Wasserstein Distributionally Robust Control Barrier Function Using Conditional Value At Risk

Wasserstein Distributionally Robust Control Barrier Function Using Conditional Value-at-Risk ...

Wasserstein Distributionally Robust Control Barrier Function Using Conditional Value-at-Risk ... In this paper, we present distributional robust cbf (dr cbf) to achieve resilience under distributional shift while keeping the advantages of cbf, such as computational efficacy and forward invariance. Wasserstein distributionally robust motion control for collision avoidance using conditional value at risk published in: ieee transactions on robotics ( volume: 38 , issue: 2 , april 2022 ).

(PDF) Robust Control Barrier Functions For Safe Control Under Uncertainty Using Extended State ...

(PDF) Robust Control Barrier Functions For Safe Control Under Uncertainty Using Extended State ... C model predictive control (smpc) and distributionally robust model predictive control (drmpc). we use conditional value at risk (cvar) to measure the probability of constraint violation and safety. in order to provide a sa e policy by construction, we rst propose using parameterized nonlinear drmpc at each time step. drmpc optimizes. This work develops a distributionally robust control barrier function (dr cbf) constraint that directly processes range sensor data to impose safety constraints to achieve safe, efficient, and robust navigation in uncertain dynamic environments. To enable distributional robustness while keeping the advantages of the control barrier function, we make the following contributions to bridge the aforementioned gaps with distributionally robust control barrier functions. In this work, we consider wasserstein ambiguity sets and derive tractable approximations of individual and joint distributionally robust chance constraints (drccs) based on the worst case conditional value at risk (wc cvar).

Dynamic Control Barrier Function-based Model Predictive Control To Safety-Critical Obstacle ...

Dynamic Control Barrier Function-based Model Predictive Control To Safety-Critical Obstacle ... To enable distributional robustness while keeping the advantages of the control barrier function, we make the following contributions to bridge the aforementioned gaps with distributionally robust control barrier functions. In this work, we consider wasserstein ambiguity sets and derive tractable approximations of individual and joint distributionally robust chance constraints (drccs) based on the worst case conditional value at risk (wc cvar). In this section, we present simulation results that demonstrate the performance and the utility of the wasserstein distributionally robust motion control method. We use conditional value at risk (cvar) to measure the probability of constraint violation and safety. in order to provide a safe policy by construction, we first propose using parameterized nonlinear drmpc at each time step. Using sensor data collected in the guadalquivir river, we empirically demonstrate the performance of the proposed method through simulations on a vessel. as a result, the vessel successfully navigates the waterway while avoiding grounding accidents, even with a limited dataset of observations. Wasserstein distributionally robust control barrier function using conditional value at risk with differentiable convex programming.

Verification And Synthesis Of Robust Control Barrier Functions: Multilevel Polynomial ...

Verification And Synthesis Of Robust Control Barrier Functions: Multilevel Polynomial ... In this section, we present simulation results that demonstrate the performance and the utility of the wasserstein distributionally robust motion control method. We use conditional value at risk (cvar) to measure the probability of constraint violation and safety. in order to provide a safe policy by construction, we first propose using parameterized nonlinear drmpc at each time step. Using sensor data collected in the guadalquivir river, we empirically demonstrate the performance of the proposed method through simulations on a vessel. as a result, the vessel successfully navigates the waterway while avoiding grounding accidents, even with a limited dataset of observations. Wasserstein distributionally robust control barrier function using conditional value at risk with differentiable convex programming.

GitHub - Jasour/Robust-Barrier-Function: Robust Barrier Function For Safety Of Uncertain ...

GitHub - Jasour/Robust-Barrier-Function: Robust Barrier Function For Safety Of Uncertain ... Using sensor data collected in the guadalquivir river, we empirically demonstrate the performance of the proposed method through simulations on a vessel. as a result, the vessel successfully navigates the waterway while avoiding grounding accidents, even with a limited dataset of observations. Wasserstein distributionally robust control barrier function using conditional value at risk with differentiable convex programming.

(PDF) Risk-Aware Wasserstein Distributionally Robust Control Of Vessels In Natural Waterways

(PDF) Risk-Aware Wasserstein Distributionally Robust Control Of Vessels In Natural Waterways

Control Barrier Functions for Nonholonomic Systems under Risk Signal Temporal Logic Specifications