Assured Runtime Monitoring and Planning: Toward Verification of Neural Networks for Safe Autonomous Operations
Esen Yel, Taylor J. Carpenter, Carmelo Di Franco, Radoslav Ivanov, Yiannis Kantaros, Insup Lee, James E. Weimer, Nicola Bezzo · IEEE Robotics & Automation Magazine · 2020
Autonomous systems operating in uncertain environments under the effects of disturbances and noises can reach unsafe states even while using finetuned controllers and precise sensors and actuators. To provide safety guarantees on such systems during motion planning operations, reachability analysis (RA) has been demonstrated to be a powerful tool. RA, however, suffers from computational complexity, especially when dealing with intricate systems characterized by high-order dynamics, making it hard to deploy for runtime monitoring.