I. The Development of Robots Towards Intelligence Robots are a product of multidisciplinary collaboration, integrating advanced theories and technologies from kinematics and dynamics, mechanical design and manufacturing, computer hardware and software, control and sensors, pattern recognition, and artificial intelligence. Simultaneously, they are a typical type of automated machine, an extension and development of specialized automated machines and numerically controlled machines. Currently, both social needs and technological progress are placing new demands on the intelligent development of robots. 1. The Expansion of Automation Applications Creates New Demands for Intelligent Robots and Systems. With the accelerating pace of social progress, the demand for automation is expanding from manufacturing to a wide range of fields, including engineering, society, and daily life. Automated machines or industrial robots that originally operated in structured factory environments were suitable for large-scale, less flexible, and less dynamic production environments, and did not require excessively high levels of intelligence. However, automated machines needed in a wider range of fields must meet the diverse needs of different unstructured environments, requiring comprehensive integration and autonomous capabilities, thus evolving into intelligent robots characterized by technological integration. 2. The interactive development of information technology and robotics has enhanced the high-tech content of robots. Information technology needs a carrier; the transformation of traditional industries and various sectors through informatization ultimately relies on automated machines to materialize information, and robots are one such carrier. On the other hand, the development of information technology, particularly advancements in high-performance computers, communication networks and electronic devices, pattern recognition and signal processing, and software, can further enhance the "intelligence" and "physical capabilities" of robots, creating conditions for their intelligent and diversified development. This interactive development between robotics and information technology is even more prominent today, driven by the rapid advancement of information technology. This has continuously improved the high-tech content of robots, keeping them at the forefront of high-tech research. 3. Conceptual and technological innovation surrounding robots is a crucial aspect of international scientific and technological competition. Robots, with their limitless potential, have always been a source of conceptual and technological innovation. Whether in space, underwater, disaster relief, service, medical, or entertainment fields, intelligent robots with corresponding functions can be envisioned as needed, and this potential is limitless, evolving from low to high. Currently, the concept of automation is rapidly expanding into a wide range of fields, and the development of information technology has greatly improved the intelligence of robots. This expansion of the imagination space has created both demand and the possibility of realization, further stimulating conceptual and technological innovation around robots, and containing the potential to generate various competitive core technologies. Therefore, it will inevitably become an important point of competition in international scientific and technological innovation. 4. Robots are a representative and vibrant highlight in the field of automation. Robots are a product of multidisciplinary integration, but as the application environment and tasks of robots become more complex, the difficulty and impact of information integration and processing in unstructured complex environments, and the planning and coordination for complex tasks, become prominent. This requires the use of theories, methods, and technologies such as information feedback, optimized control, and coordinated integration to solve these problems. The advantages of control science in system optimization and comprehensive integration will increasingly play a leading role in intelligent robots. As an automated system, intelligent robots are unparalleled by any other type of automated system in terms of the breadth and cutting-edge nature of their theories and technologies, their integration with various advanced information technologies, and the diversity of their physical implementation. Therefore, the representativeness and status of robots in automation science and technology will be further recognized as their application scope expands, the information technology they employ is updated, and their intelligence level improves. II. Intelligent Robot Collaborative Systems and Their Key Technologies In the development of robots towards intelligence, multi-robot collaborative systems represent a comprehensive technology integration platform. If the intelligence of a single robot merely makes an individual smarter, then a multi-robot collaborative system requires not only a group of intelligent robots but also their ability to cooperate effectively. Therefore, it reflects not only individual intelligence but also collective intelligence, representing an imagination and innovative exploration of human social production activities. Multi-robot collaborative systems have a wide range of applications, closely related to the expansion of automation into non-manufacturing fields. Due to the shift towards unstructured application environments, multi-mobile robot systems must be able to adapt to changes in tasks and environmental uncertainties, possessing a high degree of decision-making intelligence. Therefore, research on multi-mobile robot collaboration is no longer simply about control coordination but about the coordination and cooperation of the entire system. Here, the organization and control methods of multi-robot systems largely determine the effectiveness of the