Abstract

We propose a novel holistic approach to safe autonomous exploration and map building based on constrained Bayesian optimization. This method finds optimal continuous paths instead of discrete sensing locations that inherently satisfy motion and safety constraints. Evaluating both the objective and constraints functions requires forward simulation of expected observations. As such, evaluations are costly, and therefore the Bayesian optimizer proposes only paths that are likely to yield optimal results and satisfy the constraints with high confidence. By balancing the reward and risk associated with each path, the optimizer minimizes the number of expensive function evaluations. We demonstrate the effectiveness of our approach in a series of experiments both in simulation and with a real ground robot and provide comparisons with other exploration techniques. The experimental results show that our method provides robust and consistent performance in all tests and performs better than or as good as the state of the art.