Reproduce “CC-VPSTO: Chance-Constrained Via-Point-Based Stochastic Trajectory Optimisation for Online Robot Motion Planning under Uncertainty” on OpenResearch
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About this paper
Lara Brudermüller, Guillaume Berger, Julius Jankowski, Raunak Bhattacharyya, Raphaël Jungers, Nick Hawes
CC-VPSTO provides a real-time framework for online robot motion planning under uncertainty, generating task-efficient trajectories that adhere to constraints with a statistically bounded high probability. This method, from the Oxford Robotics Institute and collaborators, utilizes a confidence-bounded Sample Average Approximation, demonstrating successful navigation in complex environments and operation at 3 Hz on a physical robot.
Key insights
- A confidence-bounded Sample Average Approximation, utilizing a binomial distribution, effectively balances task efficiency and probabilistic safety by statistically accounting for approximation errors from finite samples.
- The framework is broadly applicable, handling arbitrary uncertainty distributions and diverse constraint types without requiring restrictive assumptions about system dynamics or constraint forms.
- In a receding-horizon MPC setting, the actual observed collision rates can be lower than the specified maximum allowed probability η, suggesting an inherent anticipatory conservatism from continuous replanning.
Summary by alphaXiv.
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