Integrated Multi-Drone Task Allocation, Sequencing, and Optimal Trajectory Generation in Obstacle-Rich 3D Environments

Abstract

Coordinating teams of aerial robots in cluttered three-dimensional (3D) environments requires a principled integration of discrete mission planning-deciding which robot serves which goals and in what order -- with continuous-time trajectory synthesis that enforces collision avoidance and dynamic feasibility. This paper introduces IMD-TAPP (Integrated Multi-Drone Task Allocation and Path Planning), an end-to-end framework that jointly addresses multi-goal allocation, tour sequencing, and safe trajectory generation for quadrotor teams operating in obstacle-rich spaces. IMD--TAPP first discretizes the workspace into a 3D navigation graph and computes obstacle-aware robot-to-goal and goal-to-goal travel costs via graph-search-based pathfinding. These costs are then embedded within an Injected Particle Swarm Optimization (IPSO) scheme, guided by multiple linear assignment, to efficiently explore coupled assignment/ordering alternatives and to minimize mission makespan. Finally, the resulting waypoint tours are transformed into time-parameterized minimum-snap trajectories through a generation-and-optimization routine equipped with iterative validation of obstacle clearance and inter-robot separation, triggering re-planning when safety margins are violated. Extensive MATLAB simulations across cluttered 3D scenarios demonstrate that IMD--TAPP consistently produces dynamically feasible, collision-free trajectories while achieving competitive completion times. In a representative case study with two drones serving multiple goals, the proposed approach attains a minimum mission time of 136~s while maintaining the required safety constraints throughout execution.

Figures (12)

• Figure 1: The IMD--TAPP system schematic. The framework takes as inputs the 3D environment with obstacles, robot start states $v_R^i$, and goal set $G$. The core processing stages include: (i) graph discretization and search to compute cost matrices $\mathbf{C}_{RG}$ and $\mathbf{C}_{GG}$, (ii) IPSO with MLA guidance to optimize assignment and visit sequences, and (iii) minimum-snap trajectory generation to produce time-parameterized trajectories. The final stage validates obstacle clearance and inter-robot separation ($\geq 2R_r\phi$) through simulation rollout, outputting collision-free multi-drone trajectories.
• Figure 2: Illustrative IMD--TAPP scenario: two drones must cooperatively visit seven goals in a 3D environment with obstacles, with each goal visited exactly once and each drone returning to its start location.
• Figure 3: High-level overview of the IMD--TAPP pipeline: (i) 3D workspace discretization and graph-search computation of obstacle-aware travel costs,(ii) discrete optimization of goal assignment and visit sequencing using an injected (PSO) algorithm guided by multiple linear assignment, and (iii) smooth trajectory generation followed by safety validation with iterative replanning when necessary.
• Figure 4: 3D discretization and obstacle-aware travel-cost computation. (a) The workspace is discretized into a voxel graph with free-space nodes; obstacles block direct paths, requiring graph search to find collision-free routes between robot starts $v_R^i$, goals $g_p$ and $g_q$. (b) Graph-search distances populate the robots-to-goals matrix $\mathbf{C}_{RG}$ (with entries $C(v_R^i, g_j)$) and the goals-to-goals matrix $\mathbf{C}_{GG}$ (with entries $C(g_p, g_q)$), which are then used by the discrete optimizer to determine assignments and visit sequences.
• Figure 5: Cost-matrix construction and solution encoding for the discrete optimization stage. Graph search produces robots-to-goals and goals-to-goals travel-cost matrices, which are evaluated through a PSO particle representation encoding a goal-visit permutation and breakpoints that partition the ordered goals among drones while enforcing the visit-once constraint.