Safe Mission Planning under Dynamical Uncertainties
This addresses safety-critical planning for robots in applications like surveillance and autonomous driving, but it appears incremental as it builds on existing uncertainty modeling and planning methods.
The paper tackles safe robot mission planning under dynamical uncertainties by developing a probabilistic model and a framework to generate safe paths, and it demonstrates performance through case studies.
This paper considers safe robot mission planning in uncertain dynamical environments. This problem arises in applications such as surveillance, emergency rescue, and autonomous driving. It is a challenging problem due to modeling and integrating dynamical uncertainties into a safe planning framework, and finding a solution in a computationally tractable way. In this work, we first develop a probabilistic model for dynamical uncertainties. Then, we provide a framework to generate a path that maximizes safety for complex missions by incorporating the uncertainty model. We also devise a Monte Carlo method to obtain a safe path efficiently. Finally, we evaluate the performance of our approach and compare it to potential alternatives in several case studies.