Autonomous Drone Swarm Mission Planning

This challenge focuses on developing an intelligent system for coordinating a drone swarm in dynamic, potentially contested environments. Participants will design and implement an AI-driven mission planning and execution framework that can adapt to changing objectives, environmental factors, and adversarial threats. The system should enable a swarm of simulated drones to perform tasks like reconnaissance, area denial, or collaborative target engagement, prioritizing mission success while maintaining swarm integrity and resilience against individual unit losses. Modern techniques like multi-agent reinforcement learning (MARL) for swarm control, coupled with large language models (LLMs) for high-level tactical reasoning and dynamic replanning, are central to this challenge. The solution will involve a centralized decision-making agent interacting with a decentralized swarm, demonstrating robust coordination and adaptive behavior in complex scenarios. The primary goal is to build a system that can take high-level mission objectives, translate them into actionable swarm behaviors, and dynamically adjust these plans based on real-time feedback from the simulated environment. This requires robust inter-agent communication, efficient resource allocation, and intelligent threat avoidance. Success will be measured by the swarm's ability to achieve mission objectives within specified constraints, demonstrating resilience to failures, and adaptively responding to unforeseen events.