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We present a path planning framework that takes into account the human'ssafety perception in the presence of a flying robot. The framework addressestwo objectives: (i) estimation of the uncertain parameters of the proposedsafety perception model based on test data collected using Virtual Reality (VR)testbed, and (ii) offline optimal control computation using the estimatedsafety perception model. Due to the unknown factors in the human tests data, itis not suitable to use standard regression techniques that minimize the meansquared error (MSE). We propose to use a Hidden Markov model (HMM) approachwhere human's attention is considered as a hidden state to infer whether thedata samples are relevant to learn the safety perception model. The HMMapproach improved log-likelihood over the standard least squares solution. Forpath planning, we use Bernstein polynomials for discretization, as theresulting path remains within the convex hull of the control points, providingguarantees for deconfliction with obstacles at low computational cost. Anexample of optimal trajectory generation using the learned human model ispresented. The optimal trajectory generated using the proposed model results inreasonable safety distance from the human. In contrast, the paths generatedusing the standard regression model have undesirable shapes due to overfitting.The example demonstrates that the HMM approach has robustness to the unknownfactors compared to the standard MSE model.

A Path Planning Framework for a Flying Robot in Close Proximity of Humans