Balanced task allocation by partitioning the multiple traveling salesperson problem

Task assignment and routing are tightly coupled problems for teams of mobile agents. To fairly balance the workload, each agent should be assigned a set of tasks which take a similar amount of time to complete. The completion time depends on the time needed to travel between tasks which depends on the order of tasks. We formulate the task assignment problem as the minimum Hamiltonian partition problem (MHPP) form agents, which is equivalent to the minmax multiple traveling salesperson problem (m-TSP). While the MHPP’s cost function depends on the order of tasks, its solutions are similar to solutions of the average Hamiltonian partition problem (AHPP) whose cost function is order-invariant. We prove that the AHPP is NP-hard and present an effective heuristic, AHP, for solving it. AHP improves a partitions of a graph using a series of transfer and swap operations which are guaranteed to improve the solution’s quality. The solution generated by AHP is used as an initial partition for an algorithm, AHP-mTSP, which solves the combined task assignment and routing problems to near optimality. For n tasks and m agents, each iteration of AHP is O(n2) and AHP-mTSP has an average run-time that scales with n2.11m0.33. Compared to state-of-the-art approaches, our approach found approximately 10% better solutions for large problems in a similar run-time

Re-establishing communication in teams of mobile robots

As communication is important for cooperation, teams of mobile robots need a way to re-establish a wireless connection if they get separated. We develop a method for mobile robots to maintain a belief of each other's positions using locally available information. They can use their belief to plan paths with high probabilities of reconnection. This approach also works for subteams cooperatively searching for a robot or group of robots that they would like to reconnect with. The problem is formulated as a constrained optimization problem which is solved using a branch-and-bound approach. We present simulation results showing the effectiveness of this strategy at reconnecting teams of up to five robots and compare the results to two other strategies

Особенности плазмохимического травления торцов кремниевых пластин для фотоэлектрических преобразователей

Выбраны оптимальные режимы плазмохимического травления торцов пластин в реакторе, разработанном в ИЯИ, который по производительности превосходит лучший зарубежный аналог при более высоком качестве обработки пластин

VICE: Variational Interpretable Concept Embeddings

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in an odd-one-out triplet task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine sufficient sample size in experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the odd-one-out triplet task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations

Turn-minimizing multirobot coverage

Document Sections I. Introduction II. Partitioning the Environment III. Combining Ranks Into Paths IV. Results V. Conclusions Authors Figures References Keywords Metrics Abstract: Multirobot coverage is the problem of planning paths for several identical robots such that the combined regions traced out by the robots completely cover their environment. We consider the problem of multirobot coverage with the objective of minimizing the mission time, which depends on the number of turns taken by the robots. To solve this problem, we first partition the environment into ranks which are long thin rectangles the width of the robot's coverage tool. Our novel partitioning heuristic produces a set of ranks which minimizes the number of turns. Next, we solve a variant of the multiple travelling salesperson problem (m-TSP) on the set of ranks to minimize the robots' mission time. The resulting coverage plan is guaranteed to cover the entire environment. We present coverage plans for a robotic vacuum using real maps of 25 indoor environments and compare the solutions to paths planned without the objective of minimizing turns. Turn minimization reduced the number of turns by 6.7% and coverage time by 3.8% on average for teams of 1-5 robots

Coherent Pion Radiation From Nucleon Antinucleon Annihilation

A unified picture of nucleon antinucleon annihilation into pions emerges from a classical description of the pion wave produced in annihilation and the subsequent quantization of that wave as a coherent state. When the constraints of energy-momentum and iso-spin conservation are imposed on the coherent state, the pion number distribution and charge ratios are found to be in excellent agreement with experiment.Comment: LaTex, 8 text pages, 1 PostScript figure, PSI-PR-93-2

Consensus on Aquatic Primary Productivity Field Protocols for Satellite Validation and Model Synthesis

The NASA PACE project, in conjunction with the IOCCG, EUMETSAT, and JAXA, have initiated an Aquatic Primary Productivity working group, with the aim to develop community consensus on multiple methods for measuring aquatic primary productivity used for satellite validation and model synthesis. A workshop to commence the working group efforts was held December 05-07, 2018 at the University Space Research Association headquarters in Columbia, MD U.S.A., bringing together 26 active researchers from 16 institutions. The group discussed the primary differences, nuances, scales, uncertainties, definitions, and best practices for measurements of primary productivity derived from in situ/on-deck/laboratory radio/stable isotope incubations, dissolved oxygen concentrations (from incubations or autonomous platforms such as floats or gliders), oxygen-argon ratios, triple oxygen isotope, natural fluorescence, and FRRF/ETR/kinetic analysis. These discussions highlighted the necessity to move the community forward towards the establishment of climate-quality primary productivity measurements that follow uniform protocols, which is imperative to ensure that existing and future measurements can be compared, assimilated, and their uncertainties determined for model development and validation. The specific deliverable resulting from of this activity will be a protocol document, published in coordination with the IOCCG. This presentation will discuss the findings of the meeting, and address future activities of the working group

Modular fluidic propulsion robots

We propose a novel concept for modular robots, termed modular fluidic propulsion (MFP), which promises to combine effective propulsion, a large reconfiguration space, and a scalable design. MFP robots are modular fluid networks. To propel, they route fluid through themselves. In this article, both hydraulic and pneumatic implementations are considered. The robots move towards a goal by way of a decentralized controller that runs independently on each module face, uses two bits of sensory information and requires neither run-time memory, nor communication. We prove that 2-D MFP robots reach the goal when of orthogonally convex shape, or reach a morphology-dependent distance from it when of arbitrary shape. We present a 2-D hydraulic MFP prototype and show, experimentally, that it succeeds in reaching the goal in at least 90% of trials, and that 71% less energy is expended when modules can communicate. Moreover, in simulations with 3-D hydraulic MFP robots, the decentralized controller performs almost as well as a state-of-the-art and centralized controller. Given the simplicity of the hardware requirements, the MFP concept could pave the way for modular robots to be used at sub-centimeter-scale, where effective modular propulsion systems have not been demonstrated

SHREC 2011: robust feature detection and description benchmark

Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and strength of the transformations that can be dealt with. The present paper is a report of the SHREC'11 robust feature detection and description benchmark results.Comment: This is a full version of the SHREC'11 report published in 3DO