Sequencing of multi‑robot behaviors using reinforcement learning

Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem, combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.     

Survivable millimeter-wave mesh networks

Millimeter-wave mesh networks have the potential to provide cost-effective high-bandwidth solutions to many current bandwidth-constrained networks including cellular backhaul. However, the availability of such networks is severely limited due to their susceptibility to weather, such as precipitation and humidity. In this paper, we present a rigorous approach to survivable millimeter-wave mesh networks based on experimentation, modeling, and simulation. Individual link performance is characterised using frame error-rate measurements from millimeter-wave transmissions on test links over a period of one year. A geometric model based on radar-reflectivity data is used to characterise rain storms and determine their impact on spatially correlated links of a mesh network. To mitigate the impact of link impairments on network services, we present two cross-layered routing protocols to route around the failures: P-WARP (predictive weather-assisted routing protocol) and XL-OSPF (cross-layered open shortest-path first). We conduct a performance analysis of the proposed mesh network under the presence of actual weather events as recorded by the US National Weather Service. Results show that the proposed approach yields the highest dependability when compared against existing routing methods.     

A New Difficulty Index for Teleoperated Robots Driving through Obstacles

Teleoperation allows humans to reach environments that would otherwise be too difficult or dangerous. The distance between the human operator and remote robot introduces a number of issues that can negatively impact system performance including degraded and delayed information exchange between the robot and human. Some operation scenarios and environments can tolerate these degraded conditions, while others cannot. However, little work has been done to investigate how factors such as communication delay, automation, and environment characteristics interact to affect teleoperation system performance. This paper presents results from a user study analyzing the effects of teleoperation factors including communication delay, autonomous assistance, and environment layout on user performance. A mobile robot driving task is considered in which subjects drive a robot to a goal location around obstacles as quickly (minimize time) and safely (avoid collisions) as possible. An environment difficulty index (ID) is defined in the paper and is shown to be able to predict the average time it takes for the human to drive the robot to a goal location with different obstacle configurations. The ID is also shown to predict the path chosen by the human better than travel time along that path.     

On matrices, automata, and double counting in constraint programming

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables $\mathcal{M}$ , with the same constraint C defined by a finite-state automaton $\mathcal{A}$ on each row of $\mathcal{M}$ and a global cardinality constraint $\mathit{gcc}$ on each column of $\mathcal{M}$ . We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the $\mathit{gcc}$ constraints from the automaton $\mathcal{A}$ . The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We also provide a domain consistency filtering algorithm for the conjunction of lexicographic ordering constraints between adjacent rows of $\mathcal{M}$ and (possibly different) automaton constraints on the rows. We evaluate the impact of our methods in terms of runtime and search effort on a large set of nurse rostering problem instances.     

Formation of a geometric pattern with a mobile wireless sensor network

Mobile wireless sensor networks (MWSNs) will enable information systems to gather detailed information about the environment on an unprecedented scale. These self‐organizing, distributed networks of sensors, processors, and actuators that are capable of movement have a broad range of potential applications, including military reconnaissance, surveillance, planetary exploration, and geophysical mapping. In many of the foreseen applications, the MWSN will need to form a geometric pattern without assistance from the user. In military reconnaissance, for example, the nodes will be dropped onto the battlefield from a plane and land at random positions. The nodes will be expected to arrange themselves into a predetermined formation in order to perform a specific task. Thus, we present algorithms for forming a line, circle, and regular polygon from a given set of random positions. The algorithms are distributed and use no communication between the nodes to minimize energy consumption. Unlike past studies of geometric problems where algorithms are either tested in simulations where each node has global knowledge of all the other nodes or implemented on a small number of robots, the robustness of our algorithms has been studied with simulations that model the sensor system in detail. The simulations demonstrate that the algorithms are robust against random errors in the sensors and actuators. © 2004 Wiley Periodicals, Inc.     

Multifidelity surrogate modeling based on radial basis functions

Multiple models of a physical phenomenon are sometimes available with different levels of approximation. The high fidelity model is more computationally demanding than the coarse approximation. In this context, including information from the lower fidelity model to build a surrogate model is desirable. Here, the study focuses on the design of a miniaturized photoacoustic gas sensor which involves two numerical models. First, a multifidelity metamodeling method based on Radial Basis Function, the co-RBF, is proposed. This surrogate model is compared with the classical co-kriging method on two analytical benchmarks and on the photoacoustic gas sensor. Then an extension to the multifidelity framework of an already existing RBF-based optimization algorithm is applied to optimize the sensor efficiency. The co-RBF method does not bring better results than co-kriging but can be considered as an alternative for multifidelity metamodeling.     

A constrained binary knapsack approximation for shortest path network interdiction

A modified shortest path network interdiction model is approximated in this work by a constrained binary knapsack which uses aggregated arc maximum flow as the objective function coefficient. In the modified shortest path network interdiction problem, an attacker selects a path of highest non-detection probability on a network with multiple origins and multiple available targets. A defender allocates a limited number of resources within the geographic region of the network to reduce the maximum network non-detection probability between all origin-target pairs by reducing arc non-detection probabilities and where path non-detection probability is modeled as a product of all arc non-detection probabilities on that path. Traditional decomposition methods to solve the shortest path network interdiction problem are sensitive to problem size and network/regional complexity. The goal of this paper is to develop a method for approximating the regional allocation of defense resources that maintains accuracy while reducing both computational effort and the sensitivity of computation time to network/regional properties. Statistical and spatial analysis methods are utilized to verify approximation performance of the knapsack method in two real-world networks.     

Discovery of Intrinsic Primitives on Triangle Meshes

The discovery of meaningful parts of a shape is required for many geometry processing applications, such as parameterization, shape correspondence, and animation. It is natural to consider primitives such as spheres, cylinders and cones as the building blocks of shapes, and thus to discover parts by fitting such primitives to a given surface. This approach, however, will break down if primitive parts have undergone almost‐isometric deformations, as is the case, for example, for articulated human models. We suggest that parts can be discovered instead by finding intrinsic primitives, which we define as parts that posses an approximate intrinsic symmetry. We employ the recently‐developed method of computing discrete approximate Killing vector fields (AKVFs) to discover intrinsic primitives by investigating the relationship between the AKVFs of a composite object and the AKVFs of its parts. We show how to leverage this relationship with a standard clustering method to extract k intrinsic primitives and remaining asymmetric parts of a shape for a given k. We demonstrate the value of this approach for identifying the prominent symmetry generators of the parts of a given shape. Additionally, we show how our method can be modified slightly to segment an entire surface without marking asymmetric connecting regions and compare this approach to state‐of‐the‐art methods using the Princeton Segmentation Benchmark.     

Punctuated simplification of man-made objects

We present a simplification algorithm for manifold polygonal meshes of plane-dominant models. Models of this type are likely to appear in man-made environments. While traditional simplification algorithms focus on generality and smooth meshes, the approach presented here considers a specific class of man-made models. By detecting and classifying edge loops on the mesh and providing a guided series of binary mesh partitions, our approach generates a series of simplified models, each of which better respects the semantics of these kinds of models than conventional approaches do. A guiding principle is to eliminate simplifications that do not make sense in constructed environments. This, coupled with the concept of “punctuated simplification”, leads to an approach that is both efficient and delivers high visual quality. Comparative results are given.