A robot moving through an unfamiliar building must build a picture of its surroundings and work out where it sits within them at the same time. Solving both problems together from camera images is known as SLAM, short for simultaneous localization and mapping. The cameras used here record a normal colour image together with the distance to everything in view; such devices are called depth cameras, or RGB-D cameras, and they suit indoor robots because they provide both appearance and scale without the cost of a laser scanner. Many open-source SLAM systems exist, and they differ considerably in how they store the map, how they recognize a place already visited, how much computing power they require, and how well they cope with dim light or rapid motion.
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A robot driving around a room often observes an object from an awkward position, where it may be partly hidden behind furniture, too distant, or lit from behind. A conventional vision pipeline runs an object detector on whatever image is available and accepts the result even when confidence is low. A robot on wheels, unlike a fixed camera, has a further option open to it: moving to a different position and observing again. Choosing where to look next, rather than only interpreting what is already in view, is known as active perception.
Low-cost robotic arms are usually driven by scripts that command each joint directly, which is adequate for a demonstration but rarely transfers to the next project and offers no protection against the arm moving through an obstacle or into itself. Robotics addresses this with two standard pieces of software: the first gives programs a common way to communicate with real or simulated motors and is called ROS 2 Control, while the second computes the joint angles needed to reach a position, checks the path for collisions, and plans a safe movement, and is called MoveIt 2. Taken together, they turn a one-off script into a reusable arm setup that transfers between simulation and hardware.
The context is sustainbable software development. We try to find coding alternatives that are economical with regard to energy (electricity) consumption.
Swarms of unmanned aerial vehicles (UAVs) are used in search and rescue, infrastructure inspection, and environmental monitoring. An operator supervising such a swarm has to follow many vehicles at once, understand where they are relative to one another, and issue commands to groups rather than to single drones. Ground control stations present this three-dimensional situation on flat screens, which leaves the operator to reconstruct the spatial picture mentally. A Mixed Reality (MR) headset can instead place drone positions, planned trajectories, and status information in the space where the drones actually fly.