At present, multi-domain technologies such as voice interaction, navigation and positioning, motion control, background scheduling management, multi-sensor technology, and communication have become the key technologies for enterprises to seize the market share of service robots. These technologies are indispensable for the product to be commercially successful. In 2015, service robot companies rose like spring bamboo shoots. In the first half of 2016, the industry said that “service robots are experiencing capital wintersâ€. On May 31, 2016, the Ministry of Industry and Information Technology of China released an explanation that the Chinese service robot market began to grow rapidly. It is estimated that by 2020, the annual sales revenue of service robots is expected to exceed 30 billion yuan. The government’s authoritative report made the statement of “capital winter†not break. With 2015 as the demarcation point, many brands that entered the industry before 2015 have started the B and C round of financing, and some brands that entered the market after 2015 also completed the A round of financing. From the actual situation, the industry still It is a hot investment area. 2017 technical tool for service robots Service robots are actually the integration and implementation of a variety of technologies, including voice interaction, navigation and positioning, motion control, background scheduling management, multi-sensor technology, communications and other multi-domain technologies. These technologies are indispensable for the product to be commercially successful. Positioning and navigation technology: the radar SLAM, go further Eighty percent of the industry's leading service robotics companies use SLAM technology. Simply put, SLAM technology refers to the complete process of positioning, mapping, and path planning for robots in an unknown environment. With the use of Google's driverless cars, the radar SLAM algorithm based on laser radar technology has become a hot topic in the research field. According to reports, although the radar SLAM is relatively expensive, it is the most stable, reliable and high-performance SLAM method. Shenzhen Youdi Technology Co., Ltd., which just completed the A round of financing, uses the radar SLAM. This technology positioning accuracy is controlled within ±10mm, which ensures that the robot creates maps in a completely unknown environment, while positioning, navigating and autonomously planning routes according to the map. That is to say, after you have assigned the task, it can independently plan the route, return to the welcoming place after completing the task, without manual operation, and have the same effect as Google's driverless technology. SLAM technology has been widely used in emerging fields such as AR, robotics, and unmanned driving. Among them, radar SLAM has become the mainstream navigation method in the industry due to its good directivity and high focusing. Motion Control Technology: Rotating Robots More Meet Market Demand You must have seen interesting and fun biped robots. However, the fact is that such robots have poor stability, slow movement speed, and are pushed down. Wheeled and tracked service robots have better balance and are more stable during the movement. “Our robots use self-developed wheeled chassis, and the wheeled chassis has greater stability, reliability and durability.†The head of the company said in an interview, “Some robots can only walk on the ground. And our robot can climb the slope, as long as the slope does not exceed 15 degrees, the whole machine can bear 40kg." At present, the biped robot mainly has two motion control modes, motor and hydraulic. The former has a relatively simple structure, but the load capacity is limited; although the latter has a large load capacity, the structure is complicated. The motion control mode of the wheeled and crawler robot is mainly composed of two parts: vertical control and lateral control. The former adjusts the moving speed; the latter adjusts the moving track. In the process of movement and obstacle avoidance, wheeled and crawler robots can adopt different control strategies according to different speeds to maintain overall stability. From the current technical development and practicality, wheeled and crawler-type robots are clearly more in line with market demand. Multi-sensor fusion technology: the ultimate product differentiation The sensor is like the "five features" of the robot. The robot acquires external information through the sensor to meet the needs of detection and data acquisition. The system completes decision-making and responds quickly by synthesizing, complementing, correcting, and analyzing the resulting information. Future robots want to be more human-like, and multi-sensor fusion technology is critical. For example, the Japanese Pepper robot is equipped with a 3D sensor, 5 touch sensors, 2 gyroscopes, 2 sonic locators, 3 buffer sensors, and 6 laser sensors. Through this technology, Pepper can recognize people's expressions, moods, surroundings, and make a richer, more humane response based on people's emotions. At present, China's mainstream service robots are mainly equipped with infrared sensors, ultrasonic sensors, tactile sensors, and visual sensors. In fact, if the service robot wants to accomplish more and more complex tasks, more sensors are needed. The maturity of multi-sensor fusion technology will be directly reflected in the differentiated functions of service robots. Deep learning algorithm: an important breakthrough in machine learning “The most important breakthrough in machine learning is deep learning.†Li Kaifu said in a public speech recently that “deep learning, simple understanding, is to give very, very large neurons, with a lot of data to train. It can be in a certain field, in terms of identification, classification, or prediction, far more than any past algorithm." The so-called deep learning algorithm is that the robot imitates the human brain to construct a neural network, and interprets the data through information collection and modeling to achieve the function of machine learning. By analyzing and learning data, robots can understand human language, action, and respond more accurately. Google's Alpha dog is constantly playing chess with himself before "debut", learning the game, letting the system play self-game and quickly complete self-evolution. Traditional robots cannot understand semantics and environment, and the emergence of deep learning algorithms has changed this situation. The more complex data models are acquired, the more "smart" the robot is. It is no longer mechanically completing tasks, but rather "thinking" and "judgement" to imitate human beings to make corresponding moves. However, due to factors such as technical level, big data acquisition difficulties, and cloud computing efficiency, there are still some bottlenecks in deep learning algorithms. It may take a while to be widely used in service robots. Intelligent voice, communication technology and back-end management technologies will all be key technologies for companies to seize market share. Due to the length of the article, there is no longer a discussion here. According to reports, in December 2016, China Robot Industry Alliance will release three alliance standards and 17 robot industry alliance standards. In 2017, the two realistic factors of vertical application scenarios and industry standards are also the key points for service robot companies to consider. The choice of vertical application scene determines the field and direction of the robot's deep cultivation, and whether it can meet the standard specifications determines whether the robot can enter the market.
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