system. Multi-robot collaborative systems are also a paradigm for realizing distributed artificial intelligence. The core of distributed artificial intelligence is to divide the entire system into several intelligent, autonomous subsystems. These subsystems are physically and geographically dispersed, capable of independently executing tasks while exchanging information and coordinating with each other to complete the overall task. This is undoubtedly attractive for completing large-scale and complex tasks, and has thus quickly gained widespread attention in military, information, and other application fields. Multi-machine collaborative systems are a concrete implementation of this concept, where each robot can be regarded as an autonomous intelligent agent. Such multi-agent robotic systems (MARS) have now become a new research hotspot in robotics. Multi-mobile robot systems, due to their mobility, can complete complex tasks in unstructured environments and are the most typical and promising type of multi-robot collaborative systems, as well as the most widely studied type of system. The following will take multi-mobile robot systems as an example to introduce the main key technologies of intelligent robot collaborative systems: 1. System Architecture The system architecture is the logical and physical information and control relationships between robots in the system, as well as the distribution pattern of problem-solving capabilities. It is the basis of multi-mobile robot collaborative behavior. Generally, the system architecture of multi-mobile robot collaborative systems is divided into two types: centralized and distributed. Centralized architectures can be planned using a single master robot (Leader) that possesses all information about the system's activities. Distributed architectures, however, do not have such a robot; all robots are equal in relation to control. Although centralized architectures can achieve globally optimal solutions, distributed structures are generally preferred due to the influence of uncertainties. In recent years, to overcome the difficulty of modeling the environment in real-world scenarios and improve the robustness and operational capabilities of multi-mobile robot collaborative systems within distributed architectures, some researchers have adopted behavior-based reactive control systems. These systems establish cooperative behavior on a reactive model, accelerating the mobile robot's response to external stimuli, avoiding complex reasoning, and thus improving the system's real-time performance. 2. Perception: Perception is the foundation of intelligent robot action, encompassing "sensing" (sensing) and "knowing and understanding" (information fusion and utilization). The most important perception problem in mobile robots is localization and environmental modeling [7]. Although there are various localization methods such as odometry, vision-based landmark recognition, map matching-based global localization, gyroscope navigation, and GPS, only GPS can achieve practical global localization in unknown unstructured environments. However, GPS is limited by factors such as accuracy and safety. How to improve localization and environmental modeling capabilities by leveraging the cooperation between robots is an important aspect of studying the intelligence of multi-mobile robot systems. In recent years, various methods for synchronous processing of environmental map building and localization have been proposed [8]. Among them, environmental modeling and localization processes are mutually complementary, and they gradually become clearer in the process of mutual iteration, but often require harsh environmental conditions. In addition, many collaborative tasks only require relative position information between collaborators, such as formation and local collision avoidance. Therefore, sensor-based local localization has also attracted attention. Robots detect each other through sensors such as ultrasound, infrared, laser, or vision, and then information is fused through statistical, filtering, and other algorithms to obtain the relative position of each robot in the system. 3. Planning problems, primarily including task planning and path planning, have always been major research areas in artificial intelligence and robotics. Extensive and long-term research has been conducted on them, and the results have been applied to the planning problems of multi-robot collaborative systems. Corresponding to the system architecture, the planning of multi-mobile robot systems typically includes two approaches: centralized planning and distributed planning. Centralized planning generally yields efficient and globally optimal planning results, but it is mainly suitable for static environments and struggles to cope with environmental changes. In distributed planning, each robot plans its own actions based on its own environmental information. Its advantage is its adaptability to environmental changes, but its disadvantages include the inability to obtain a globally optimal solution and the potential for deadlock. 4. Learning and Evolution Learning and evolution are manifestations of the adaptability and flexibility of a system. At present, reinforcement learning and genetic programming are mainly used in collaborative robotics, and have been successfully applied in multi-robot handling systems and robot soccer [10][11]. At present, multi-robot learning and evolution are still at a relatively low behavioral level, and the tasks and environments of their learning and evolution are also very simple. When faced with more complex tasks and environments, there are time delay evaluation and combinatorial explosion problems. In addition, the distributed learning and evolution of multiple agents is significantly different from the traditional centralized learning and evolution methods, and more effective behavior optimization methods need to be found. 