Building Safer, Smarter, and More Efficient Autonomous Robots

The following article was originally published to ElectronicProducts.com

Autonomous robots are intelligent machines that can understand and navigate through their environment without human control or intervention. Although autonomous robot technology is relatively young, there are many different use cases of autonomous robots in factories, warehouses, cities, and homes. For example, autonomous robots can be used to transport goods around warehouses, or perform last-mile delivery, while other kinds of autonomous robots can vacuum homes or mow lawns.

Autonomy requires that robots can sense and orient themselves within a mapped environment, dynamically detect the obstacles around them, track those obstacles, plan their route to reach a specified destination, and control the vehicle to follow that plan. In addition, the robot must perform these tasks only when it is safe to do so, avoiding situations that pose risks to humans, property, or the autonomous system itself.

With robots working in greater proximity to humans than ever before, they must not only be autonomous, mobile, and energy-efficient but also meet functional safety requirements. Sensors, processors, and control devices can help designers reach the rigorous requirements of functional safety standards, such as International Electrotechnical Commission (IEC) 61508.

Considerations for sensing in autonomous robots

A robot without sensors will inevitably crash into obstacles, including walls, other robots, or humans, and could potentially result in serious injury. There are several different types of sensors that can help solve the challenges posed by autonomous robots.

Vision sensors closely emulate human vision and perception. Vision systems can solve the challenges of localization, obstacle detection, and collision avoidance because they have high-resolution spatial coverage and the ability to not only detect objects but classify those objects. Vision sensors are also more cost-efficient when compared with sensors like LiDAR. However, vision sensors are very computationally intensive.

Power-hungry central processing units (CPUs) and graphics processing units (GPUs) can pose a challenge in power-constrained autonomous robot systems. When designing an energy-efficient robotic system, CPU- or GPU-based processing should be minimal.

The system-on-chip (SoC) in an efficient vision system should process the vision signal chain at high speeds and low power, with optimized system costs. The SoC must also offload computationally intensive tasks such as raw image processing, dewarping, stereo depth estimation, scaling, image pyramid generation, and deep learning for maximum system efficiency. SoCs used for vision processing must be smart, safe, and energy-efficient, which high levels of on-chip integration in a heterogeneous SoC architecture can achieve.

Read the article in full on ElectronicProducts.com: Designing safer, intelligent, and efficient autonomous robots