How do you build smarter, more independent robots with sensors and processors?

Using Sensor Fusion and Edge AI to solve complex problems in autonomous robots Using Sensor fusion and Edge AI To solve complex problems in autonomous robots Through sensor integration, data from different types of sensors in the processor can help solve some of the more complex autonomous robot challenges.

An autonomous robot is an intelligent machine that understands its environment and navigates from it without human control or intervention. Although autonomous robot technology is relatively new, it is already working in factories. Warehouse. It is widely used in urban and household fields. For example, standalone robots could be used to transport goods around a warehouse or make last-mile deliveries, while other types of robots could be used to clean or mow a home lawn.

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 route to a given destination, and control the vehicle to follow the route. In addition, robots must perform these tasks only safely to avoid risks to individuals, property, or the system itself. As humans and robots interact with each other more frequently, they need not only autonomy, but autonomy. Mobility and energy efficiency also need to meet functional safety requirements. With sensors, designers can meet the stringent requirements of functional safety standards such as the International Electrotechnical Commission 61508.

A variety of different types of sensors can be used to solve the challenges of autonomous robots. There are two types of sensors:

1. Vision sensor. Vision sensors can effectively simulate human vision and perception. The visual system can handle positioning. Challenges such as obstacle detection and collision prevention because of their high-resolution spatial coverage capabilities and the ability to detect and classify objects. Vision sensors are more cost-effective than sensors such as lidar, but vision sensors are computationally intensive.

2. Power-hungry central processing unit (CPU) and graphics processing Unit (GPU) It may pose challenges for limited power consumption robot systems. In the design of energy efficient robot systems, the CPU or GPU should be processed as little as possible. The motion picture system (SoC) in an efficient vision system should operate at a high rate. Process visual signal chains with low power consumption and low system cost. Visual processing SoC must be intelligent. Safety and energy saving. The TDA4 processor family is highly integrated and designed with a heterogeneous architecture to provide computers with computer vision performance, deep learning processing, stereo vision capabilities, and video analytics.

3.TI millimeter Wave radar. The use of TI millimeter wave radar in robotic applications is a relatively novel concept, but using it, TI has for some time realized the concept of autonomy with millimeter wave sensing. In automotive applications, TI millimeter Wave radar is a key component of an advanced driver assistance system (ADAS) for monitoring the vehicle's surroundings. You can put some similar concepts in the ADAS robotics field (such as circumnavigation monitoring or collision avoidance).

4. From the point of view of sensing technology, TI millimeter wave radar is unique because this sensor can provide the distance between objects. Speed and Angle of arrival information can better guide the robot's navigation, thus avoiding impact. Based on the data from the radar sensors, the robot can be based on the location of the person or object it is approaching. Speed and trajectory determine whether to continue safely, slow down or even stop.

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

For more complex applications, a single sensor of any kind may not be enough to achieve autonomy. Eventually, multiple sensors, such as cameras or radars, should complement each other in the same system. Through sensor integration, data from different types of sensors in the processor helps address some of the more complex autonomous robot challenges.

Sensor integration helps make robots more accurate, and using edge artificial intelligence (AI) can make robots smarter. The integration of AI robot systems can help robots with intelligent perception. Make decisions and perform actions. AI robots are able to intelligently detect objects and their positions, classify objects, and operate accordingly. For example, when a robot is navigating a cluttered warehouse, edge AI can help the robot infer what kind of objects (including people) are in its path. Boxes, machines, or even other robots) and decide to navigate around these objects appropriately.

In designing to adopt AI in robotic systems, both hardware and software have a number of design considerations. The TDA processor family is suitable for edge AI capabilities Hardware accelerators can help handle compute-intensive tasks in real time. An accessible and easy-to-use edge AI software development environment helps simplify and accelerate the application development and hardware deployment process.

Conclusion

Designing smarter, more independent robots is necessary to continue to increase the level of automation. Robots can be used in the warehouse and distribution sectors to keep up with and facilitate the development of e-commerce. The robot can also do daily chores such as dusting and weeding. Using independent robots can increase productivity and efficiency, help improve our lives, and give life more value.