Inspection for structural properties (surface stiffness and coefficient of restitution) is crucial for understanding and performing aerial manipulations in unknown environments, with little to no prior knowledge on their state. Inspection-onthe-fly is the uncanny ability of humans to infer states during manipulation, reducing the necessity to perform inspection and manipulation separately. This paper presents an infrastructure for inspection-on-the-fly method for aerial manipulators using hybrid physical interaction control. With the proposed method, structural properties (surface stiffness and coefficient of restitution) can be estimated during physical interactions. A three-stage hybrid physical interaction control paradigm is presented to robustly approach, acquire and impart a desired force signature onto a surface. This is achieved by combining a hybrid force/motion controller with a model-based feed-forward impact control as intermediate phase. The proposed controller ensures a steady transition from unconstrained motion control to constrained force control, while reducing the lag associated with the force control phase. And an underlying Operational Space dynamic configuration manager permits complex, redundant vehicle/arm combinations. Experiments were carried out in a mock-up of a Dept. of Energy exhaust shaft, to show the effectiveness of the inspection-on-the-fly method to determine the structural properties of the target surface and the performance of the hybrid physical interaction controller in reducing the lag associated with force control phase.

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Construction spaces are constantly evolving, dynamic environments in need of continuous surveying, inspection, and assessment. Traditional manual inspection of such spaces proves to be an arduous and time-consuming activity. Automation using robotic agents can be an effective solution. Robots, with perception capabilities can autonomously classify and survey indoor construction spaces. In this paper, we present a novel identification-on-the-fly approach for coarse classification of indoor spaces using the unique signature of clutter. Using the context granted by clutter, we recognize common indoor spaces such as corridors, staircases, shared spaces, and restrooms. The proposed clutter slices pipeline achieves a maximum accuracy of 93.6% on the presented clutter slices dataset. This sensor independent approach can be generalized to various domains to equip intelligent autonomous agents in better perceiving their environment.

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