Precautions for autonomous robot detection

You can use a variety of different types of sensors to solve the challenges accompanied by autonomous robots. The two types of sensors are introduced in detail below:

Visual sensor. Visual sensors can effectively simulate human vision and perception. The visual system can cope with challenges such as positioning, obstacle detection and collision, because they have high -resolution space coverage capabilities and can detect and classify objects. Compared with sensors such as lidar, visual sensors are also more cost -effective, but visual sensors are computational dense sensors.

The central processor (CPU) and graphics processor (GPU) with high power consumption may bring challenges to the robot system with limited power consumption. When designing an energy -saving robot system, the CPU or GPU processing should be as little as possible. The on -chip system (SOC) in the high -efficiency visual system shall process the visual signal chain at high speed, low power and low system cost. SOCs for visual processing must be intelligent, safe and energy -saving. The TDA4 processor series is highly integrated and adopts heterogeneous architecture design. It aims to provide computer visual performance, deep learning processing, stereo visual function and video analysis with as low as possible power consumption.

TI millimeter wave radar. The use of TI millimeter wave radar in robotics is a relatively novel concept, but the concept of autonomy with TI millimeter wave sensing has been used for some time. In automotive applications, the TI millimeter wave radar is a key component in the high -end driving auxiliary system (ADAS) to monitor the surrounding environment of the vehicle. You can apply some similar ADAS concepts (such as surrounding monitoring or collision) in the field of robotics.

From the perspective of sensing technology, the TI millimeter wave radar has a certain uniqueness, because such sensors can provide the distance, speed, and arrival angle information of the object, which can better guide the robot navigation to avoid impact. Based on radar sensor data, the robot can decide whether to continue safely, or slow down or even stop according to the location, speed and trajectory of the close or object.

Use sensor fusion and edge AI to solve the complex problems of autonomous robots

For more complex applications, any type of single sensor may not be enough to achieve autonomy. In the end, multiple sensors such as cameras or radar should be used in the same system to complement each other. Through the fusion of the sensor, the data of different types of sensors in the processor can help cope with some more complex autonomous robot challenges.

Sensor fusion helps to make the robot more accurate, and the use of marginal artificial intelligence (AI) can make the robot intelligent. Incorporating AI into the robot system can help the robot in intelligence, make decisions and execute operations. Robots with marginal AI can intelligently detect objects and positions, classify objects and take corresponding operations. For example, when the robot navigates in a messy warehouse, the edge AI can help the robot to infer what kinds of objects (including personnel, boxes, machines, and even other robots) on the path, and decide that the appropriate operation of navigation around these objects is decided Essence

When designing the AI robot system, both hardware and software have some designs to consider matters. The TDA4 processor series has a hardware accelerator suitable for edge AI functions, which can help real -time processing and calculating dense tasks. Able to access the marginal AI software development environment that is easy to use will help simplify and accelerate application development and hardware deployment.