There is a growing need for robots to perform complex tasks autonomously. However, there remain certain tasks that cannot -or should not -be completely automated. While these tasks may require one or several operators, we can oftentimes schedule when an operator should assist. We build on our previous work to present a methodology for allocating operator attention across multiple robots while attempting to minimize the execution time of the robots involved. In this paper, we: 1) Analyze of the complexity of this problem, 2) Provide a scalable methodology for designing robot policies so that few operators can oversee many robots, 3) Describe a methodology for designing both policies and robot trajectories to permit operators to assist many robots, and 4) Present simulation and hardware experiments demonstrating our methodologies.

There is a need for semi-autonomous systems capable of performing both automated tasks and supervised maneuvers. When dealing with multiple robots or robots with high complexity (such as humanoids), we face the issue of effectively coordinating operators across robots. We build on our previous work to present a methodology for designing trajectories and policies for robots such that a few operators can supervise multiple robots. Specifically, we: (1) Analyze the complexity of the problem, (2) Design a procedure for generating policies allowing operators to oversee many robots, (3) Present a method for designing policies and robot trajectories to allow operators to oversee multiple robots, and (4) Include both simulation and hardware experiments demonstrating our methodologies.

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Recent advances in mobile device, wireless networking, and positional technologies have helped location-aware applications become pervasive. However, location trajectory privacy concerns hinder the adoptability of such applications. In this article, we survey existing trajectory privacy work in the context of wireless sensor networks, location-based services, and geosocial networks. In each context, we categorize and summarize the main techniques according to their own feathers. Furthermore, we discuss future trajectory privacy research challenges and directions.

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