We present a prototype virtual reality user interface for robot teleoperation that supports high-level specification of 3D object positions and orientations in remote assembly tasks. Users interact with virtual replicas of task objects. They asynchronously assign multiple goals in the form of 6DoF destination poses without needing to be familiar with specific robots and their capabilities, and manage and monitor the execution of these goals. The user interface employs two different spatiotemporal visualizations for assigned goals: one represents all goals within the user’s workspace (Aggregated View), while the other depicts each goal within a separate world in miniature (Timeline View). We conducted a user study of the interface without the robot system to compare how these visualizations affect user efficiency and task load. The results show that while the Aggregated View helped the participants finish the task faster, the participants preferred the Timeline View.

We study various models for the one-dimensional multi-stage facility location problems with transient agents, where a transient agent arrives in some stage and stays for a number of consecutive stages. In the problems, we need to serve each agent in one of their stages by determining the location of the facility at each stage. In the first model, we assume there is no cost for moving the facility across the stages. We focus on optimal algorithms to minimize both the social cost objective, defined as the total distance of all agents to the facility over all stages, and the maximum cost objective, defined as the max distance of any agent to the facility over all stages. For each objective, we give a slice-wise polynomial (XP) algorithm (i.e., solvable in m^f(k) for some fixed parameter k and computable function f, where m is the input size) and show that there is a polynomial-time algorithm when a natural first-come-first-serve (FCFS) order of agent serving is enforced. We then consider the mechanism design problem, where the agents' locations and arrival stages are private, and design a group strategy-proof mechanism that achieves good approximation ratios for both objectives and settings with and without FCFS ordering. In the second model, we consider the facility's moving cost between adjacent stages under the social cost objective, which accounts for the total moving distance of the facility. Correspondingly, we design XP (and polynomial time) algorithms and a group strategy-proof mechanism for settings with or without the FCFS ordering.

In this paper, a novel multimode crystal oscillator approach is proposed for a Power Management Unit (PMU). It consists of a digitally controlled Automatic Gain Control (AGC) loop allowing multiple power modes to meet the system's low phase noise and power consumption requirements. A new technique with a programmable switched capacitor array controlled by a counter is presented preserving the integrity of the clock during the mode transition. A 32.768 kHz crystal oscillator using the proposed idea was fabricated in a 0.18 μm CMOS technology. Measurement results show that it achieves phase noise of -120 dBc/Hz at 1 kHz offset in normal operating mode and only consumes 860nA current off 3.3V backup battery in RTC-only (low-power) mode including all the digital supporting circuitry. It also shows the seamless transition between the operating modes.

A novel adaptive learning algorithm is presented for the repetitive tracking control of a class of unstable nonminimum phase systems. After each repetitive trial, a least squares method is used to estimate the system parameters. The output tracking error and the identified system model are used through stable inversion to find the feed forward input, together with the desired state trajectories for the next trial. An adaptive backstepping based tracking controller is used in each trial to ensure the regulation of the desired state trajectories. Simulation results demonstrate that the proposed learning control scheme is very effective in reproducing the desired trajectories.