Multi-Agent Mission Planning With Localized Task Allocation

The patent segments the coordination problem by introducing a correlation order parameter k that limits optimization to small groups of agents rather than all agents globally. This segmentation transforms the intractable global optimization problem into manageable local optimization problems, where only agents within distance k of each other require detailed coordination, while distant agents use rougher approximation methods.

The patent applies partial action by implementing exact coordination optimization only for agent groups within correlation distance k, rather than for all agent pairs. This partial optimization approach achieves sufficient task allocation quality for locally critical coordination while avoiding the computational intractability of global exact optimization, balancing precision where needed with efficiency elsewhere.

The patent segments the computational workload by correlation order k, requiring intensive exact optimization only for small local groups within distance k, while using efficient approximation algorithms for the remaining agent allocations. This segmentation enables meeting real-time response requirements with manageable computational resource consumption on embedded platforms.

The patent changes the parameter of optimization scope from global (all agents) to local (agents within correlation order k), and adjusts the solution precision parameter based on distance. This parameter change allows the system to meet real-time constraints by reducing the computational burden while maintaining sufficient accuracy for mission-critical allocations.

The patent segments communication requirements by introducing correlation order k, where agents only need detailed coordination information with other agents within distance k. Agents beyond this distance require only rough approximation coordination, dramatically reducing communication overhead while maintaining system effectiveness for locally-critical missions.

The patent applies local quality by providing high-quality detailed coordination information only where physically necessary (within correlation order k), and rougher approximation information elsewhere. This local differentiation of communication quality reduces overall communication overhead while maintaining reliability for missions where coordination is physically critical.

The patent segments the coordination problem by correlation order k, allowing the system to scale to large numbers of agents without proportionally increasing coordination complexity. Each agent only needs to coordinate in detail with a limited local neighborhood, making the coordination complexity per agent constant rather than growing with total system size.

The patent applies partial action by implementing detailed coordination optimization only for a fraction of agent pairs (those within correlation distance k), while using efficient approximation for the remainder. This enables scalable deployment of large agent swarms with manageable per-agent computational and communication loads.