How can sensors and processors be used to build smarter, more autonomous robots

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How can sensors and processors be used to build smarter, more autonomous robots

June 09 , 2023 0

In designs based on energy efficient robotic systems, CPU or GPU processing should be as minimal as possible. From the perspective of sensing technology, TI millimeter wave radar is unique because this sensor can provide the distance between objects based on radar sensor data, and the robot can decide whether to continue driving safely, slow down or even terminate based on the location, speed and trajectory of people and objects near the robot.

An autonomous robot is an intelligent machine that understands its environment and navigates from it without human intervention or intervention. Although autonomous robot technology is relatively new, it is already on the construction site. Warehouse. It is widely used in urban and household fields. For example, standalone robots are good for transporting goods around warehouses, or implementing last-mile deliveries, while other types of robots are good for dusting homes or mowing lawns.

To achieve autonomy, robots need to be able to sense and locate themselves in a mapped environment, dynamically detect obstacles around them, track those obstacles, plan a path to a specific destination, and control the vehicle to follow the road. In addition, the robot must perform these tasks only when it is safe to do so to prevent personal injury. The asset or system itself creates risk. As people interact with robots more frequently, they must not only have autonomy, but autonomy, mobility and energy savings must also meet functional safety regulations.

Precautions for autonomous robot detection

A variety of different types of sensors can be used to deal with the challenges posed by autonomous robots. There are two types of sensors:

1. Vision sensor. Vision sensors can effectively simulate human vision and intuition. The visual system can handle positioning. Challenges such as obstacle detection and collision prevention because of its high-resolution spatial coverage capabilities and the ability to inspect and classify objects. Vision sensors are also cost-effective compared to sensors such as lidar, but vision sensors are intensive sensors.

2. Power-intensive central processing unit (CPU) and graphics processing unit (GPU) It may pose challenges for limited power consumption robot systems. In designs based on energy efficient robotic systems, CPU or GPU processing should be as minimal as possible. The motion picture system (SoC) in an efficient vision system should operate at a high rate. Solve low power consumption and low system cost of visual signal chain. SoC for vision processing must be smart. Safety and energy saving. The TDA4Cpu family is highly integrated with a selection of heterogeneous architectural modes designed to provide computer vision features with the lowest power consumption possible. Deep learning solutions. 3D visual representation and video analysis.

3.TI millimeter Wave radar. TI millimeter wave radar applied to robots is a relatively new concept, but with TI applied for some time, millimeter wave sensing has completed the concept of autonomy. In automotive applications, TI millimeter wave radar is one of the key elements of an advanced driver assistance system (ADAS) that monitors the car's surroundings. You can put something similar to ADAS robotics concepts (such as looking around for supervision or collision avoidance).

4. From the point of view of sensing technology, TI millimeter wave radar is unique because this sensor can provide spacing between objects. Speed and arrival perspective information can better guide robot navigation, thus reducing collisions. Based on radar sensor data, the robot can decide whether to continue driving safely, slow down or even terminate based on the location, speed and trajectory of people and objects near it.
Use sensor fusion and edge fusion AI to tackle the complex problems of autonomous robots

For more complex applications, any type of separate sensor may not be enough to accomplish autonomy. Finally, multiple sensors, such as cameras or radars, should complement each other in the same system. According to sensor integration, using data from the processor's different types of sensors helps address more complex autonomous robot challenges.

Sensor integration helps make robots more accurate, and using edge artificial intelligence (AI) can make robots smarter. AI robot systems can help robots learn intelligently. Make decisions and perform actions. AI robots are able to intelligently examine objects and locations, screen objects and act accordingly. For example, when a robot is navigating through a cluttered warehouse, edge AI can help the robot infer what kind of objects are in its path (including people, boxes, machines, and even other robots) and decide what is appropriate to navigate around those objects.

In planning the selection of AI in robotic systems, both hardware and software have some design considerations. TDA4 Processor This family is suitable for edge AI capabilities Hardware accelerators can help with parallel processing of compute-intensive tasks.

Conclusion

Designing smarter, more independent robots is a prerequisite for continuing to improve automation. Robots are suitable for the warehouse and distribution industries, and then keep up with and facilitate the development of e-commerce. The robot can also do daily chores such as dusting and weeding. The application of independent robots can improve productivity and efficiency, which is conducive to improving our lives and bringing more value to life.