The optimization of functions to find the best solution according to one or several objectives has a central role in many engineering and research fields. Recently, a new family of optimization algorithms, named Quality-Diversity optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, Quality-Diversity algorithms are searching for a large collection of both diverse and high-performing solutions. The role of this collection is to cover the range of possible solution types as much as possible, and to contain the best solution for each type. The contribution of this paper is threefold. Firstly, we present a unifying framework of Quality-Diversity optimization algorithms that covers the two main algorithms of this family (Multi-dimensional Archive of Phenotypic Elites and the Novelty Search with Local Competition), and that highlights the large variety of variants that can be investigated within this family. Secondly, we propose algorithms with a new selection mechanism for Quality-Diversity algorithms that outperforms all the algorithms tested in this paper. Lastly, we present a new collection management that overcomes the erosion issues observed when using unstructured collections. These three contributions are supported by extensive experimental comparisons of Quality-Diversity algorithms on three different experimental scenarios.
While this definition is shared with the existing literature, we also stress the importance of the coverage regularity of the produced collections. In the vast majority of the applications presented previously, not only is the coverage of importance but its uniformity is as well. For example, in the locomotion tasks, an even coverage of all possible turning abilities of the robot is required to allow the execution of arbitrary trajectories @cite_5 .
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Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible. The sizes of test suites grow as software evolves. Due to resource constraints, it is important to prioritize the execution of test cases so as to increase chances of early detection of faults. Prior techniques for test case prioritization are based on the total number of coverage requirements exercised by the test cases. In this paper, we present a new approach to prioritize test cases based on the coverage requirements present in the relevant slices of the outputs of test cases. We present experimental results comparing the effectiveness of our prioritization approach with that of existing techniques that only account for total requirement coverage, in terms of ability to achieve high rate of fault detection. Our results present interesting insights into the effectiveness of using relevant slices for test case prioritization. Program coverage is used across many stages of software development. While common during testing, program coverage has also found use outside the test lab, in production software. However, production software has stricter requirements on run-time overheads, and may limit possible program instrumentation. Thus, optimizing the placement of probes to gather program coverage is important. We introduce and study the problem of customized program coverage optimization. We generalize previous work that optimizes for complete coverage instrumentation with a system that adapts optimization to customizable program coverage requirements. Specifically, our system allows a user to specify desired coverage locations and to limit legal instrumentation locations. We prove that the problem of determining optimal coverage probes is NP-hard, and we present a solution based on mixed integer linear programming. Due to the computational complexity of the problem, we also provide two practical approximation approaches. We evaluate the effectiveness of our approximations across a diverse set of benchmarks, and show that our techniques can substantially reduce instrumentation while allowing the user immense freedom in defining coverage requirements. When naive instrumentation is dense or expensive, our optimizations succeed in lowering execution time overheads. We introduce a new problem of continuous, coverage-aware trajectory optimization under localization and sensing uncertainty. In this problem, the goal is to plan a path from a start state to a goal state that maximizes the coverage of a user-specified region while minimizing the control costs of the robot and the probability of collision with the environment. We present a principled method for quantifying the coverage sensing uncertainty of the robot. We use this sensing uncertainty along with the uncertainty in robot localization to develop C-OPT, a coverage-optimization algorithm which optimizes trajectories over belief-space to find locally optimal coverage paths. We highlight the applicability of our approach in multiple simulated scenarios inspired by surveillance, UAV crop analysis, and search-and-rescue tasks. We also present a case study on a physical, differential-drive robot. We also provide quantitative and qualitative analysis of the paths generated by our approach. Barrier coverage in visual camera sensor networks (visual barrier coverage) has important real-world applications like battlefield surveillance, environmental monitoring, and protection of government property. Cost-effective deployment, a fundamental issue of visual barrier coverage, considers how to deploy the fewest camera sensors along the barrier to detect intruders (e.g., capture faces) with desirable performance. Existing visual barrier coverage approaches like full-view coverage require numerous camera sensors for capturing intruders' faces deterministically for any trajectory and facing angle. However, intruders' trajectories and facing angles are bounded and deterministic intruder detection requires many camera sensors for rare intrusion cases. Certain practical applications can tolerate limited intrusion mis-detection given budget limitations. This paper proposes local face-view barrier coverage, a novel concept that achieves statistical barrier coverage in