摘要：AbstractWe present an approach for a mobile robot to seek the global maximum of an initially unknown function defined over its operating space. The method exploits a Lipschitz assumption to defne an upper bound on the function from previously seen samples, and optimistically moves towards the largest upper-bound point. This point is iteratively changed whenever new samples make it clear that it is suboptimal. In simulations, the method finds the global maxima with much less computation than an existing, much more involved technique, while keeping performance acceptable. Real-robot experiments confirm the effectiveness of the approach.
