The Study on Real-Time RRT-Based Path Planning for UAVs Using a STM32 Microcontroller
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| Publicat a: | Electronics vol. 14, no. 24 (2025), p. 4901-4917 |
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| Altres autors: | , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14244901 |2 doi | |
| 035 | |a 3286276598 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Tsai Shang-En |u Department of Computer Science and Information Engineering, Chang Jung Christian University, Tainan City 711, Taiwan | |
| 245 | 1 | |a The Study on Real-Time RRT-Based Path Planning for UAVs Using a STM32 Microcontroller | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Real-time path planning for autonomous Unmanned Aerial Vehicles (UAVs) under strict hardware limitations remains a central challenge in embedded robotics. This study presents a refined Rapidly-Exploring Random Tree (RRT) algorithm implemented within an onboard embedded system based on a 32-bit STM32 microcontroller, demonstrating that real-time autonomous navigation can be achieved under low-power computation constraints. The proposed framework integrates a three-stage process—path pruning, Bézier curve smoothing, and iterative optimization—designed to minimize computational overhead while maintaining flight stability. By leveraging the STM32’s limited 72 MHz ARM Cortex-M3 core and 20 KB SRAM, the system performs all planning stages directly on the microcontroller without external computation. Experimental flight tests verify that the UAV can autonomously generate and follow smooth, collision-free trajectories across static obstacle fields with high tracking accuracy. The results confirm the feasibility of executing a full RRT-based planner on an STM32-class embedded platform, establishing a practical pathway for resource-efficient, onboard UAV autonomy. | |
| 653 | |a Microcontrollers | ||
| 653 | |a Robotics | ||
| 653 | |a Curves | ||
| 653 | |a Computation | ||
| 653 | |a Embedded systems | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Planning | ||
| 653 | |a Optimization | ||
| 653 | |a Altitude | ||
| 653 | |a Trees | ||
| 653 | |a Design | ||
| 653 | |a Autonomous navigation | ||
| 653 | |a Collision avoidance | ||
| 653 | |a Algorithms | ||
| 653 | |a Real time | ||
| 653 | |a Path planning | ||
| 653 | |a Efficiency | ||
| 653 | |a Flight tests | ||
| 653 | |a Autonomy | ||
| 700 | 1 | |a Shih-Ming, Yang |u Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan City 701, Taiwan | |
| 700 | 1 | |a Wei-Cheng, Sun |u Department of Computer Science and Information Engineering, Chang Jung Christian University, Tainan City 711, Taiwan | |
| 773 | 0 | |t Electronics |g vol. 14, no. 24 (2025), p. 4901-4917 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286276598/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286276598/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286276598/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |