Telerobotic systems have traditionally been designed and operated from a human point of view. Though this approach suffices for some domains, it is clearly sub-optimal for tasks such as operating multiple vehicles or controlling planetary rovers. Thus, I believe it is worthwhile to examine a new teleoperation approach: collaborative control. In this robot-centric model, instead of the human always being "in charge", the robot works as a peer and makes requests of the human. In other words, the human is treated as an imprecise, limited source of planning and information, just like sensors and maps and other noisy modules. To examine the numerous human-machine interaction and system design issues raised by this new approach, I propose to build a vehicle teleoperation system based on collaborative control. In my research, I will show how this approach enables efficient teleoperation and optimizes use of human resources.

This paper formulates analysis in terms of spatial interest functions and consensus regions, and presents system architecture, interface, and algorithms for processing voting data

ShareCam is a robotic pan, tilt, and zoom webbased camera controlled by simultaneous frame requests from online users. Part I describes the system. This paper, Part II, focuses on algorithms. The ShareCam problem is to find a camera frame that optimizes a measure of total user satisfaction. We present a grid-based approximation algorithm: given camera frame requests from n users, and approximation bound #, we analyze the tradeoff between solution quality and processing speed and prove that the algorithm runs in O(n/# ) time. The algorithm can be distributed to run in O(1/# ) time at each client and in O(n + 1/# ) time at the server. Experiments suggest that performance of the distributed algorithm degrades gracefully as clients fail to complete their part of the computation. ShareCam can be found online at: http://www.tele-actor.net/sharecam/.

ShareCam is a robotic pan, tilt, and zoom web-based camera controlled by simultaneous frame requests from online users. Part II describes algorithms. This paper, Part I, focuses on the system. Robotic webcameras are commercially available but currently restrict control to only one user at a time. ShareCam introduces a new interface that allows simultaneous control by many users. In this Java-based interface, participating users interact from their remotely located browsers where users draw desired frames over a fixed panoramic image. User inputs are transmitted back to a pair of PC servers that compute optimal camera parameters, servo the camera, and provide a video stream to all users. We describe the system, online experiments, and compare results with two frame selection models based on user ``satisfaction,'' one memoryless and the second based on satisfaction over multiple motion cycles. ShareCam is available online at: www.tele-actor.net/sharecam/

This paper describes Version 3.0 of the system architecture, SDV interface, algorithms for automated goal selection, and metrics for collaboration and leadership. We report results from a July 2001 field test with 56 remote users. See: www.tele-actor.net

We consider a system that allows n networked users to share control over a robotic webcamera.

We propose the ShareCam Problem: controlling a single robotic pan, tilt, zoom camera based on simultaneous frame requests from n online users. To solve it, we propose a new piecewise linear metric, Intersection Over Maximum (IOM), for the degree of satisfaction for each users. To maximize overall satisfaction, we present several algorithms. For a discrete set of m distinct zoom levels, we give an exact algorithm that runs in O(n m) time. The algorithm can be distributed to run in O(nm) time at each client and in O(n log n + mn) time at the server.

We are developing a coordinated team of robots to assemble structures. The assembly tasks are sufficiently complex that no single robot, or type of robot, can complete the assembly alone. Even with a group of multiple heterogeneous robots, each adding its unique set of capabilities to the system, the number of contingencies that must be addressed for a completely autonomous system is prohibitively large. Teleoperating a multiple robot system, at the other extreme, is difficult and performance may be highly dependent on the skill of the operator. We propose and evaluate an implementation of a framework that, ideally, provides the operator with a means to interact seamlessly with the autonomous control system. Using an architecture that incorporates sliding autonomy, the operator can augment autonomous control by providing input to help the system recover from unexpected errors and increase system efficiency. Our implementation is motivated by results from an extended series of experiments we are conducting with three robots that work together to dock both ends of a suspended beam.

Abstract—New multi-axis satellites allow camera imaging parameters to be set during each time slot based on competing demand for images, specified as rectangular requested viewing zones over the camera’s reachable field of view. The single frame selection (SFS) problem is to find the camera frame parameters that maximize reward during each time window. We formalize the SFS problem based on a new reward metric that takes into account area coverage and image resolution. For a set of client requests and a satellite with discrete resolution levels, we give an algorithm that solves the SFS problem in time @ P A. For satellites with continuously variable resolution @ a A, we give an algorithm that runs in time @ QA. We have implemented all algorithms and verify performance using random inputs. Note to Practitioners—This paper is motivated by recent innovations in earth imaging by commercial satellites. In contrast to previous methods that required waits of up to 21 days for desired earth-satellite alignment, new satellites have onboard pan-tilt-zoom cameras that can be remotely directed to provide near real-time response to requests for images of specific areas on the earth’s surface. We consider the problem of resolving competing requests for images: Given client demand as a set of rectangles on the earth surface, compute camera settings that optimize the tradeoff between pan, tilt, and zoom parameters to maximize camera revenue during each time slot. We define a new quality metric and algorithms for solving the problem for the cases of discrete and continuous zoom values. These results are a step toward multiple frame selection which will be addressed in future research. The metric and algorithms presented in this paper may also be applied to collaborative teleoperation of ground-based robot cameras for inspection and videoconferencing and for scheduling astronomic telescopes. Index Terms—Camera, ground imaging, multi-axis, satellite, teleoperation, telerobotics.

Abstract—Deployed as a natural environment observatory or a surveillance device, a remote networked robotic pan-tilt-zoom camera needs to be controlled by simultaneous frame requests from both online users and in situ sensors such as motion detectors. This paper presents algorithms that are capable of finding a camera frame that optimizes a measure of total satisfaction over all requests, which is a generalized version of the single frame-selection problem proposed by Song et al. in 2006.We present a lattice-based approximation algorithm; given n requests and approximation bound ɛ, we analyze the tradeoff between solution quality and the corresponding computation time, and prove that the algorithm runs in O(n/ɛ3) time. We also develop a branch-and-bound-like implementation that reduces the constant factor of the algorithm by more than 70%. We have implemented the algorithms, and numerical experiment results conform to our analysis. Field experiments of the proposed algorithms have been conducted in the past three years. The proposed algorithms have been deployed successfully in a variety of real world applications including natural environment observation, building construction monitoring, and the surveillance of public space. Index Terms—Natural environment observation, pan-tilt-zoom camera, teleoperation, telerobotics. Z NOMENCLATURE =[z, z] is a set of feasible values of image resolution/camera zoom range. (x, y) Center position of a camera frame. z Camera frame size, z ∈ Z. c Camera frame c =[x, y, z]. c ∗ Optimal camera frame c ∗ =[x ∗,y ∗,z ∗]. n Number of request frames. zi Image resolution of the ith request, zi ∈ Z, i = 1,...,n. ri Request i, ri =[Ti,zi], where Ti is an arbitrary closed region, and it takes constant time to compute its area, i =1,...,n. Satisfaction function of request i. si