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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a novel holistic approach for safe autonomous exploration and mapbuilding based on constrained Bayesian optimisation. This method finds optimalcontinuous paths instead of discrete sensing locations that inherently satisfymotion and safety constraints. Evaluating both the objective and constraintsfunctions requires forward simulation of expected observations. As suchevaluations are costly, the Bayesian optimiser proposes only paths which arelikely to yield optimal results and satisfy the constraints with highconfidence. By balancing the reward and risk associated with each path, theoptimiser minimises the number of expensive function evaluations. Wedemonstrate the effectiveness of our approach in a series of experiments bothin simulation and with a real ground robot and provide comparisons to otherexploration techniques. Evidently, each method has its specific favourableconditions, where it outperforms all other techniques. Yet, by reasoning on theusefulness of the entire path instead of its end point, our method provides arobust and consistent performance through all tests and performs better than oras good as the other leading methods.

Occupancy Map Building through Bayesian Exploration