Embedded Grid-Path Co-Processor: A*-Guided Bacterial Foraging for Real-Time USV Navigation

• Monisha Rengaraj

  Author

In this paper, an approach to global path planning for unmanned surface vehicles is recast as a problem closer to embedded systems rather than that of artificial intelligence (AI) by the integration of a chemotaxis operator with an A*‑like heuristic combined with bacterial foraging. The resulting global path planner enables deterministic low-latency navigation on resource-constrained controllers. A model of the grid-partitioned environment makes bounded-memory representations feasible, while its fixed-cost neighbour evaluation assists in timing budgets used in the common case. Disruptive tumbling is avoided via a continuity checker that only invokes when a tumble will break path connectivity. Paths are represented as variable-length sequences that are mapped to compact data structures for the on-board execution. The algorithm parameters are treated as design-space knobs that co-optimise the convergence as well as the trajectory length and the iteration counts with due regard to compute and memory constraints. According to the Morris method, reproduction and elimination–dispersal are the most influential factors embedded in the scheduling. Simulation studies with five grid sizes show this approach produces shorter paths when compared with GA and ACO with fewer iterations. Also, with growing map sizes, the number of iterations becomes predictable for embedded deployments. The architecture thus obtained is a ready-to-implement template for a USV controller: a FIFO-friendly neighbourhood scan, a continuity-aware tumbling and event-triggered A* repairs that conform to standard microcontroller or SoC constraints.

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