camera sensor networks leveraging intruders' trajectory lengths l along the barrier and head rotation angles δ. Using (l, δ) and other parameters, we derive a rigorous probability bound for intruder detection for local face-view barrier coverage via a feasible deployment pattern. Our detection probability bound and deployment pattern can guide practical camera sensor network deployments with camera sensor budgets. Extensive evaluations show that local face-view barrier coverage requires up to 50 fewer camera sensors than full-view barrier coverage. Monitoring and surveillance are important aspects in modern wireless sensor networks. In applications of wireless sensor networks, it often asks for the sensors to quickly move from the interior of a specified region to the region's perimeter, so as to form a barrier coverage of the region. The region is usually given as a simple polygon or even a circle. In comparison with the traditional concept of full area coverage, barrier coverage requires fewer sensors for detecting intruders, and can thus be considered as a good approximation of full area coverage. In this paper, we present an O(n2.5 log n) time algorithm for moving n sensors to the perimeter of the given circle such that the new positions of sensors form a regular n-gon and the maximum of the distances travelled by mobile sensors is minimized. This greatly improves upon the previous time bound O(n3.5 log n). Also, we describe an O(n4) time algorithm for moving n sensors, whose initial positions are on the perimeter of the circle, to form a regular n-gon such that the sum of the travelled distances is minimized. This solves an open problem posed in [2]. Moreover, our algorithms are simpler and have more explicit geometric flavor. Testing software product lines (SPLs) is very challenging due to a high degree of variability leading to an enormous number of possible products. The vast majority of today's testing approaches for SPLs validate products individually using different kinds of reuse techniques for testing. Because of their reusability and adaptability capabilities, model-based approaches are suitable to describe variability and are therefore frequently used for implementation and testing purposes of SPLs. Due to the enormous number of possible products, individual product testing becomes more and more infeasible. Pairwise testing offers one possibility to test a subset of all possible products. However, according to the best of our knowledge, there is no contribution discussing and rating this approach in the SPL context. In this contribution, we provide a mapping between feature models describing the common and variable parts of an SPL and a reusable test model in the form of statecharts. Thereby, we interrelate feature model-based coverage criteria and test model-based coverage criteria such as control and data flow coverage and are therefore able to discuss the potentials and limitations of pairwise testing. We pay particular attention to test requirements for feature interactions constituting a major challenge in SPL engineering. We give a concise definition of feature dependencies and feature interactions from a testing point of view, and we discuss adequacy criteria for SPL coverage under pairwise feature interaction testing and give a generalization to the T-wise case. The concept and implementation of our approach are evaluated by means of a case study from the automotive domain. In search and surveillance applications in robotics, it is intuitive to spatially distribute robot trajectories with respect to the probability of locating targets in the domain. Ergodic coverage is one such approach to trajectory planning in which a robot is directed such that the percentage of time spent in a region is in proportion to the probability of locating targets in that region. In this work, we extend the ergodic coverage algorithm to robots operating in constrained environments and present a formulation that can capture sensor footprint and avoid obstacles and restricted areas in the domain. We demonstrate that our formulation easily extends to coordination of multiple robots equipped with different sensing capabilities to perform ergodic coverage of a domain. In this paper, we address the problem of persistently covering an environment with a group of mobile robots. In contrast to traditional coverage, in our scenario the coverage level of the environment is always changing. For this reason, the robots have to continually move to maintain a desired coverage level. In this context, our contribution is a complete approach to the problem, including distributed estimation of the coverage and control of the motion of the robots. First, we present an algorithm that allows every robot to estimate the global coverage function only with local information. We pay special attention to the characterization of the algorithm, establishing bounds on the estimation error, and we demonstrate that the algorithm guarantees a perfect estimation in particular areas. Second, we introduce a new function to determine the possible improvement of the coverage at each point of the environment. Upon this metric, we build a motion control strategy that drives the robots to the points of the highest improvement while following the direction of the gradient of the function. Finally, we simulate the proposal to test its correctness and performance. This paper presents an algorithm for the complete coverage of free space by a team of mobile robots. Our approach is based on a single robot coverage algorithm, which divides the target two-dimensional space into regions called cells, each of which can be covered with simple back-and-forth motions; the decomposition of free space in a collection of such cells is known as Boustrophedon decomposition. Single robot coverage is achieved by ensuring that the robot visits every cell. The new multi-robot coverage algorithm uses the same planar cell-based decomposition as the single robot approach, but provides extensions to handle how teams of robots cover a single cell and