AI 中文总结
本文提出GPU并行伊辛求解器框架，将智慧农业无人机路径规划问题转化为QUBO模型，仿真显示其性能优于遗传算法和模拟退火，可作为大规模智慧农业的稳健实时解决方案。
AI 中文摘要
传统路径规划方法常受限于局部最优、有限的可扩展性和缓慢的收敛速度，这极大限制了它们在解决大规模问题时的有效性。为应对这些局限，本文将问题求解范式从算法优化转向计算架构的并行化，提出一种利用图形处理器（GPU）并行伊辛求解器的新型优化框架。该方法在GPU硬件上模拟量子退火的运行原理，可快速搜索伊辛模型的最低能量状态。与常受限于量子比特数量的物理量子设备不同，本方法借助Fixstars Amplify（FA）平台在高度并行化的GPU上执行并行退火，可同时评估数千种潜在路径候选和海量状态转换。凭借大规模并行处理，该框架的核心优势在于即使问题规模增大，仍能最小化计算时间。此外，为在FA平台上求解路径规划问题，本文将该问题形式化为二次无约束二进制优化（QUBO）模型，此形式化将飞行约束和运行时间最小化的目标转化为能量状态，从而可通过基于伊辛的架构处理该问题。仿真结果表明，与遗传算法和模拟退火方法相比，本文提出的方法能持续识别更优的飞行路径，同时保持稳定的计算性能。这些发现凸显了其作为下一代大规模智慧农业的稳健、可扩展的实时解决方案的潜力。
英文摘要
Traditional path planning methods are often constrained by local optima, limited scalability, and slow convergence, which significantly restrict their effectiveness in solving large-scale problems. To address these limitations, this paper shifts the problem-solving paradigm from algorithmic refinement to parallelization of computational architecture. We propose a novel optimization framework utilizing a Graphics Processing Unit (GPU)-parallelized Ising solver. Our method mimics the operational principles of quantum annealing on GPU hardware, enabling rapid search for the minimum-energy state of Ising models. Unlike physical quantum devices, which are often constrained by the number of qubits, our approach leverages the Fixstars Amplify (FA) platform to perform parallel annealing on highly parallelized GPUs. This enables the simultaneous evaluation of thousands of potential path candidates and vast state transitions. By leveraging large-scale parallel processing, the core strength of this framework lies in minimizing computation time even as the problem scale increases. Furthermore, to solve the path planning problem using the FA platform, we formulate the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model. This formulation converts the objectives of flight constraints and operational time minimization into an energy state, enabling problem processing via the Ising-based architecture. Simulation results demonstrate that our proposed method consistently identifies superior flight paths while maintaining stable computational performance compared with the genetic algorithm and simulated annealing method. These findings highlight its potential as a robust, scalable real- time solution for next-generation large-scale smart agriculture.
Comments6 pages, 3 figures, conference
