Planning robot actions under position and shape uncertainty

Geometric uncertainty may cause various failures during the execution of a robot control program. Avoiding such failures makes it necessary to reason about the effects of uncertainty in order to implement robust strategies. Researchers first point out that a manipulation program has to be faced with two types of uncertainty: those that might be locally processed using appropriate sensor based motions, and those that require a more global processing leading to insert new sensing operations. Then, they briefly describe how they solved the two related problems in the SHARP system: how to automatically synthesize a fine motion strategy allowing the robot to progressively achieve a given assembly relation despite position uncertainty, and how to represent uncertainty and to determine the points where a given manipulation program might fail

Connectivity-guaranteed and obstacle-adaptive deployment schemes for mobile sensor networks

Mobile sensors can relocate and self-deploy into a network. While focusing on the problems of coverage, existing deployment schemes largely over-simplify the conditions for network connectivity: they either assume that the communication range is large enough for sensors in geometric neighborhoods to obtain location information through local communication, or they assume a dense network that remains connected. In addition, an obstacle-free field or full knowledge of the field layout is often assumed. We present new schemes that are not governed by these assumptions, and thus adapt to a wider range of application scenarios. The schemes are designed to maximize sensing coverage and also guarantee connectivity for a network with arbitrary sensor communication/sensing ranges or node densities, at the cost of a small moving distance. The schemes do not need any knowledge of the field layout, which can be irregular and have obstacles/holes of arbitrary shape. Our first scheme is an enhanced form of the traditional virtual-force-based method, which we term the Connectivity-Preserved Virtual Force (CPVF) scheme. We show that the localized communication, which is the very reason for its simplicity, results in poor coverage in certain cases. We then describe a Floor-based scheme which overcomes the difficulties of CPVF and, as a result, significantly outperforms it and other state-of-the-art approaches. Throughout the paper our conclusions are corroborated by the results from extensive simulations

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Task-Driven Video Collection

Vision systems are increasingly being deployed to perform complex surveillance tasks. While improved algorithms are being developed to perform these tasks, it is also important that data suitable for these algorithms be acquired - a non-trivial task in a dynamic and crowded scene viewed by multiple PTZ cameras. In this paper, we describe a multi-camera system that collects images and videos of moving objects in such scenes, subject to task constraints. The system constructs "task visibility intervals" that contain information about what can be sensed in future time intervals. Constructing these intervals requires prediction of future object motion and consideration of several factors such as object occlusion and camera control parameters. Using a plane-sweep algorithm, these atomic intervals can be combined to form multi-task intervals, during which a single camera can collect videos suitable for multiple tasks simultaneously. Although cameras can then be scheduled based on the constructed intervals, finding an optimal schedule is a typical NP-hard problem. Due to this, and the lack of exact future information in a dynamic environment, we propose several methods for fast camera scheduling that yield solutions within a small constant factor of optimal. Experimental results illustrate system capabilities for both real and more complicated simulated scenarios

Searching edges in the overlap of two plane graphs

Consider a pair of plane straight-line graphs, whose edges are colored red and blue, respectively, and let n be the total complexity of both graphs. We present a O(n log n)-time O(n)-space technique to preprocess such pair of graphs, that enables efficient searches among the red-blue intersections along edges of one of the graphs. Our technique has a number of applications to geometric problems. This includes: (1) a solution to the batched red-blue search problem [Dehne et al. 2006] in O(n log n) queries to the oracle; (2) an algorithm to compute the maximum vertical distance between a pair of 3D polyhedral terrains one of which is convex in O(n log n) time, where n is the total complexity of both terrains; (3) an algorithm to construct the Hausdorff Voronoi diagram of a family of point clusters in the plane in O((n+m) log^3 n) time and O(n+m) space, where n is the total number of points in all clusters and m is the number of crossings between all clusters; (4) an algorithm to construct the farthest-color Voronoi diagram of the corners of n axis-aligned rectangles in O(n log^2 n) time; (5) an algorithm to solve the stabbing circle problem for n parallel line segments in the plane in optimal O(n log n) time. All these results are new or improve on the best known algorithms.Comment: 22 pages, 6 figure

Spacecraft Trajectory Planning for Optimal Observability using Angles-Only Navigation

This work leverages existing techniques in angles-only navigation to develop optimal range observability maneuvers and trajectory planning methods for spacecraft under constrained relative motion. The resulting contribution is a guidance method for impulsive rendezvous and proximity operations valid for elliptic orbits of arbitrary eccentricity. The system dynamics describe the relative motion of an arbitrary number of maneuvering (chaser) spacecraft about a single non-cooperative resident-space-object (RSO). The chaser spacecraft motion is constrained in terms of the 1) collision bounds of the RSO, 2) maximum fuel usage, 3) eclipse avoidance, and 4) optical sensor field of view restrictions. When more than one chaser is present, additional constraints include 1) collision avoidance between formation members, and 2) formation longevity via fuel usage balancing. Depending on the type of planetary orbit, quasi-circular or elliptic, the relative motion dynamics are approximated using a linear time-invariant or a linear time-varying system, respectively. The proposed method uses two distinct parameterizations corresponding to each system type to reduce the optimization problem from 12 to 2 variables in Cartesian space, thus simplifying an otherwise intractable optimization problem

5-axis double-flank CNC machining of spiral bevel gears via custom-shaped milling tools -- Part I: modeling and simulation

A new category of 5-axis flank computer numerically controlled (CNC) machining, called \emph{double-flank}, is presented. Instead of using a predefined set of milling tools, we use the shape of the milling tool as a free parameter in our optimization-based approach and, for a given input free-form (NURBS) surface, compute a custom-shaped tool that admits highly-accurate machining. Aimed at curved narrow regions where the tool may have double tangential contact with the reference surface, like spiral bevel gears, the initial trajectory of the milling tool is estimated by fitting a ruled surface to the self-bisector of the reference surface. The shape of the tool and its motion then both undergo global optimization that seeks high approximation quality between the input free-form surface and its envelope approximation, fairness of the motion and the tool, and prevents overcutting. That is, our double-flank machining is meant for the semi-finishing stage and therefore the envelope of the motion is, by construction, penetration-free with the references surface. Our algorithm is validated by a commercial path-finding software and the prototype of the tool for a specific gear model is 3D printed.RYC-2017-22649 BERC 2014-201