无需人工标注，让无人机自主规划路径并实时优化轨迹。
A Self-Supervised Learning Approach with Differentiable Optimization for UAV Trajectory Planning
- 用自监督学习结合可微优化，实现端到端路径规划
- 实测位置追踪误差降低31.33%，控制能耗减少49.37%
- 适合对实时性与鲁棒性要求高的无人机应用
尽管无人机在多个领域广泛应用，但在三维环境中进行路径规划仍面临严峻挑战，尤其在尺寸、重量和功耗（SWAP）约束下。传统模块化规划系统因信息共享有限和陷入局部最优，常导致延迟和性能下降。端到端学习方法虽简化流程，但依赖大规模数据集，存在显著的仿真到现实差距，或缺乏动力学可行性。本文提出一种自监督无人机轨迹规划框架，将基于学习的深度感知与可微轨迹优化相结合。通过3D代价地图引导无人机行为，无需专家示范或人工标注。同时引入基于神经网络的时间分配策略，提升效率与最优性。该系统融合了强健的学习感知与可靠的物理优化，增强泛化能力与可解释性。仿真与真实环境实验验证了方法的有效性与鲁棒性，相较当前最优方法，位置追踪误差降低31.33%，控制努力减少49.37%。
原文摘要 · Abstract (English)
While Unmanned Aerial Vehicles (UAVs) have gained significant traction across various fields, path planning in 3D environments remains a critical challenge, particularly under size, weight, and power (SWAP) constraints. Traditional modular planning systems often introduce latency and suboptimal performance due to limited information sharing and local minima issues. End-to-end learning approaches streamline the pipeline by mapping sensory observations directly to actions but require large-scale datasets, face significant sim-to-real gaps, or lack dynamical feasibility. In this paper, we propose a self-supervised UAV trajectory planning pipeline that integrates a learning-based depth perception with differentiable trajectory optimization. A 3D cost map guides UAV behavior without expert demonstrations or human labels. Additionally, we incorporate a neural network-based time allocation strategy to improve the efficiency and optimality. The system thus combines robust learning-based perception with reliable physics-based optimization for improved generalizability and interpretability. Both simulation and real-world experiments validate our approach across various environments, demonstrating its effectiveness and robustness. Our method achieves a 31.33% improvement in position tracking error and 49.37% reduction in control effort compared to the state-of-the-art.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。