2 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions
Jonathan Michaux, Qingyi Chen, Challen Enninful Adu +2
Generating receding-horizon motion trajectories for autonomous vehicles in real-time while also providing safety guarantees is challenging. This is because a future trajectory need…
Not All Actions Are Created Equal: Bayesian Optimal Experimental Design for Safe and Optimal Nonlinear System Identification
Parker Ewen, Gitesh Gunjal, Joey Wilson +3
Uncertainty in state or model parameters is common in robotics and typically handled by acquiring system measurements that yield information about the uncertain quantities of inter…
RADIUS: Risk-Aware, Real-Time, Reachability-Based Motion Planning
Jinsun Liu, Challen Enninful Adu, Lucas Lymburner +3
Deterministic methods for motion planning guarantee safety amidst uncertainty in obstacle locations by trying to restrict the robot from operating in any possible location that an…
REFINE: Reachability-based Trajectory Design using Robust Feedback Linearization and Zonotopes
Jinsun Liu, Yifei Shao, Lucas Lymburner +7
Performing real-time receding horizon motion planning for autonomous vehicles while providing safety guarantees remains difficult. This is because existing methods to accurately pr…
Leveraging the Template and Anchor Framework for Safe, Online Robotic Gait Design
Jinsun Liu, Pengcheng Zhao, Zhenyu Gan +2
Online control design using a high-fidelity, full-order model for a bipedal robot can be challenging due to the size of the state space of the model. A commonly adopted solution to…
Guaranteed Globally Optimal Planar Pose Graph and Landmark SLAM via Sparse-Bounded Sums-of-Squares Programming
Joshua G. Mangelson, Jinsun Liu, Ryan M. Eustice +1
Autonomous navigation requires an accurate model or map of the environment. While dramatic progress in the prior two decades has enabled large-scale SLAM, the majority of existing…