CARE: Confidence-Rich Autonomous Robot Exploration Using Bayesian Kernel Inference and Optimization

In this letter, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutualinformation(CRMI)ofqueryingcontrolactions,thenadopt an objective function consisting of predicted CRMI values and prediction uncertainties to conduct Bayesian optimization (BO), i.e., GP-based BO (GPBO). The trade-off between the best action with the highest CRMI value (exploitation) and the action with high prediction variance (exploration) can be realized. To further improve the efficiency of GPBO, we propose a novel lightweight information gain inference method based on Bayesian kernel in- ference and optimization (BKIO), achieving an approximate loga- rithmic complexity without the need for training. BKIO can also infertheCRMIandgeneratethebestactionusingBOwithbounded cumulativeregret,whichensuresitscomparableaccuracytoGPBO with much higher efficiency. Extensive numerical and real-world experiments show the desired efficiency of our proposed methods without losing exploration performance in different unstructured, cluttered environments.