What does 'sensor fusion' involve in robotic systems?

Sensor fusion involves the integration of data from multiple sensors to enhance environmental interpretation. In robotic systems, this process is crucial because different sensors can capture various aspects of the environment, such as distance, temperature, motion, and visual data. By combining the information from these different sources, the robotic system can develop a more comprehensive understanding of its surroundings, allowing for improved decision-making and more effective navigation.

For instance, a robot equipped with both visual and infrared sensors can better detect obstacles in low-light conditions by merging the visual data it collects with the heat signatures detected by the infrared sensor. This synergy leads to greater accuracy and robustness in the robot's perception and response to its environment.

Other options do not align with the concept of sensor fusion. Relying on a single sensor limits the range and reliability of information, while synchronizing movements pertains more to the control of the robot rather than its perception of the environment. Isolating sensor data for individual analysis does not leverage the benefits of combining multiple data streams, which is the essence of sensor fusion.