NeuralPathLite: Fast and Robust Diffusion-Based Path Planning for Autonomous Navigation
The deployment of Artificial Intelligence (AI)-based autonomous mobile robots in high-stakes environments, such as defense swarms and large-scale warehouse logistics, demands path planners that are not only theoretically optimal but scalable. While traditional search-based methods like A* guarantee resolution optimality, they suffer from exponential complexity in high-resolution maps. Conversely, sampling-based algorithms like Rapidly-exploring Random Tree* (RRT*) efficiently explore large spaces but lack path smoothness. State-of-the-art diffusion planners predominantly rely on computationally intensive backbones, rendering them unsuitable for real-time deployment where bandwidth and compute are constrained. To bridge this gap, we introduce NeuralPathLite, a lightweight neural-network-based, image-conditioned diffusion planner specifically optimized for embedded and centralized swarm architectures. Unlike prior works that struggle with visual ambiguity, we introduce a Semantic Visual Anchoring mechanism that explicitly disentangles start-goal semantics from obstacle features, preventing mode collapse in multi-colored, cluttered environments. Furthermore, we demonstrate a novel Secure Copy Protocol (SCP)-based deployment framework, exploiting our model’s low latency to enable centralized path generation for distributed robot swarms. Evaluated on the Mobile Robot Local Planning Benchmark (MRPB) and deployed on a Husky A200, NeuralPathLite achieves an inference latency of 0.16 seconds, a $7.4\times $ speedup over competing state-of-the-art methods with a 96.12% success rate in out-of-distribution scenarios. This combination of algorithmic efficiency and robust visual conditioning establishes NeuralPathLite as a viable backbone for AI-based robotic automation. The source code and dataset will be publicly available. Note to Practitioners—This paper addresses the critical challenge of deploying high-performance path planning in real-time robotic systems where computational resources are limited or environment complexity is high. While modern learning-based planners offer sophisticated results, their high inference time often makes them impractical for real-world hardware. We introduce NeuralPathLite, a fast and robust planning method designed to bridge this gap by providing high-quality, smooth paths with minimal computational overhead. Beyond the algorithm, we present a scalable Server-Client Protocol (SCP) framework specifically engineered for high-risk or large-scale deployments, such as search and rescue in hazardous zones, warehouse automation, and monitoring of life-critical environments where human access is restricted. This architecture allows for the centralized management of multiple robots using SCP to offload heavy generative compute, effectively reducing the hardware cost per unit by enabling the use of low-power AI agents without dedicated high-end Graphics Processing Units (GPUs). The system has been validated using the Husky A200 differential drive robot, demonstrating its readiness for field applications. For practitioners, this work offers a ready-to-implement strategy for managing fleets of autonomous robots economically.