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Conference Aug 2026

Research on obstacle avoidance path planning based on improved artificial potential field method

To overcome the limitations of the traditional artificial potential field method, including local minima, unreachable targets, path oscillations, and insufficient consideration of road structure information, this paper proposes an improved potential field algorithm for path planning on structured roads. Based on the conventional attractive and obstacle repulsive forces, a novel road boundary repulsive potential field is introduced to constrain the lateral driving range of the vehicle. A forward auxiliary force is introduced to break the force balance and escape from local minima. A combined force-limiting and dynamic position update mechanism is designed to suppress trajectory mutations. A scenario with a length of 100 m and a width of 4 m containing 5 dynamic obstacles is constructed for verification. The results show that the improved algorithm enables the vehicle to reach the target point without stopping or reversing in a continuous obstacle scenario, with a target reach-ability rate of over 99% and a terminal error of less than 0.3 meters. The maximum lateral deviation of the planned trajectory is less than 0.5 m, the average curvature is less than 0.08 m⁻¹, and no boundary crossing or collision occurs throughout the entire process. The generated trajectory is smooth and fully compatible with the vehicle's kinematic characteristics.

Wenlai Cai, Li-Cheng Li, Ren-Qiang Li et al. · 1 citation
Conference Aug 2026

Design of an intelligent fire warning system based on STM32 microcontroller and edge AI

To address the problems of slow response, high false alarm rate, single detection dimension, and lack of intelligent linkage in traditional fire warning systems, this paper designs and implements an intelligent fire early warning system based on a dual STM32F103C8T6 master-slave architecture and a K230 edge computing unit. The system integrates smoke, temperature-humidity, and flame sensors along with the YOLO vision model. It adopts a weighted voting-based multi-source data fusion decision mechanism. Under interference conditions such as low illumination and occlusion, the system enhances robustness through image enhancement and a temporal voting mechanism. Data is wirelessly transmitted to the OneNET cloud platform and a WeChat mini-program via the ESP8266 module using the MQTT protocol. When a fire is detected, the system can automatically execute emergency responses including power cutoff, smoke exhaust fan activation, and water pump startup. Test results show that the system operates stably, responds quickly, achieves high recognition accuracy and low false alarm rate, making it suitable for early fire warning in homes and small spaces.

Bo Ye, Cheng Yang · 0 citations

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