The system combines the Contract Net Protocol (CNP) for task allocation and Conflict-Based Search (CBS) for multi-agent path planning, ensuring efficient and collision-free navigation of multiple robots in a shared workspace.
Robots are simulated on a grid-based environment, where each agent is visualized as an orange circle moving across the grid to perform assigned pick-and-place tasks. The framework demonstrates distributed negotiation for task assignments (via CNP) and optimal, conflict-free motion planning (via CBS).

Contract Net Protocol (CNP):
A distributed task allocation protocol used in multi-agent systems. One agent (the manager) announces a task, other agents (contractors) bid for it, and the manager awards the contract to the most suitable bidder. It’s efficient for dynamic task assignment in environments like warehouses.

Conflict-Based Search (CBS):
A multi-agent pathfinding algorithm that plans collision-free paths for multiple agents. It starts with individual shortest paths and, when conflicts occur (like two agents trying to occupy the same space), it resolves them by branching and adding constraints, ensuring safe and optimal paths.

Key Features:
Task Allocation with CNP: Robots negotiate dynamically to accept or reject tasks based on availability and cost.
Collision-Free Path Planning with CBS: Ensures robots move optimally through the warehouse without conflicts.
Python-based Implementation: All modules, from communication protocols to path planning, are written in Python.
Grid-Based Visualization: Agents are displayed as orange circles moving across a 2D grid, enabling real-time observation of task execution and coordination.
Scalable Architecture: Supports extending the system to larger warehouse settings with multiple agents.

Applications:
Automated warehouse logistics.
Research in multi-agent systems, distributed AI, and robotics.
Testing frameworks for task allocation and cooperative path planning.