Agenticswarm improves multi drone teamwork with adaptable task planning
AgenticSwarm: Semantic Perception and Adaptive Task Allocation for Heterogeneous Multi-UAV Missions
Robotics
Summary
Coordinating many drones to work together in complicated areas is tough because they need to understand what they see and listen to human instructions, all while dealing with changing conditions like drone failures or low battery. The authors created AgenticSwarm, a tool that helps drones recognize objects and tasks using images and language, then smartly assign jobs considering obstacles and energy use. The system can adjust on the fly when problems happen, keeping track of what’s done and what still needs work. Tests show it works better than some existing methods by reducing repeated work and speeding up recovery after issues.
What this means in practice
- •For drone fleet operators: Improve coordination and task updates for fleets operating in dynamic outdoor environments with heterogeneous drones.
- •For indoor robotics teams: Enable adaptive mission execution for multiple aerial robots navigating indoor test environments using semantic scene understanding.
Authors
Muhammad Ahsan Mustafa, Yasheerah Yaqoot, Faryal Batool, Roohan Ahmed Khan, Valerii Serpiva, Dzmitry Tsetserukou
Abstract
Multi UAV missions in complex environments require the system to understand both the surrounding scene and the intent of a human operator while maintaining feasible task allocation as mission conditions change. This paper presents AgenticSwarm, an agentic framework for semantic perception and adaptive task allocation in heterogeneous multi UAV missions. An agent interprets aerial imagery and natural language instructions to construct a grounded mission representation that links perceived objects and regions with task requirements, capability constraints, and mission dependencies. This information augments a constrained task allocation process in which obstacle aware path feasibility, energy consumption, and protected return home requirements are incorporated before assignment. During execution, changes such as UAV failure, battery degradation, or task modification trigger residual mission reconstruction from the current system state, while completed work and reconnaissance progress are retained. AgenticSwarm is evaluated across five diverse Gazebo environments and an indoor real test environment, demonstrating its ability to connect semantic reasoning with constrained allocation and adaptive multi UAV mission execution. Compared with a Grounding DINO+SAM~2.1 perception baseline, the SAM3-based pipeline improves class-aware recall by 25.2 percentage points (pp) and semantic label accuracy by 29.5 pp. Ablating residual mission replanning increases mean repeated work from 0% to 61.7% and post-event recovery time by 58.6%, highlighting the contribution of adaptive replanning to mission execution.