A Self-Supervised Learning Approach with Differentiable Optimization for UAV Trajectory Planning

2025-04-05

AI Summary

Trajectory planning for SWAP-constrained UAVs in 3D environments remains challenging: modular approaches suffer from local minima, while end-to-end methods exhibit strong data dependency, large Sim2Real gaps, and dynamic infeasibility. Method: We propose the first self-supervised framework jointly integrating deep perception and differentiable trajectory optimization. It leverages an unlabeled 3D cost map to guide planning and introduces a neural-network-driven time-allocation strategy, enabling fully end-to-end differentiable perception–planning training. Contribution/Results: The method balances generalizability with physical interpretability. Experiments demonstrate significant improvements over SOTA: 31.33% reduction in position tracking error and 49.37% decrease in control energy consumption. Robustness is validated across both simulation and real-world UAV platforms, confirming practical transferability and reliability.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• UAV path planning in 3D under SWAP constraints
• Overcoming latency and suboptimality in modular planning systems
• Bridging sim-to-real gaps in end-to-end learning approaches

Innovation

• Self-supervised learning for UAV trajectory planning
• Differentiable optimization integrates learning and physics
• Neural network-based time allocation enhances efficiency