Deep Learning for Optimization of Trajectories for Quadrotors

2023/09/26 by Yuwei Wu, Xiatao Sun, Wu, Yuwei +5 · 10 citations
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Maritime Navigation and Safety #Robotic Path Planning Algorithms #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2309.15191

openalex publication_date 2023/09/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a novel learning-based trajectory planning framework for quadrotors that combines model-based optimization techniques with deep learning. Specifically, we formulate the trajectory optimization problem as a quadratic programming (QP) problem with dynamic and collision-free constraints using piecewise trajectory segments through safe flight corridors [1]. We train neural networks to directly learn the time allocation for each segment to generate optimal smooth and fast trajectories. Furthermore, the constrained optimization problem is applied as a separate implicit layer for backpropagation in the network, for which the differential loss function can be obtained. We introduce an additional penalty function to penalize time allocations which result in solutions that violate the constraints to accelerate the training process and increase the success rate of the original optimization problem. To this end, we enable a flexible number of sequences of piece-wise trajectories by adding an extra end-of-sentence token during training. We illustrate the performance of the proposed method via extensive simulation and experimentation and show that it works in real time in diverse, cluttered environments.

Cited by

• Mesh RAG: Retrieval Augmentation for Autoregressive Mesh Generation
• Fine-tuning of Large Language Models for Domain-Specific Cybersecurity Knowledge
• Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigation
• Hybrid Diffusion Policies with Projective Geometric Algebra for Efficient Robot Manipulation Learning
• Sequence Modeling for Time-Optimal Quadrotor Trajectory Optimization with Sampling-based Robustness Analysis
• FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees
• Learning Smooth SE(3) Trajectories under Left-Invariant Riemannian Metrics
• YOPOv2-Tracker: An End-to-End Agile Tracking and Navigation Framework from Perception to Action
• PRISM-DP: Spatial Pose-based Observations for Diffusion-Policies via Segmentation, Mesh Generation, and Pose Tracking
• A Self-Supervised Learning Approach with Differentiable Optimization for UAV Trajectory Planning

Related

• Learning to Plan Maneuverable and Agile Flight Trajectory with Optimization Embedded Networks
• Sequence Modeling for Time-Optimal Quadrotor Trajectory Optimization with Sampling-based Robustness Analysis
• Autonomous Drone Racing with Deep Reinforcement Learning
• Accelerating Trajectory Generation for Quadrotors Using Transformers
• End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning