A robotics engineer is optimizing a fleet of 15 robots. Each robot has a 92% chance of successfully completing a task. What is the probability, to the nearest percent, that at least 13 robots succeed?

Sun, 03 May 2026 16:05:20 +0000

**How to Calculate the Odds of Robot Success: A Deep Dive for Modern Engineers** Could a robotics engineer really be fine-tuning performance across a fleet of 15 robots, each with a 92% success rate on key tasks? If so, understanding the probability that at least 13 will succeed offers valuable insight into reliability, risk, and system optimization—trends that matter as automation spreads across U.S. industries. This isn’t just a math question; it’s a lens into how engineers balance precision and probability in complex systems. ### Why This Problem Matters for Robotics and Engineering In recent years, robotics fleets have become central to sectors like logistics, manufacturing, and delivery services. Teams of robots work simultaneously, each with individual performance metrics that affect overall mission success. Engineers rely on probability models to predict how well a group performs under uncertainty. The question—what’s the chance at least 13 out of 15 robots succeed—mirrors real-world concerns: reliability, redundancy, and risk management. As automation grows, such calculations shape decisions about training, deployment, and backup systems. For curious professionals and tech-savvy readers in the U.S., grasping this model highlights the hidden complexity behind seamless automation.