Non-Gaussian Risk Bounded Trajectory Optimization for Stochastic Nonlinear Systems in Uncertain Environments

@article{Han2022NonGaussianRB,
  title={Non-Gaussian Risk Bounded Trajectory Optimization for Stochastic Nonlinear Systems in Uncertain Environments},
  author={Weiqiao Han and Ashkan M. Z. Jasour and Brian Charles Williams},
  journal={2022 International Conference on Robotics and Automation (ICRA)},
  year={2022},
  pages={11044-11050}
}
We address the risk bounded trajectory optimization problem of stochastic nonlinear robotic systems. More precisely, we consider the motion planning problem in which the robot has stochastic nonlinear dynamics and uncertain initial locations, and the environment contains multiple dynamic uncertain obstacles with arbitrary probabilistic distributions. The goal is to plan a sequence of control inputs for the robot to navigate to the target while bounding the probability of colliding with… 

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References

SHOWING 1-10 OF 37 REFERENCES

I and J

“A and B”:

Direct fabrication of large micropatterned single crystals. p1205 21 Feb 2003. (news): Academy plucks best biophysicists from a sea of mediocrity. p994 14 Feb 2003.

Convex Chance Constrained Predictive Control without Sampling

This paper proposes a new approach to chance-constrained predictive control that does not require the evaluation of multivariate densities and uses a new bounding approach to ensure that chance constraints are satisfied, while showing empiricall y that the conservatism introduced is small.

and M

  • Pavone, “Chance-constrained sequential convex programming for robust trajectory optimization,” in 2020 European Control Conference (ECC). IEEE
  • 2020

CasADi: a software framework for nonlinear optimization and optimal control

This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last 7 years.

Real-Time Risk-Bounded Tube-Based Trajectory Safety Verification

A fast convex algorithm is provided to efficiently evaluate the probabilistic nonlinear safety constraints in the presence of arbitrary probability distributions and long planning horizons in real-time, without the need for uncertainty samples and time discretization.

Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments

This paper provides a risk bounded trajectory planning method that looks for continuous-time trajectories with guaranteed bounded risk over the planning time horizon and provides convex methods based on sum-of-squares optimization to efficiently solve the obtained nonconvex time-varying optimization problem.

A one-sided Vysochanskii-Petunin inequality with financial applications

Moment-Based Exact Uncertainty Propagation Through Nonlinear Stochastic Autonomous Systems

In this paper, we address the problem of uncertainty propagation through nonlinear stochastic dynamical systems. More precisely, given a discrete-time continuous-state probabilistic nonlinear

Non-Gaussian Chance-Constrained Trajectory Planning for Autonomous Vehicles Under Agent Uncertainty

This letter extends the state-of-the-art by presenting a methodology to upper-bound chance-constraints defined by polynomials and mixture models with potentially non-Gaussian components, and achieves its generality by using statistical moments of the distributions in concentration inequalities toupper-bound the probability of constraint violation.