Research Progress and Prospect of Multi-robot Collaborative SLAM in Complex Agricultural Scenarios - MA Nan; CAO Shanshan; BAI Tao; KONG Fantao; SUN Wei (智慧农业)
[Significance]The rapid development of artificial intelligence and automation has greatly expanded the scope of agricultural automation, with applications such as precision farming using unmanned machinery, robotic grazing in outdoor environments, and automated harvesting by orchard-picking robots. Collaborative operations among multiple agricultural robots enhance production efficiency and reduce labor costs, driving the development of smart agriculture. Multi-robot simultaneous localization and mapping (SLAM) plays a pivotal role by ensuring accurate mapping and localization, which are essential for the effective management of unmanned farms. Compared to single-robot SLAM, multi-robot systems offer several advantages, including higher localization accuracy, larger sensing ranges, faster response times, and improved real-time performance. These capabilities are particularly valuable for completing complex tasks efficiently. However, deploying multi-robot SLAM in agricultural settings presents significant challenges. Dynamic environmental factors, such as crop growth, changing weather patterns, and livestock movement, increase system uncertainty. Additionally, agricultural terrains vary from open fields to irregular greenhouses, requiring robots to adjust their localization and path-planning strategies based on environmental conditions. Communication constraints, such as unstable signals or limited transmission range, further complicate coordination between robots. These combined challenges make it difficult to implement multi-robot SLAM effectively in agricultural environments. To unlock the full potential of multi-robot SLAM in agriculture, it is essential to develop optimized solutions that address the specific technical demands of these scenarios.[Progress]Existing review studies on multi-robot SLAM mainly focus on a general technological perspective, summarizing trends in the development of multi-robot SLAM, the advantages and limitations of algorithms, universally applicable conditions, and core issues of key technologies. However, there is a lack of analysis specifically addressing multi-robot SLAM under the characteristics of complex agricultural scenarios. This study focuses on the main features and applications of multi-robot SLAM in complex agricultural scenarios. The study analyzes the advantages and limitations of multi-robot SLAM, as well as its applicability and application scenarios in agriculture, focusing on four key components: multi-sensor data fusion, collaborative localization, collaborative map building, and loopback detection. From the perspective of collaborative operations in multi-robot SLAM, the study outlines the classification of SLAM frameworks, including three main collaborative types: centralized, distributed, and hybrid. Based on this, the study summarizes the advantages and limitations of mainstream multi-robot SLAM frameworks, along with typical scenarios in robotic agricultural operations where they are applicable. Additionally, it discusses key issues faced by multi-robot SLAM in complex agricultural scenarios, such as low accuracy in mapping and localization during multi-sensor fusion, restricted communication environments during multi-robot collaborative operations, and low accuracy in relative pose estimation between robots.[Conclusions and Prospects]To enhance the applicability and efficiency of multi-robot SLAM in complex agricultural scenarios, future research needs to focus on solving these critical technological issues. Firstly, the development of enhanced data fusion algorithms will facilitate improved integration of sensor information, leading to greater accuracy and robustness of the system. Secondly, the combination of deep learning and reinforcement learning techniques is expected to empower robots to better interpret environmental patterns, adapt to dynamic changes, and make more effective real-time decisions. Thirdly, large language models will enhance human-robot interaction by enabling natural language commands, improving collaborative operations. Finally, the integration of digital twin technology will support more intelligent path planning and decision-making processes, especially in unmanned farms and livestock management systems. The convergence of digital twin technology with SLAM is projected to yield innovative solutions for intelligent perception and is likely to play a transformative role in the realm of agricultural automation. This synergy is anticipated to revolutionize the approach to agricultural tasks, enhancing their efficiency and reducing the reliance on labor.