Development of a Decentralized Algorithm Using Interval Type 3—Fuzzy Logic for Task Allocation and Multi-Agent Path Finding

Coordinating robot swarms requires jointly solving the interdependent Multi-Robot Task Allocation (MRTA) and Multi-Agent Path Finding (MAPF) problems under strict time and communication constraints, yet most existing methods rely on centralized planning or expose agents’ exact positions. In this study, a fully decentralized algorithm is proposed in which each agent estimates the positions and intended plans of others from broadcast bid values rather than shared coordinates, anticipating conflicts at intersections before moving and dynamically altering its movement or task assignment when it predicts it cannot reach its task in time. The method combines the Priority Inheritance with Backtracking (PIBT) algorithm for collision-free navigation with a novel Interval Type-3 Fuzzy Logic (IT3FL) mechanism for conflict resolution and congestion-aware rerouting. The approach was evaluated across seven benchmark environments against the centralized methods Enhanced Conflict-Based Search (ECBS) and ECBS with Task Allocation (ECBS-TA) and the Consensus-Based Auction Algorithm (CBAA). It reduced path cost by up to 7.10% relative to ECBS in open environments, while centralized methods remained superior in complex corridor-based maps. In the most demanding constrained scenario, it reduced solution cost by up to 47.03% and improved task completion by 35% over CBAA, demonstrating a robust, scalable decentralized alternative.