# DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles

arXiv: https://arxiv.org/abs/2109.07024 · HTML: https://mlsanity.com/paper/2109.07024/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors](https://mlsanity.com/paper/2008.08835/) (2020)
- [FIESTA: Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots](https://mlsanity.com/paper/1903.02144/) (2019)
- [RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight](https://mlsanity.com/paper/2007.03465/) (2020)
- [Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap](https://mlsanity.com/paper/2010.07429/) (2020)
- [Search-based Motion Planning for Quadrotors using Linear Quadratic Minimum Time Control](https://mlsanity.com/paper/1709.05401/) (2017)
- [Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments](https://mlsanity.com/paper/2002.04920/) (2020)
- [A Real-Time Approach for Chance-Constrained Motion Planning with Dynamic Obstacles](https://mlsanity.com/paper/2001.08012/) (2020)

## Built on by (most cited first, 15 of 15)

- [Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents](https://mlsanity.com/paper/2505.23450/) (2025)
- [Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach](https://mlsanity.com/paper/2408.12129/) (2024)
- [Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization](https://mlsanity.com/paper/2209.07003/) (2022)
- [Recording Brain Activity While Listening to Music Using Wearable EEG Devices Combined with Bidirectional Long Short-Term Memory Networks](https://mlsanity.com/paper/2408.12124/) (2024)
- [EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition](https://mlsanity.com/paper/2410.09954/) (2024)
- [Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning](https://mlsanity.com/paper/2410.19769/) (2024)
- [RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics](https://mlsanity.com/paper/2308.15368/) (2023)
- [A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera](https://mlsanity.com/paper/2209.08258/) (2022)
- [A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles](https://mlsanity.com/paper/2301.08422/) (2023)
- [GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time Adolescent Sports Posture Correction](https://mlsanity.com/paper/2411.06725/) (2024)
- [Intelligent logistics management robot path planning algorithm integrating transformer and GCN network](https://mlsanity.com/paper/2501.02749/) (2025)
- [CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning](https://mlsanity.com/paper/2503.10045/) (2025)
- [MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic](https://mlsanity.com/paper/2507.00304/) (2025)
- [CORB-Planner: Corridor as Observations for RL Planning in High-Speed Flight](https://mlsanity.com/paper/2509.11240/) (2025)
- [Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter](https://mlsanity.com/paper/2607.23565/) (2026)

## Abstract (arXiv)

Safe UAV navigation is challenging due to the complex environment structures, dynamic obstacles, and uncertainties from measurement noises and unpredictable moving obstacle behaviors. Although plenty of recent works achieve safe navigation in complex static environments with sophisticated mapping algorithms, such as occupancy map and ESDF map, these methods cannot reliably handle dynamic environments due to the mapping limitation from moving obstacles. To address the limitation, this paper proposes a trajectory planning framework to achieve safe navigation considering complex static environments with dynamic obstacles. To reliably handle dynamic obstacles, we divide the environment representation into static mapping and dynamic object representation, which can be obtained from computer vision methods. Our framework first generates a static trajectory based on the proposed iterative corridor shrinking algorithm. Then, reactive chance-constrained model predictive control with temporal goal tracking is applied to avoid dynamic obstacles with uncertainties. The simulation results in various environments demonstrate the ability of our algorithm to navigate safely in complex static environments with dynamic obstacles.