Robots rely on GPS (Global Positioning System) for outdoor navigation by using satellite signals to determine their geographic location and plan movement paths. A GPS receiver on the robot calculates its latitude, longitude, and altitude by measuring the time it takes for signals from multiple satellites to arrive. This data is combined with onboard sensors (like inertial measurement units or wheel encoders) to estimate position more accurately, especially in environments where GPS signals might be weak or intermittent. For example, agricultural robots use GPS to follow pre-mapped routes for planting or harvesting, while delivery robots in urban areas use it to stay within designated paths.
To navigate effectively, robots integrate GPS data with mapping and path-planning algorithms. Once a target location is set, the robot’s software converts GPS coordinates into a actionable route, often using waypoints. Obstacle avoidance systems—such as lidar or cameras—work alongside GPS to adjust the path in real time. For instance, a robot tasked with inspecting solar farms might use GPS to traverse between panels but switch to sensor-based navigation when approaching obstacles like uneven terrain. This hybrid approach ensures continuous operation even if GPS accuracy degrades (e.g., under heavy tree cover). Developers often implement sensor fusion techniques, like Kalman filters, to blend GPS data with inertial sensor inputs, reducing positional drift over time.
However, GPS has limitations. Signal blockage in urban canyons or dense forests can cause errors, and standard consumer-grade GPS may have meter-level inaccuracies. To address this, robots designed for precision tasks often use differential GPS (DGPS) or Real-Time Kinematic (RTK) systems, which correct errors using ground-based reference stations, achieving centimeter-level accuracy. For example, autonomous lawn mowers use RTK-GPS to create precise boundary maps. Additionally, robots may fall back to dead reckoning (using wheel odometry and IMUs) when GPS fails temporarily. Developers must balance cost, accuracy, and environmental constraints when designing these systems—choosing between consumer-grade GPS for low-cost drones or high-precision setups for industrial applications.