Fast distance queries with rectangular swept sphere volumes

The problem of distance computation arises in many applications including motion planning, CAD/CAM, dynamic simulation and virtual environments. Most prior work in this area has been restricted to separation or penetration distance computation between two objects. In this paper, we address the problem of computing a measure of distance between two configurations of a rigid or articulated model. The underlying distance metric is defined as the length of the longest displacement vector over the corresponding vertices of the model between two configurations. Our algorithm is based on Chasles theorem in Screw theory, and we show that the maximum distance can be realized only by a vertex of the convex hull of a rigid object. We use this formulation to compute the distance, and present two acceleration techniques to speed up the computation: incremental walking on the dual space of the convex hull and culling vertices on the convex hull using a bounding volume hierarchy (BVH). Our algorithm can be easily extended to articulated models by maximizing the distance over its each link and we also present culling techniques to accelerate the computation. We highlight the performance of our algorithm on many complex models and describe its application to proximity queries and motion planning.

Due to the advancement of geo-positioning technology, the terrain data has become increasingly popular and has drawn a lot of research effort from both academia and industry. The distance computation on the terrain surface is a fundamental and important problem that is widely applied in geographical information systems and 3D modeling. As could be observed from the existing studies, online computation of the distance on the terrain surface is very expensive. All existing index-based methods are only efficient under the case where the distance query must be performed among a small set of predefined points-of-interest known apriori. But, in general cases, they could not scale up to sizable datasets due to their intolerable oracle building time and space consumption. In this paper, we studied the arbitrary point-to-arbitrary point distance query on the terrain surface in which no assumption is imposed on the query points, and the distance query could be performed between any two arbitrary points. We propose an indexing structure, namely Efficient Arbitrary Point-to-Arbitrary Point Distance Oracle (EAR-Oracle), with theoretical guarantee on the accuracy, oracle building time, oracle size and query time. Our experiments demonstrate that our oracle enjoys excellent scalability and it scales up to enormous terrain surfaces but none of the existing index-based methods could be able to. Besides, it significantly outperforms all existing online computation methods by orders of magnitude in terms of the query time.

Due to the advance of the geo-spatial positioning and the computer graphics technology, digital terrain data become more and more popular nowadays. Query processing on terrain data has attracted considerable attention from both the academic community and the industry community. One fundamental and important query is the shortest distance query and many other applications such as proximity queries (including nearest neighbor queries and range queries), 3D object feature vector construction and 3D object data mining are built based on the result of the shortest distance query. In this paper, we study the shortest distance query which is to find the shortest distance between a point-of-interest and another point-of-interest on the surface of the terrain due to a variety of applications. As observed by existing studies, computing the exact shortest distance is very expensive. Some existing studies proposed e-approximate distance oracles where e is a non-negative real number and is an error parameter. However, the best-known algorithm has a large oracle construction time, a large oracle size and a large distance query time. Motivated by this, we propose a novel e-approximate distance oracle called the Space Efficient distance oracle (SE) which has a small oracle construction time, a small oracle size and a small distance query time due to its compactness storing concise information about pairwise distances between any two points-of-interest. Our experimental results show that the oracle construction time, the oracle size and the distance query time of SE are up to two orders of magnitude, up to 3 orders of magnitude and up to 5 orders of magnitude faster than the best-known algorithm.

In the past decades, shortest distance methods for road networks have been developed that focus on how to speed up the latency of a single source-target pair distance query. Large analytical applications on road networks including simulations (e.g., evacuation planning), logistics, and transportation planning require methods that provide high throughput (i.e., distance computations per second) and the ability to "scale out" by using large distributed computing clusters. A framework called SPDO is presented which implements an extremely fast distributed algorithm for computing road network distance queries on Apache Spark. The approach extends our previous work of developing the ε-distance oracle which has now been adapted to use Spark's resilient distributed dataset (RDD). Compared with state-of-the-art methods that focus on reducing latency, the proposed framework improves the throughput by at least an order of magnitude, which makes the approach suitable for applications that need to compute thousands to millions of network distances per second.

The need for collision detection arises in several robotics areas, including motion-planning, online collision avoidance, and simulation. At the heart of most current methods are algorithms for interference detection and/or distance computation. A few recent algorithms and implementations are very fast, but to use them for accurate collision detection, very small step sizes can be necessary, reducing their effective efficiency. We present a fast, implemented technique for doing exact distance computation and interference detection for translationally-swept bodies. For rotationally swept bodies, we adapt this technique to improve accuracy, for any given step size, in distance computation and interference detection. We present preliminary experiments which show that the combination of basic and swept-body calculations holds much promise for faster accurate collision detection.

