Mobile robots mainly need to solve positioning, planning, control and other problems, the current focus of research areas include environmental perception and modeling, positioning and navigation, environmental understanding, multi-robot coordination, etc. In the future, mobile robots will develop towards the following trends:

S20360-SRS 1 “Natural navigation + autonomous path planning” becomes the mainstream

The development of mobile robots has experienced different stages of track (such as magnetic tape traction), beacon (such as two-dimensional code), and non-beacon (such as SLAM, instant positioning and map construction). SLAM technology can enable robots to achieve positioning and navigation without beacons, with easy deployment, flexibility and other characteristics, more suitable for applications in complex operating environment, business often changes in the scene, so it is favored by more and more customers, is S20360-SRS  becoming the mainstream trend in the industry.

Industry development shows that the development of navigation technology makes the equipment gradually transition from “car” to “robot”. With the development of new technologies, the degree of AGV autonomy and intelligence is getting higher and higher, and the evolution of AMR has widely expanded the application of the industry.

S20360-SRS

At this stage, there is not any kind of “all-inclusive” navigation way, only according to the characteristics of the application to choose the most suitable navigation way, different applications for navigation requirements are not the same. Among all kinds of navigation methods, the most popular ones are natural navigation methods such as laser and vision that do not rely on artificial environment.

The diversity of application determines the diversification of the development direction of technology, and the standards for measuring the advantages and disadvantages of technology vary according to different application needs, so it is difficult to use a unified standard to measure S20360-SRS various different technologies.

2 Deep learning will be widely used to enhance robots’ 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 and visual SLAM, introduce label information into the optimization process, build a map with object labels, and realize the understanding of the surrounding environment content of the robot.

Traditional 2D obstacle detection has many limitations. Semantic segmentation by artificial intelligence can more effectively S20360-SRS  judge the situation of people or obstacles and improve the efficiency of circumnavigation. 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 robot is mainly reflected in the three aspects of “state perception”, “real-time decision” and “accurate execution”. The combination of new generation information technologies such as the Internet of Things, AI, and 5G with robotics allows devices to interact efficiently, data to flow more freely, and algorithms to direct hardware to maximize performance.