Research progress of the obstacle detection and obstacle avoidance technology for agricultural robots

Obstacle detection and avoidance are critical technologies in the autonomous navigation of agricultural robots. The ability to accurately perceive and avoid obstacles ensures the safe operation of agricultural robots in complex environments, reduces labor costs, and improves production efficiency. This paper provides a comprehensive review of the applications of single-sensor technologies and multi-sensor fusion technologies in obstacle detection for agricultural robots from various perspectives. First, the paper reviews recent advancements in using single-sensor technologies for obstacle detection, including RGB cameras, panoramic cameras, stereo cameras, depth cameras, LiDAR, ultrasonic radar, and millimeter-wave radar. The technical advantages and limitations of each type of sensor are analyzed in detail. For example, RGB and depth cameras offer high spatial resolution and are effective in structured environments but face challenges in low-light or high-dust conditions. Similarly, LiDAR provides precise distance measurements and is highly reliable for obstacle detection but remains costly for large-scale deployment. Ultrasonic and millimeter-wave radars, known for their robustness in harsh conditions, are often limited by their resolution and detection range. These analyses highlight the trade-offs involved in adopting specific sensors for agricultural robotics. Second, the paper summarizes the research progress in multi-sensor fusion technologies for obstacle detection, such as the integration of visual sensors with LiDAR and the fusion of visual sensors with millimeter-wave radar. Detailed analyses are provided on the characteristics and advantages of these fusion techniques. For instance, combining vision-based sensors with LiDAR enhances obstacle detection accuracy by leveraging the complementary strengths of high-resolution imagery and precise distance measurements. Similarly, the fusion of millimeter-wave radar with vision-based systems enables reliable detection in adverse weather conditions, such as rain or fog, while mitigating the limitations of individual sensors. These fusion approaches are discussed in the context of their suitability for various agricultural scenarios, emphasizing their ability to enhance the robustness and reliability of obstacle detection systems. Additionally, the paper reviews the progress of obstacle avoidance technologies for agricultural robots. Agricultural robots are broadly categorized into three types: large-scale agricultural machinery, small agricultural robots, and agricultural drones. For each category, the paper systematically examines the recent advancements in obstacle avoidance technologies, focusing on their applications in complex agricultural environments. The discussion highlights the unique requirements and challenges faced by each type of robot. For instance, large-scale agricultural machinery requires highly reliable obstacle avoidance systems to navigate expansive fields efficiently, while small robots emphasize flexibility and precision in row crops. Agricultural unmanned aerial vehicle, operating in three-dimensional environments, face unique challenges in real-time obstacle detection and avoidance due to their high-speed motion and variable terrain. Finally, the paper summarizes the current state of research and identifies the key challenges in obstacle detection and avoidance technologies for agricultural robots. These include the need for improving the adaptability of sensors and algorithms to highly dynamic agricultural environments, reducing the cost and energy consumption of sensing systems, and enhancing the robustness of multi-sensor fusion approaches. Furthermore, this paper provides a forward-looking perspective on the development of these technologies, offering theoretical foundations and technical references to accelerate the advancement of autonomous navigation for agricultural robots. This comprehensive review aims to contribute to the field by identifying current limitations and exploring innovative solutions, ultimately facilitating the safe and efficient deployment of agricultural robots in diverse and challenging environments.