The hardware compatibility of legged locomotion is often illustrated by Zero Moment Point (ZMP) that has been extensively studied for decades. One of the most popular models for computing the ZMP is the linear inverted pendulum (LIP) model that expresses ZMP as a linear function of the center of mass(COM) and its acceleration. In the real world, however, it may not accurately predict the true ZMP of hardware due to various reasons such as unmodeled dynamics and differences between simulation model and hardware. In this paper, we aim to improve the theoretical ZMP model by learning the real hardware dynamics from experimental data. We first optimize the motion plan using the theoretical ZMP model and collect COP data by executing the motion on a force plate. We then train a new ZMP model that maps the motion plan variable to the actual ZMP and use the learned model for finding a new hardware-compatible motion plan. Through various locomotion tasks of a quadruped, we demonstrate that motions planned for the learned ZMP model are compatible on hardware when those for the theoretical ZMP model are not. Furthermore, experiments using ZMP models with different complexities reveal that overly complex models may suffer from over-fitting even though they can potentially represent more complex, unmodeled dynamics.

Approximate computing is an emerging design paradigm that exploits the intrinsic ability of applications to produce acceptable outputs even when their computations are executed approximately. In this work, we explore approximate computing for a key computation pattern, Reduce-and-Rank (RnR), which is prevalent in a wide range of workloads including video processing, recognition, search and data mining. An RnR kernel performs a reduction operation (e.g., distance computation, dot product, L1-norm) between an input vector and each of a set of reference vectors, and ranks the reduction outputs to select the top reference vectors for the current input. We propose two complementary approximation strategies for the RnR computation pattern. The first is interleaved reduction-and-ranking, wherein the vector reductions are decomposed into multiple partial reductions and interleaved with the rank computation. Leveraging this transformation, we propose the use of intermediate reduction results and ranks to identify future computations that are likely to have low impact on the output, and can hence be approximated. The second strategy, input similarity based approximation, exploits the spatial or temporal correlation of inputs (e.g., pixels of an image or frames of a video) to identify computations that are amenable to approximation. These strategies address a key challenge in approximate computing - identification of which computations to approximate - and may be used to drive any approximation mechanism such as computation skipping and precision scaling to realize performance or energy improvements. A second key challenge in approximate computing is that the extent to which computations can be approximated varies significantly from application to application, and across inputs for even a single application. Hence, quality configurability, or the ability to automatically modulate the degree of approximation at runtime is essential. To enable quality configurability in RnR kernels, we propose a kernel-level quality metric that correlates well to application-level quality, and identify key parameters that can be used to tune the proposed approximation strategies dynamically. We develop a runtime framework that modulates the identified parameters during execution of RnR kernels to minimize their energy while meeting a given target quality. To evaluate the proposed concepts, we designed quality-configurable hardware implementations of 6 RnR-based applications from the recognition, mining, search and video processing application domains in 45nm technology. Our experiments demonstrate 1.06X-2.18X reduction in energy consumption with virtually no loss in output quality (<;0.5%) at the application-level. The energy benefits further improve up to 2.38X and 2.5X when the quality constraints are relaxed to 2.5% and 5% respectively.

The ultrasonic sensor is the most replied sensor in mobile robot to get environmental information and avoid obstacles. In fact, this type of sensors offers satisfactory results with affordable cost. However, one sensor is insufficient for a better perception of the environment. In this paper we opt for the conception of an ultrasonic sensor network to extract as much information as possible for measuring the distance between the robot and the obstacle. The algorithm for distance calculation is based on the measurement of the time of flight for the ultrasonic wave. The experimental results show a good performance of the conceived system for the distinction between different shapes of obstacles.

The a4a initiative aims to provide timely and cost effective advice for the circa. 250 fish stocks that,\nthrough the EU Data Collection Framework, will have at least 10 years of data by the year 2020. Current\nprocesses for assessing the state of and managing fish stocks are intensive processes, each stock\nrequiring the attention of one or more stock assessment scientist to produce preliminary catch advice,\nwhich is subsequently reviewed by one or two committees before the final catch advice is published.\nIngrained in the development of these processes has been the development of more and more complex\nstock assessment models which typically require highly skilled personnel to set up and run.

Control of manipulator collision avoidance by using computer‐aided potential field method is studied. A repulsive force is artificially created using the distances between the robot links and obstacles, which are generated by a distance computation algorithm. Typical approaches for definition of link coordinate frames and kinematics computations are adopted. Control gains in the potential field control model are selected with respect to the ranges of the angular velocity and angular acceleration of an industrial robot. Results on collision detection and collision avoidance control are presented. Using a high‐speed personal computer, real‐time manipulator control was achieved. From the results, changing repulsive force gain can change the safety distance. This makes the safety zone adjustable and provides greater intelligence for robotic tasks under the ever‐changing environment.

We present a hybrid approach to generate plausible motions for high-DOF human-like articulated figures in constrained environment with multiple obstacles. The method combines motion planning and motion blending techniques to efficiently produce natural and collision-free human motion for different tasks.