how teams are allocated among cells. This method allows planning to occur in a two-dimensional configuration space for a team of N robots. The robots operate under the restriction that communication between two robots is available only when they are within line of sight of each other. In this paper, we investigate nonuniform coverage of a planar region by a network of autonomous, mobile agents. We derive centralized nonuniform coverage control laws from uniform coverage algorithms using cartograms, transformations that map nonuniform metrics to a near Euclidean metric. We also investigate time-varying coverage metrics and the design of control algorithms to cover regions with slowly varying, nonuniform metrics. Our results are applicable to the design of mobile sensor networks, notably when the coverage metric varies as data is collected such as in the case of an information metric. The results apply also to the study of animal groups foraging for food that is nonuniformly distributed and possibly changing. Global barrier coverage that requires much fewer sensors than full coverage, is known to be an appropriate model of coverage for movement detection applications such as intrusion detection. However, it has been proved that given a sensor deployment, sensors can not locally determine whether the deployment provides global barrier coverage, making it impossible to develop localized algorithms, thus limiting its use in practice. In this paper, we introduce the concept of local barrier coverage to address this limitation. Motivated by the observation that movements are likely to follow a shorter path in crossing a belt region, local barrier coverage guarantees the detection of all movements whose trajectory is confined to a slice of the belt region of deployment. We prove that it is possible for individual sensors to locally determine the existence of local barrier coverage, even when the region of deployment is arbitrarily curved. Although local barrier coverage does not always guarantee global barrier coverage, we show that for thin belt regions, local barrier coverage almost always provides global barrier coverage. To demonstrate that local barrier coverage can be used to design localized algorithms, we develop a novel sleep-wakeup algorithm for maximizing the network lifetime, called Localized Barrier Coverage Protocol (LBCP). We show that LBCP provides close to optimalenhancement in network lifetime, while providing global barrier coverage most of the time. It outperforms an existing algorithm called Randomized Independent Sleeping (RIS) by up to 6 times. Majority of wireless sensor networks (WSNs) clustering protocols in literature have focused on extending network lifetime and little attention has been paid to the coverage preservation as one of the QoS requirements along with network lifetime. In this paper, an algorithm is proposed to be integrated with clustering protocols to improve network lifetime as well as preserve network coverage in heterogeneous wireless sensor networks (HWSNs) where sensor nodes can have different sensing radii and energy attributes. The proposed algorithm works in proactive way to preserve network coverage and extend network lifetime by efficiently leveraging mobility to optimize the average coverage rate using only the nodes that are already deployed in the network. Simulations are conducted to validate the proposed algorithm by showing improvement in network lifetime and enhanced full coverage time with less energy consumption We present an online algorithm for a robot to shape its path to a locally optimal configuration for collecting information in an unknown dynamic environment. As the robot travels along its path, it identifies both where the environment is changing, and how fast it is changing. The algorithm then morphs the robot's path online to concentrate on the dynamic areas in the environment in proportion to their rate of change. A Lyapunov-like stability proof is used to show that, under our proposed path shaping algorithm, the path converges to a locally optimal configuration according to a Voronoi-based coverage criterion. The path shaping algorithm is then combined with a previously introduced speed controller to produce guaranteed persistent monitoring trajectories for a robot in an unknown dynamic environment. Simulation and experimental results with a quadrotor robot support the proposed approach. Locomotion in uneven terrain is important for a wide range of robotic applications, including Search&Rescue operations. Our mobile manipulation robot Momaro features a unique locomotion design consisting of four legs ending in pairs of steerable wheels, allowing the robot to omnidirectionally drive on sufficiently even terrain, step over obstacles, and also to overcome height differences by climbing. We demonstrate the feasibility and usefulness of this design on the example of the DARPA Robotics Challenge, where our team NimbRo Rescue solved seven out of eight tasks in only 34 minutes. We also introduce a method for semi-autonomous execution of weight-shifting and stepping actions based on a 2D heightmap generated from 3D laser data. We study the problem of achieving maximum barrier coverage by sensors on a barrier modeled by a line segment, by moving the minimum possible number of sensors, initially placed at arbitrary positions on the line containing the barrier. We consider several cases based on whether or not complete coverage is possible, and whether non-contiguous coverage is allowed in the case when complete coverage is impossible. When the sensors have unequal transmission ranges, we show that the problem of finding a minimum-sized subset of sensors to move in order to achieve maximum contiguous or non-contiguous coverage on a finite line segment barrier is NP-complete. In contrast, if the sensors all have the same range, we give efficient algorithms to achieve maximum contiguous as well as non-contiguous coverage. For some cases, we reduce the problem to finding a maximum-hop path of a certain minimum (maximum) weight on