Overview: This article provides a comprehensive review of the concept and characteristics of agricultural robots, examining the development status of agricultural robots in several countries. The goal is to provide insights for future research.
1. Definition of Agricultural Robots
Agricultural robots refer to autonomous equipment designed for agricultural applications, equipped with precise sensing, autonomous decision-making, intelligent control, and automatic execution capabilities. Their core structure consists of four main components: a precise information sensing system, decision-making and control system, operation execution mechanism, and an autonomous mobile platform—often referred to as the "eyes, brain, hands, and feet" of the robot.
- Precise Information Sensing: This component involves multimodal sensing systems based on vision, touch, hearing, and taste technologies to detect spatial environments, target positions, and shapes. Sensing and navigation technologies are central to this, utilizing a combination of sensors like cameras, LiDAR, ultrasound, and GPS to achieve high-precision environmental perception, trajectory planning, and obstacle detection.
- Autonomous Decision-Making and Intelligent Control: This system includes communication, decision-making, and learning components. Data transmission is conducted via multi-machine communication, multi-component communication, production information communication, and decision-making information communication. Technologies like knowledge reasoning, human-machine interaction, and machine learning (including deep learning and reinforcement learning) enable the robot to autonomously identify targets, predict production, make task decisions, and diagnose faults, adjusting its actions based on its experience and environmental feedback.
- Automatic Operation Execution: This section focuses on efficient and robust robotic drives and end-effectors. With technologies like arm design, operation planning, and flexible operation controls, combined with ground-based autonomous mobile platforms, agricultural robots achieve precise targeting, compliant operation, dynamic servo, and effective human-machine interaction. Software and control algorithms allow for multi-task and multi-machine/execution coordination during operations.
In practice, agricultural robots integrate with artificial intelligence, big data, cloud computing, and the Internet of Things (IoT) to form an agricultural robotics application system. These systems are already well-developed and applied in key processes such as fertilization, pest control, yield estimation, grafting, pruning, inspection, harvesting, and transportation in both field and facility planting. Additionally, they are used in livestock farming for feeding, inspection, disinfection, and milking.
2. Global Development of Agricultural Robots
2.1 The United States
In 2011, the National Science Foundation (NSF) of the United States, in collaboration with the Department of Defense (DoD) and the Defense Advanced Research Projects Agency (DARPA), launched the "National Robotics Initiative" (NRI). This initiative aimed to promote research in fundamental robotic science, technology, and systems integration. In 2017, the NRI 2.0 was introduced, investing $30 to $45 million annually to support the development of collaborative robots, focusing on fundamental science, methods, technology, and system integration to promote interactive collaboration across various robot types and scales. In May 2023, the White House released the third edition of the "National Artificial Intelligence R&D Strategic Plan," which identifies the development of more capable and reliable robots as one of its nine key strategies, emphasizing their application in agriculture.
2.2 The European Union
Since 2010, the European Union has steadily increased its investment in robotic research. Through the "Horizon 2020" program, the EU allocated over 700 million euros for robotics research and innovation. The AgROBOfood project, a dedicated agricultural robotics initiative under this program, focuses on developing highly adaptable agricultural robots based on regional production needs. The project also aims to strengthen research and business connections across different regions by establishing a sustainable network of digital innovation hubs. Other projects, such as ROBS4CROPS, CROPS, and TrimBot2020, have been launched to enhance productivity, competitiveness, and sustainable development in agriculture.
2.3 The United Kingdom
The UK has long been a leader in artificial intelligence and robotics, having started research in robotic technology in the 1990s. In 2013, the UK government established the Robotic and Autonomous Systems Interest Group (RAS-SIG) to develop strategic blueprints for robotics and optimize the allocation of resources within the field. In 2014, the UK Technology Strategy Board released the "Robotics and Autonomous Systems 2020 Strategy" (RAS2020), outlining the vision for agricultural robots. This vision focuses on seamless cooperation between robots, farms, and food factories, promoting autonomous collaboration between robots and humans across multiple modes of interaction.
2.4 Japan
Japan is renowned as a "robotics superpower," and in recent years, its need for robotic development and application has grown due to the challenges of an aging and shrinking population. In response, the Japanese government released the "Japan's Robot Strategy" in 2015, identifying agriculture, forestry, fisheries, and the food industry as one of the five key areas for robotic development. The strategy emphasizes expanding the use of robots in environmental sensing, cultivation, harvesting, and livestock wearable technologies, aiming to maintain production stability and reduce labor costs.
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