation-dependent safety benefit, while the additional computational complexity of GA-based task allocation yields a detectable mission-efficiency benefit only when repulsion is disabled.
Aditya Anil Kapile, Pedro Machado, Isibor Kennedy Ihianle