Fast Dynamic P-RRT*-Based UAV Path Planning and Trajectory Tracking Control Under Dense Obstacles
Abstract
:1. Introduction
- (1)
- A fast bidirectional dynamic informed P-RRT* (BDIP-RRT*) algorithm is first developed for UAV in dense obstacle environments. Superior to the P-RRT* algorithm [14], the bidirectional dynamic informed structure is optimized in terms of sampling probability, sampling range, target bias, and a multi-tree structure to improve the quality and speed of initial path generation.
- (2)
- A hybrid optimized trajectory generator is proposed that significantly improves trajectory smoothness and efficiency by optimizing jerk and snap compared with the spline-based method in [30]. More specifically, an adaptive distance interpolation strategy is introduced to generate trajectory control points, further balancing control performance and trajectory deviation.
- (3)
- Two prescribed-time control laws are designed to ensure fast and accurate UAV position and attitude control. A segmented function is introduced in the prescribed-time control laws to suppress the growth of the scale function and solve the singularity-like problem [39].
2. Preliminaries and Problem Statement
3. Integrated Planning and Control Framework and Algorithm
3.1. Advanced Path Planning
3.1.1. P-RRT* and Bidirectional Dynamic Informed Structure
3.1.2. BDIP-RRT* with Greedy Algorithm
Algorithm 1 BDIP-RRT* with greedy (, ) |
|
3.2. Comprehensive Trajectory Optimization
3.2.1. Adaptive Distance Interpolation Strategy
3.2.2. Hybrid Optimization Trajectory Function
3.3. Prescribed-Time Trajectory Tracking Control
4. Numerical Simulation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
UAV | Unmanned Aerial Vehicle |
P-RRT* | Potential function-based Rapid-exploration Random Tree star |
BDIP-RRT* | Bidirectional Dynamic Informed P-RRT* |
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Algorithms | Length (m) | Time (s) |
---|---|---|
P-RRT* | 25.385 | 0.925 |
P-RRT* with greedy | 24.773 | 0.631 |
BDIP-RRT* | 24.943 | 0.410 |
BDIP-RRT* with greedy | 24.588 | 0.308 |
Algorithms | Length (m) | Time (s) |
---|---|---|
P-RRT* with greedy | 99.296 | 0.133 |
BDIP-RRT* with greedy | 96.596 | 0.049 |
Algorithm | Metrics | Length (m) | Time (s) |
---|---|---|---|
P-RRT* with greedy | Mean | 101.696 | 0.575 |
Standard deviation | 3.256 | 0.393 | |
BDIP-RRT* with greedy | Mean | 98.202 | 0.124 |
Standard deviation | 1.370 | 0.063 |
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Zhu, X.; Gao, Y.; Li, Y.; Li, B. Fast Dynamic P-RRT*-Based UAV Path Planning and Trajectory Tracking Control Under Dense Obstacles. Actuators 2025, 14, 211. https://doi.org/10.3390/act14050211
Zhu X, Gao Y, Li Y, Li B. Fast Dynamic P-RRT*-Based UAV Path Planning and Trajectory Tracking Control Under Dense Obstacles. Actuators. 2025; 14(5):211. https://doi.org/10.3390/act14050211
Chicago/Turabian StyleZhu, Xiangyu, Yufeng Gao, Yanyan Li, and Bo Li. 2025. "Fast Dynamic P-RRT*-Based UAV Path Planning and Trajectory Tracking Control Under Dense Obstacles" Actuators 14, no. 5: 211. https://doi.org/10.3390/act14050211
APA StyleZhu, X., Gao, Y., Li, Y., & Li, B. (2025). Fast Dynamic P-RRT*-Based UAV Path Planning and Trajectory Tracking Control Under Dense Obstacles. Actuators, 14(5), 211. https://doi.org/10.3390/act14050211