5. Coordination and Cooperation Strategies When multi-mobile robot systems cooperate to complete complex tasks, coordination between tasks, planning, and control of each robot is involved [12][13]. The study of multi-agent theory has provided ideas and strategies for these coordination behaviors. However, how to combine these abstract ideas and strategies into specific systems and realize them while also reflecting universality involves what tools to use to correctly describe the system behavior at each level. At present, the most typical descriptive tool at the task coordination level is the finite state machine (FSA) method in discrete event dynamic system theory. However, how to use hybrid system theory and methods to uniformly describe the behavior at different levels is still a hot research topic. In addition, multiple mobile robots running in the same environment often have conflicts when utilizing resources. Without appropriate coordination strategies, the system will not work properly. For foreseeable conflicts, planning can be used to avoid them. However, the situation when the system is running dynamically is often not accurately predicted in advance, and relying solely on planning methods to resolve conflicts will be very limited. The resolution of dynamic conflicts mainly includes the negotiation method, the convention method, and the familiar model method. Deadlock detection and resolution in dynamic environments is still a very challenging problem. 6. Research on multi-robot systems has been ongoing for nearly 20 years, with early work primarily focusing on system hardware and related individual technologies. However, with the gradual improvement of multi-mobile robot hardware systems, current software research has lagged significantly. Developed software is often tailored to specific hardware systems and single tasks, exhibiting low technical integration, poor versatility, and an inability to effectively leverage hardware capabilities. Therefore, there is an urgent need to develop a highly open, versatile, robot hardware-independent, and scalable system software platform to integrate existing fragmented technological achievements and provide standards for the design framework of system software. In the past three years, the United States and European countries have launched several large-scale projects for the development of collaborative multi-mobile robot system software, resulting in some representative software development platforms that have already been applied. 7. Experimental Research on Multi-Mobile Robot Collaborative Systems Experimental research initially began with computer simulation, using computer software to create a hypothetical robot swarm. This approach allows for more freedom in imposing ideal mechanisms on the robot entities, enabling them to interact in different ways. However, while this approach can examine the influence of many mathematical or biological principles on the collaborative behavior norms of robot swarms, it is difficult to directly apply it to constructing actual operational systems. In recent years, with the improvement of robot and component performance, research on real-world multi-robot systems has been increasing, gradually narrowing the gap between theoretical research and the actual environment and existing physical robots. Currently, most international robot fundamental research laboratories are conducting research simultaneously through both computer simulation and real-world experiments. III. Development Trends In summary, the main development trends in multi-mobile robot collaborative system research can be summarized as follows: (1) Distributed multi-agent architectures are gradually gaining dominance; (2) Research is shifting from hardware and single-technology studies towards establishing general-purpose software development platforms; (3) Behavior-based methods will be integrated with traditional planning and control methods; (4) Emphasis is placed on research into group collaboration theories, methods, and technologies for tasks with varying characteristics, unstructured environments, and localized perception conditions. (5) It requires high self-organization, self-learning, and adaptability; (6) While emphasizing autonomy, it is still necessary to continuously improve the coordination between humans and multiple robots; (7) In experimental research, it is important to emphasize the practical approach and develop practical technologies with high robustness and adaptability in real environments; (8) The needs of special applications, manufacturing, and service industries are becoming increasingly urgent. Research in this field in my country has already begun. With the support of the 863 Program, the National Natural Science Foundation of China, and other funding, after years of continuous research, a number of domestic units have achieved a high level of research in some areas, and the experimental research situation has also improved significantly. Preliminary results have been achieved in system architecture, coordinated control, and experimental research. After more than 20 years of development, research in the field of multi-mobile robot cooperative systems has achieved certain results, but has also encountered many difficulties. In particular, fundamental research in the areas of complex system control and distributed intelligence is significantly insufficient, lacking strong theoretical and technical support. Furthermore, most technologies have demanding environmental requirements, with experiments often conducted in artificial or semi-artificial environments. These factors limit the development of multi-mobile robot systems and their transformation into practical systems. In conclusion, given the unstructured and dynamic characteristics of the real world, highly adaptable, robust, and flexible cooperative theories, methods, and technologies will be the focus of future research.