Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning

by   Yue Meng, et al.

Density of the reachable states can help understand the risk of safety-critical systems, especially in situations when worst-case reachability is too conservative. Recent work provides a data-driven approach to compute the density distribution of autonomous systems' forward reachable states online. In this paper, we study the use of such approach in combination with model predictive control for verifiable safe path planning under uncertainties. We first use the learned density distribution to compute the risk of collision online. If such risk exceeds the acceptable threshold, our method will plan for a new path around the previous trajectory, with the risk of collision below the threshold. Our method is well-suited to handle systems with uncertainties and complicated dynamics as our data-driven approach does not need an analytical form of the systems' dynamics and can estimate forward state density with an arbitrary initial distribution of uncertainties. We design two challenging scenarios (autonomous driving and hovercraft control) for safe motion planning in environments with obstacles under system uncertainties. We first show that our density estimation approach can reach a similar accuracy as the Monte-Carlo-based method while using only 0.01X training samples. By leveraging the estimated risk, our algorithm achieves the highest success rate in goal reaching when enforcing the safety rate above 0.99.


Density Planner: Minimizing Collision Risk in Motion Planning with Dynamic Obstacles using Density-based Reachability

Autonomous systems with uncertainties are prevalent in robotics. However...

Learning Density Distribution of Reachable States for Autonomous Systems

State density distribution, in contrast to worst-case reachability, can ...

Safe Mission Planning under Dynamical Uncertainties

This paper considers safe robot mission planning in uncertain dynamical ...

Efficient Computation of Collision Probabilities for Safe Motion Planning

We address the problem of safe motion planning. As mobile robots and aut...

Convex Approximation for Probabilistic Reachable Set under Data-driven Uncertainties

This paper is proposed to efficiently provide a convex approximation for...

Adaptive Safety Margin Estimation for Safe Real-Time Replanning under Time-Varying Disturbance

Safe navigation in real-time is challenging because engineers need to wo...

Please sign up or login with your details

Forgot password? Click here to reset