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Found 12 papers, 7 papers with code
Convex Risk Bounded Continuous-Time Trajectory Planning and Tube Design in Uncertain Nonconvex Environments
To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem.
Real-Time Tube-Based Non-Gaussian Risk Bounded Motion Planning for Stochastic Nonlinear Systems in Uncertain Environments via Motion Primitives
We consider the motion planning problem for stochastic nonlinear systems in uncertain environments.
Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems
In particular, in the provided optimization problem, we use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems.
TIP: Task-Informed Motion Prediction for Intelligent Vehicles
When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples.
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction.
Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures
The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models to predict both agent positions and control inputs conditioned on the scene contexts.
Risk Conditioned Neural Motion Planning
In this paper, we propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic and produce risk-bounded policies efficiently by adding an extra risk term in the loss function of the policy network.
Moment-Based Exact Uncertainty Propagation Through Nonlinear Stochastic Autonomous Systems
Moments of uncertain states can be used in estimation, planning, control, and safety analysis of stochastic dynamical systems.
Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures
The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models for predictions of both agent positions and controls.
A Closed-Form Uncertainty Propagation in Non-Rigid Structure from Motion
In this paper, we present a statistical inference on the element-wise uncertainty quantification of the estimated deforming 3D shape points in the case of the exact low-rank SDP problem.
Moment State Dynamical Systems for Nonlinear Chance-Constrained Motion Planning
Chance-constrained motion planning requires uncertainty in dynamics to be propagated into uncertainty in state.
Convex Constrained Semialgebraic Volume Optimization: Application in Systems and Control
In this paper, we generalize the chance optimization problems and introduce constrained volume optimization where enables us to obtain convex formulation for challenging problems in systems and control.
Optimization and Control
