Directional sensors, such as video cameras, have become ubiquitous to many autonomous robots applications, such as monitoring and surveillance. The performance of these sensors and processing algorithms, however, may be hindered by the presence of objects that block visibility. This article presents a novel approach for planning the path of a mobile directional sensor deployed to observe multiple targets distributed in an environment populated with multiple obstacles and occlusions. Unlike existing art gallery or watchman’s route methods, the visibility theory and motion planners developed in this article account for both line-of-sight visibility and bounded field-of-view constraints, and can provide obstacle avoidance based on robot geometry and kinodynamic constraints. The computational complexity analysis and experiments on a camera-equipped drone demonstrate that, despite the challenging geometric characteristics of directional C-targets, the approach scales to real-world problems. Furthermore, when compared to algorithms inspired by traveling salesman and target coverage approaches, the directional visibility planners presented in this article are significantly more effective both at guaranteeing complete target visibility and at minimizing distance traveled.

Bayesian nonparametric models, such as the Dirichlet Process Gaussian Process (DPGP), have been shown very effective at learning models of dynamic targets exclusively from data. Previous work on batch DPGP learning and inference, however, ceases to be efficient in multi-sensor applications that require decentralized measurements to be obtained sequentially over time. Batch processing, in this case, leads to redundant computations that may hinder online applicability. This paper develops a recursive approach for DPGP learning and inference in which a novel Dirichlet Process prior based on Wasserstein metric is used for measuring the similarity between multiple Gaussian Processes (GPs). Combined with the GP recursive fusion law, the proposed recursive DPGP fusion approach enables efficient online data fusion. The problem of active sensing for recursive DPGP learning and inference is also investigated by uncertainty reduction via expected mutual information. Simulation and experimental results show that the proposed approach successfully learns the models of moving targets and outperforms existing benchmark methods.