Mobile robots mainly need to solve problems such as positioning, planning, and control. At present, the key research areas include environmental perception and modeling, positioning and navigation, environmental understanding, and multi-robot coordination. In the future, mobile robots will develop towards the following trends:
01"Natural navigation + autonomous path planning" became the mainstream. The development of mobile robots has gone through different stages of track (such as tape pulling), beacon (such as QR code), and beacon-free (such as SLAM, instant positioning and map construction). SLAM technology can enable robots to achieve positioning and navigation without beacons. It has the characteristics of easy deployment and flexibility, and is more suitable for applications in scenarios with complex operating environments and frequent business changes. Therefore, it is favored by more and more customers. become the mainstream trend in the industry. Industry development shows that the development of navigation technology has gradually transitioned equipment from "cars" to "robots". With the development of new technologies, the degree of autonomy and intelligence of AGVs is getting higher and higher, and the evolution of AMR has widely expanded the application of the industry. At this stage, there is no navigation method that can "conquer the world", and the most suitable navigation method can only be selected according to the characteristics of the application. Different applications have different navigation requirements. Among various navigation methods, the most popular ones are natural navigation methods that do not depend on artificial environment, such as laser and vision. The diversity of applications determines the diversification of technological development directions. The standards for measuring the pros and cons of technologies vary according to application requirements. It is difficult to measure various technologies with a unified standard.
02 Deep learning will be widely used to enhance the robot's understanding of the surrounding environment. The application of deep learning technology in AI in computer vision mainly includes object recognition, target detection and tracking, semantic segmentation, instance segmentation, etc. Semantic SLAM can combine object recognition with Combined with visual SLAM, label information is introduced into the optimization process, and a map with object labels is constructed to realize the robot's understanding of the content of the surrounding environment. There are many limitations in traditional 2D obstacle detection. Through artificial intelligence semantic segmentation, the situation of people or obstacles can be more effectively judged, and the detour efficiency can be improved. The robot system can improve the application efficiency and intelligence level. The accelerated integration of new technologies and robotics will further promote the upgrading of products. The autonomy of mobile robots is mainly reflected in three aspects: "state perception", "real-time decision-making" and "accurate execution". The combination of next-generation information technologies such as the Internet of Things, AI, and 5G with robotics enables devices to interact efficiently, data flows more freely, and the hardware can be directed to maximize its effectiveness through algorithms.
03 Large-scale cluster operations have become inevitable, and more efficient multi-machine collaboration has become a trend. In practical applications, robots usually cooperate in clusters to complete specific tasks. Such as: pallet handling and collection of goods on the platform, storage and picking of raw material boxes, material handling between production lines; pallets can be transported by unmanned forklifts, and QR code-like KIVA robots can be used for storage and picking of raw materials. Inter-material handling can use SLAM robots.
Once the Ketai Swarm Pro group scheduling system reaches hundreds or even thousands of robots, simple logical thinking can no longer solve the problem, and the efficiency of the entire group collaboration cannot be effectively guaranteed. At this time, robots need to be able to continuously learn and revise their own strategies. AI will play an important role in it, allowing the entire system to be continuously optimized, and the level of group intelligence will become higher and higher. When the scale of the mobile robot system expands, the traditional management and scheduling system is facing more and more stringent requirements. The mobile robot management system needs to perform efficient traffic management and task scheduling for AMRs with obstacle avoidance and detour capabilities. The situation that heterogeneous mobile robot systems coexist in the same application site will increasingly appear. Some of the new mobile robot management systems will be distributed and deployed in the cloud, and have reliable redundancy; can support online map and strategy updates to adapt to changing operating routes and scheduling strategies; be able to move around with SLAM capability The robot performs optimal scheduling, efficiently and flexibly manages task allocation and traffic control in the system; through certain standardized means, it manages and controls the coordinated operation of heterogeneous robot systems on the same site.
04 Isomorphic simulation, digital twin, to provide customers with one-stop service. In the process of intelligent and automated transformation, customers will go through a long decision-making chain, from scheme conception to scheme design and actual investment. Usually, this decision-making process Relying on the experience of the designer, this may lead to a large deviation between the planning results and the actual requirements, resulting in waste or delays in the construction period. A full-featured isomorphic simulation system can avoid human bias in the design process, and can greatly improve evaluation efficiency; it can provide one-stop solutions for planning, simulation, implementation, operation, etc., to achieve isomorphic simulation and digital twin, Greatly reduce the risk of robot project planning and improve the efficiency of operation and maintenance.
05 Application scenarios will be further expanded On the basis of further development of technology, the application scenarios of mobile robots will be further expanded in the future, and will gradually penetrate into various fields and links of the manufacturing industry. With the further improvement of end customers' demand for intelligence, there will be fewer and fewer single AGV/AMR-based projects in the future. Therefore, different types of mobile robots and how to achieve coordinated operation between mobile robots and other automation equipment will become a problem. The key to testing the ability of enterprise program implementation. In addition, from indoor to outdoor, outdoor applications in semi-closed scenarios such as park logistics will also be one of the development directions of mobile robots. In addition to the above three views, the future industrial application of mobile robot technology will also accelerate the integration of artificial intelligence, mobile Internet, big data processing and other technologies to create new technologies, products and application models.
The C-Navi high-precision laser/laser SLAM navigation technology independently developed by Ketai Robot can achieve high-precision positioning and navigation with repeat positioning accuracy up to millimeters; distributed multi-machine group intelligence collaboration technology, powerful algorithms and hardware support, truly do To decentralization, it can realize autonomous decision-making, efficient path planning and traffic coordination of mobile robots, reduce the high computing power requirements of central computing nodes, and can coordinate more than 1,000 robots at the same time.
Ketai Swarm Pro group intelligent scheduling system is specially developed and designed for coordinating multi-AGV/AMR joint operations. It has strong compatibility and has functions such as intelligent task allocation, flexible scheduling, real-time interface display, traffic coordination, vehicle management, and shared resource management. The system is widely used in warehousing and logistics, automated production lines, heavy industry, and automobile manufacturing. It can intelligently coordinate multiple AGVs/AMRs to efficiently and flexibly complete automatic loading and unloading, automatic handling, automatic palletizing, and automatic warehouse entry and exit functions. Ketai continues to innovate and the industry-leading robot technology can better adapt to the future development of the industrial mobile robot industry.