Fuzzy-logic-based model predictive control: A paradigm integrating optimal and common-sense decision making

Exploring unknown environments and locating multiple targets with multi-robot teams remains challenging due to uncertainty about such environments and the high computational cost of existing planning methods. Model Predictive Control (MPC) is a widely used and effective approach for planning under constraints; however, traditional MPC relies on Bayesian representations and stochastic cost functions, which limit scalability and decision-making horizons in complex search scenarios. This paper introduces fuzzy-logic-based model predictive control (FLMPC), integrated with dynamic fuzzy maps of the environment, to emulate human-like reasoning and to simplify optimization, while preserving and leveraging the predictive structure of MPC and its systematic handling of constraints within the decision-making loop. Building on this foundation, we present a multi-robot exploration framework based on FLMPC for efficient target search in unknown environments. Instead of optimizing stochastic cost functions, FLMPC uses fuzzy abstractions of environmental attributes, such as passability and certainty, derived from probability distributions and local observations. This approach enables longer-horizon planning and efficient handling of multiple objectives. To enhance coordination among robots, FLMPC is extended into a bi-level parent–child architecture, where a high-level parent controller guides global exploration while local child controllers handle short-term planning. This structure not only improves coordination, but also increases robustness to environmental uncertainty thanks to combining long-term strategic decisions with reactive local adjustments that allow handling unexpected changes and environmental uncertainties more effectively. Extensive simulations in unknown 2D environments with randomly placed obstacles and human targets evaluate the proposed FLMPC framework embedded within a parent-child architecture against conventional MPC with stochastic cost functions. Results demonstrate up to 50× faster optimization and significantly improved search performance under environmental uncertainty, positioning FLMPC as a scalable and efficient planning method for large-scale search-and-rescue missions that require coordinated multi-robot exploration.