English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Motion planning for autonomous robots in human-populated environments posesnumerous challenges due to uncertainties in the robot's dynamics, environment,and interaction with other agents. Sampling-based MPC approaches, such as ModelPredictive Path Integral (MPPI) control, have shown promise in addressing thesecomplex motion planning problems. However, the performance of MPPI heavilyrelies on the choice of the sampling distribution. Existing literature oftenuses the previously computed input sequence as the mean of a Gaussiandistribution for sampling, leading to potential failures and local minima. Inthis paper, we propose novel derivations of the MPPI method to enhance itsefficiency, robustness, and convergence. Our approach includes a mathematicalformulation allowing for arbitrary sampling distributions, addressing numericalissues, and alleviating the problem of local minima. We present an efficientimportance sampling scheme that combines classical and learning-based ancillarycontrollers simultaneously, resulting in more informative sampling and controlfusion. We demonstrate our proposed scheme's superior efficiency and robustnessthrough experiments by handling model uncertainties, rapid environmentalchanges and reducing susceptibility to local minima.

Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers