Risk-Averse Receding Horizon Motion Planning for Obstacle Avoidance using Coherent Risk Measures
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866914734617395200 |
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| author | Dixit, Anushri Ahmadi, Mohamadreza Burdick, Joel W. |
| author_facet | Dixit, Anushri Ahmadi, Mohamadreza Burdick, Joel W. |
| contents | This paper studies the problem of risk-averse receding horizon motion planning for agents with uncertain dynamics, in the presence of stochastic, dynamic obstacles. We propose a model predictive control (MPC) scheme that formulates the obstacle avoidance constraint using coherent risk measures. To handle disturbances, or process noise, in the state dynamics, the state constraints are tightened in a risk-aware manner to provide a disturbance feedback policy. We also propose a waypoint following algorithm that uses the proposed MPC scheme for discrete distributions and prove its risk-sensitive recursive feasibility while guaranteeing finite-time task completion. We further investigate some commonly used coherent risk metrics, namely, conditional value-at-risk (CVaR), entropic value-at-risk (EVaR), and g-entropic risk measures, and propose a tractable incorporation within MPC. We illustrate our framework via simulation studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_09596 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Risk-Averse Receding Horizon Motion Planning for Obstacle Avoidance using Coherent Risk Measures Dixit, Anushri Ahmadi, Mohamadreza Burdick, Joel W. Systems and Control Robotics Optimization and Control This paper studies the problem of risk-averse receding horizon motion planning for agents with uncertain dynamics, in the presence of stochastic, dynamic obstacles. We propose a model predictive control (MPC) scheme that formulates the obstacle avoidance constraint using coherent risk measures. To handle disturbances, or process noise, in the state dynamics, the state constraints are tightened in a risk-aware manner to provide a disturbance feedback policy. We also propose a waypoint following algorithm that uses the proposed MPC scheme for discrete distributions and prove its risk-sensitive recursive feasibility while guaranteeing finite-time task completion. We further investigate some commonly used coherent risk metrics, namely, conditional value-at-risk (CVaR), entropic value-at-risk (EVaR), and g-entropic risk measures, and propose a tractable incorporation within MPC. We illustrate our framework via simulation studies. |
| title | Risk-Averse Receding Horizon Motion Planning for Obstacle Avoidance using Coherent Risk Measures |
| topic | Systems and Control Robotics Optimization and Control |
| url | https://arxiv.org/abs/2204.09596 |