a related graph, and solve it using dynamic programming. Recent work has used variations of symbolic execution to automatically generate high-coverage test inputs [3, 4, 7, 8, 14]. Such tools have demonstrated their ability to find very subtle errors. However, one challenge they all face is how to effectively handle the exponential number of paths in checked code. This paper presents a new technique for reducing the number of traversed code paths by discarding those that must have side-effects identical to some previously explored path. Our results on a mix of open source applications and device drivers show that this (sound) optimization reduces the numbers of paths traversed by several orders of magnitude, often achieving program coverage far out of reach for a standard constraint-based execution system. In this letter, we consider the problem of rendering robotic tasks persistent by ensuring that the robots' energy levels are never depleted, which means that the tasks can be executed over long time horizons. This process is referred to as the persistification of the task. In particular, the state of each robot is augmented with its battery level so that the desired persistent behavior can be encoded as the forward invariance of a set such that the robots never deplete their batteries. Control barrier functions are employed to synthesize controllers that ensure that this set is forward invariant and, therefore, that the robotic task is persistent. As an application, this letter considers the persistification of a robotic sensor coverage task in which a group of robots has to cover an area of interest. The successful persistification of the coverage task is shown in simulation and on a team of mobile robots. The WordNet lexical database is now quite large and offers broad coverage of general lexical relations in English. As is evident in this volume, WordNet has been employed as a resource for many applications in natural language processing (NLP) and information retrieval (IR). However, many potentially useful lexical relations are currently missing from WordNet. Some of these relations, while useful for NLP and IR applications, are not necessarily appropriate for a general, domain-independent lexical database. For example, WordNet’s coverage of proper nouns is rather sparse, but proper nouns are often very important in application tasks. The standard way lexicographers find new relations is to look through huge lists of concordance lines. However, culling through long lists of concordance lines can be a rather daunting task (Church and Hanks, 1990), so a method that picks out those lines that are very likely to hold relations of interest should be an improvement over more traditional techniques. This chapter describes a method for the automatic discovery of WordNetstyle lexico-semantic relations by searching for corresponding lexico-syntactic patterns in large text collections. Large text corpora are now widely available, and can be viewed as vast resources from which to mine lexical, syntactic, and semantic information. This idea is reminiscent of what is known as “data mining” in the artificial intelligence literature (Fayyad and Uthurusamy, 1996), however, in this case the ore is raw text rather than tables of numerical data. The Lexico-Syntactic Pattern Extraction (LSPE) method is meant to be useful as an automated or semi-automated aid for lexicographers and builders of domain-dependent knowledge-bases. The LSPE technique is light-weight; it does not require a knowledge base or complex interpretation modules in order to suggest new WordNet relations. In this paper, we study the problem of moving @math n sensors on a line to form a barrier coverage of a specified segment of the line such that the maximum moving distance of the sensors is minimized. Previously, it was an open question whether this problem on sensors with arbitrary sensing ranges is solvable in polynomial time. We settle this open question positively by giving an @math O(n2logn) time algorithm. For the special case when all sensors have the same-size sensing range, the previously best solution takes @math O(n2) time. We present an @math O(nlogn) time algorithm for this case; further, if all sensors are initially located on the coverage segment, our algorithm takes @math O(n) time. Also, we extend our techniques to the cycle version of the problem where the barrier coverage is for a simple cycle and the sensors are allowed to move only along the cycle. For sensors with the same-size sensing range, we solve the cycle version in @math O(n) time, improving the previously best @math O(n2) time solution. In their seminal work on homological sensor networks, de Silva and Ghrist showed the surprising fact that it's possible to certify the coverage of a coordinate-free sensor network even with very minimal knowledge of the space to be covered. Here, coverage means that every point in the domain (except possibly those very near the boundary) has a nearby sensor. More generally, their algorithm takes a pair of nested neighborhood graphs along with a labeling of vertices as either boundary or interior and computes the relative homology of a simplicial complex induced by the graphs. This approach, called the Topological Coverage Criterion (TCC), requires some assumptions about the underlying geometric domain as well as some assumptions about the relationship of the input graphs to the domain. The goal of this paper is to generalize these assumptions and show how the TCC can be applied to both much more general domains as well as very weak assumptions on the input. We give a new, simpler proof of the de Silva-Ghrist Topological Coverage Criterion that eliminates any assumptions about the smoothness of the boundary of the underlying space, allowing the results to be applied to much more general problems. The new proof factors the geometric, topological, and combinatorial aspects, allowing us to provide a coverage condition that supports thick boundaries, k-coverage, and weighted coverage, in which sensors have varying radii.