Computing Informative Views to Support Remote Robot Operations

Remote assets generally have myopic sensor feedback that does not provide sufficient information alone to maintain situational awareness for effective teleoperation. Situational awareness has a large impact on mission outcomes: salient information fused and appropriately displayed to a remote operator has been shown to result in higher mission success. Additional robots or reconfigurable sensing platforms can be used to address this problem by providing additional views of the task. However, manually allocating and positioning additional resources to obtain requisite views would further burden remote operators. To address this issue, we have developed a framework to autonomously compute informative, supporting views of a task and dispatch resources to obtain the supporting views. The key insight is to leverage information in a task model called an Affordance Template that encodes important task objects and their relationship to the operated robot to predict informative viewing geometries. We demonstrate the system evaluating metrics that provide sensor coverage and to reduce target object pose uncertainty.