Asymptotically optimal motion planners guarantee that solutions approach optimal as more iterations are performed. There is a recently proposed roadmap-based method that provides this desirable property, the PRM ∗ approach, which minimizes the computational cost of generating the roadmap. Even for this method, however, the roadmap can be slow to construct and quickly grows too large for storage or fast online query resolution. From graph theory, there are many algorithms that produce sparse subgraphs, known as spanners, which can guarantee near optimal paths. In this work, a method for interleaving an incremental graph spanner algorithm with the asymptotically optimal PRM ∗ algorithm is described. The result is an asymptotically near-optimal motion planning solution. Theoretical analysis and experiments performed on typical, geometric motion planning instances show that large reductions in construction time, roadmap density, and online query resolution time can be achieved with a small sacrifice of path quality. If smoothing is applied, the results are even more favorable for the near-optimal solution.

Tube-rupture events in shell and tube heat exchangers can result in significantly high surge pressures. Steady state and dynamic methods can be used to assess the impacts of these events on heat exchanger system piping networks. This paper presents the findings of a set of dynamic surge simulations on the impacts of tube-rupture events in a Propane-Feed Gas Heat Exchanger System. Once adjacent piping design is considered, the Joukowsky formulation-based method is not always appropriate to estimate tube-rupture surge impacts. Dynamic simulations need to be conducted to assess the tube-rupture impact on piping systems due to the surge wave amplification as it is transmitted and reflected in the complex pipe network. For blocked-in (noflow) or isolated systems, properly designed relief mechanisms are required to alleviate the tuberupture resultant pressure build-up.

Purpose – This paper aims to find an objects representation scheme with high precision and to compute the objects’ separation distance effectively in final analysis. Proximity queries have been used widely in robot trajectory planning, automatic assembly planning, virtual surgery and many other applications. The core of proximity query is the precise computation of (minimum) separation distance in narrow phase, and specific object representation scheme corresponds to different methods of separation distance computation. Design/methodology/approach – In this paper, a second-order cone programming (SOCP)-based (minimum) separation distance computation algorithm was proposed. It treats convex superquadrics, descriptive primitives of complex object as the study objects. The separation distance between two convex superquadrics was written as a general nonlinear programming (NLP) problem with superquadric constraints and then transformed into an SOCP problem with the method of conic formulation of superquadric constraints. Finally, a primal-dual interior point method embedded in MOSEK was used for solving the SOCP problem. Findings – The proposed algorithm achieved exact separation distance computation between convex superquadrics, with a relative error of 10-6. It is particularly suitable for proximity queries in narrow phase of static collision detection algorithms. Further, the proposed algorithm achieved continuous collision detection between rectilinear translation superquadrics. Originality/value – The proposed algorithm in narrow phase of static collision detection algorithms makes objects’ separation distance effectively computed. Proximity queries are easy and more accurate to perform in this way.

BackgroundThe intention of mandibular reconstruction is to restore the complex anatomy with maximum possible functionality and high accuracy. The aim of this study was to evaluate the accuracy of computer-assisted surgery in primary mandibular reconstruction with an iliac crest bone flap compared with an osteomyocutaneous fibula flap.Materials and methodsPreoperative computed tomography data of the mandible and the iliac crest or fibula donor site were imported into a specific surgical planning software program. Surgical guides were manufactured using a rapid prototyping technique for translating the virtual plan, including information on the transplant dimensions and shape, into real-time surgery. Using postoperative computed tomography scans and an automatic surface-comparison algorithm, the actual postoperative situation was compared with the preoperative virtual simulation.ResultsThe actual flap position showed a mean difference from the virtual plan of 2.43 mm (standard deviation [SD] ±1.26) and a surface deviation of 39% <2 mm and 15% <1 mm for the iliac crest bone flap, and a mean difference of 2.18 mm (SD ±1.93) and a surface deviation of 60% <2 mm and 37% <1 mm for the osteomyocutaneous fibula flap. The position of the neomandible reconstructed with an osteomyocutaneous fibula flap indicated a mean difference from the virtual plan of 1.25 mm (SD ±1.31) and a surface deviation of 82% <2 mm and 57% <1 mm, in contrast to a mean difference of 1.68 mm (SD ±1.25) and a surface deviation of 63% <2 mm and 38% <1 mm for the neomandible after reconstruction with an iliac crest bone flap. For shape analysis, a similarly high accuracy could be calculated for both flaps.ConclusionVirtual surgical planning is an effective method for mandibular reconstruction with vascularized bone flaps, and can help to restore the anatomy of the mandible with high accuracy in position and shape. It seems that primary mandibular reconstruction with the osteomyocutaneous fibula flap is more accurate compared with the vascularized iliac crest bone flap.