Adaptive action selection without explicit communication formultirobot box-pushing

The paper describes a novel action selection method for multiple mobile robots box-pushing in a dynamic environment. The robots are designed to need no explicit communication and be adaptive to dynamic environments by changing modules of behavior. The various control methods for a multirobot system have been studied both in centralized and decentralized approaches, however, they needed explicit communication such as a radio, though such communication is expensive and unstable. Furthermore, though it is a significant issue to develop adaptive action selection for a multirobot system to a dynamic environment, few studies have been done on it. Thus, we propose action selection without explicit communication for multirobot box-pushing which changes a suitable behavior set depending on a situation for adaptation to a dynamic environment. First, four situations are defined with two parameters: the existence of other robots and the task difficulty. Next, we propose an architecture of action selection which consists of a situation recognizer and sets of suitable behaviors to the situations and carefully design the suitable behaviors for each of the situations. Using the architecture, a mobile robot recognizes the current situation and activates the suitable behavior set to it. Then it acts with a behavior-based approach using the activated behaviors and can change the current situation when the environment changes. We fully implement our method on four real mobile robots and conduct various experiments in dynamic environments. As a result, we find out our approach is promising for designing adaptive multirobot box-pushing     

Multiple Traveling Robot Problem: A Solution Based on Dynamic Task Selection and Robust Execution

The multiple traveling robot problem (MTRP), the real-world version of the well-known NP-hard multiple traveling salesman problem (MTSP), asks for finding routes of robots to visit a set of targets. Various objectives may be defined for this problem (e.g., minimization of total path length, time, etc.). The overall solution quality is dependent on both the quality of the solution constructed by the paths of robots and the efficient allocation of the targets to robots. Unpredictability of the exact processing times of tasks, unstable cost values during execution, and inconsistencies due to uncertain information further complicate MTRP. This paper presents a multirobot cooperation framework employing a dynamic task selection scheme to solve MTRP. The proposed framework carries out an incremental task allocation method that dynamically adapts to current conditions of the environment, thus handling diverse contingencies. Globally efficient solutions are obtained through mechanisms that result in the allocation of the most suitable tasks from dynamically generated priority-based rough schedules. Since the presented approach is for real-world task execution, computational requirements are kept at a minimum, and the framework is designed to be applicable on real robots even with limited capabilities. The efficiency and the robustness of the proposed scheme is evaluated through experiments both in simulations and on real robots.     

Cooperation Issues and Distributed Sensing for Multirobot Systems

This paper considers the properties a multirobot system should exhibit to perform an assigned task cooperatively. Our experiments regard specifically the domain of RoboCup middle-size league (MSL) competitions. But the illustrated techniques can be usefully applied also to other service robotics fields like, for example, videosurveillance. Two issues are addressed in the paper. The former refers to the problem of dynamic role assignment in a team of robots. The latter concerns the problem of sharing the sensory information to cooperatively track moving objects. Both these problems have been extensively investigated over the past years by the MSL robot teams. In our paper, each individual robot has been designed to become reactively aware of the environment configuration. In addition, a dynamic role assignment policy among teammates is activated, based on the knowledge about the best behavior that the team is able to acquire through the shared sensorial information. We present the successful performance of the Artisti Veneti robot team at the MSL Challenge competitions of RoboCup-2003 to show the effectiveness of our proposed hybrid architecture, as well as some tests run in laboratory to validate the omnidirectional distributed vision system which allows us to share the information gathered by the omnidirectional cameras of our robots.     

Utilizing cluster analysis to structure concurrent engineeringteams

The problem of structuring a concurrent engineering team was studied. This research considered various mathematical clustering approaches to group product development design tasks together, and then constructed cross-functional teams based on the task clusters formed from each approach, Resultant team structures were evaluated against each other, and against a traditional discipline-centered hierarchical structure. The goal of this effort was to develop a structuring methodology for concurrent engineering teams that would allow projects to be completed faster, and with a lower risk of project failure. Team structures were developed using alternative clustering techniques and different combinations of data as inputs into the clustering techniques. Clustering approaches included single linkage, complete linkage, average linkage, the centroid method, and Ward's method. Data sources were from the initial stages of product development, and included task risk levels, task precedence relationships, disciplines required, personnel available, task technical importance, task difficulty, task priority, component requirement interactions, and projected communication levels between design tasks. Additional analysis was done on the effects of multiteam assignments for critical personnel. Team structures developed using the average linkage clustering approach and a data set composed of projected communication levels between tasks and discipline requirements for each design task were found to support the development of shorter duration projects with lower risk levels     

基于多核处理器的节能任务调度方法

由于关系到系统的安全性及散热代价等方面,能耗问题已经成为嵌入式系统研究的重点。对于多核处理器上具有依赖关系的周期性硬实时任务,设计了一种基于动态电压调节的节能任务调度方法。该方法首先用RDAG算法将任务独立化,然后以功耗最低为原则,采用遗传算法确定任务映射。基于Intel PXA270功耗模型,采用了几个随机任务集进行仿真实验,结果表明该方法比现有的方法节省了20%～30%的能耗。     

An autonomous Wireless Networked Robotics System for backbone deployment in highly-obstructed environments

A Wireless Networked Robotics System can assist in settings that lack infrastructure e.g., urban search and rescue. A team of networked mobile robots can provide a communication substrate in those settings by acting as routers in a wireless mesh network. We study the problem of deploying a few mobile robots, and how to position them, so that all clients using the resulting robotic network are connected and all network links satisfy minimum rate requirements. The key challenge we address is that in an environment with obstacles the strength of a wireless link is a non-monotonic function of the distance between the link end-points. The problem is thus fundamentally one of making router placement decisions in a non-metric space. Our approach to the problem is based on virtual potential fields. Clients and environmental obstacles are modeled as virtual charged particles exerting virtual forces on the robots. We validate our algorithm with physical robots in an indoor environment and demonstrate that we are able to get feasible solutions.     

Combining replication and checkpointing redundancies for reducing resiliency overhead

We herein propose a heuristic redundancy selection algorithm that combines resubmission, replication, and checkpointing redundancies to reduce the resiliency overhead in fault‐tolerant workflow scheduling. The appropriate combination of these redundancies for workflow tasks is obtained in two consecutive phases. First, to compute the replication vector (number of task replicas), we apportion the set of provisioned resources among concurrently executing tasks according to their needs. Subsequently, we obtain the optimal checkpointing interval for each task as a function of the number of replicas and characteristics of tasks and computational environment. We formulate the problem of obtaining the optimal checkpointing interval for replicated tasks in situations where checkpoint files can be exchanged among computational resources. The results of our simulation experiments, on both randomly generated workflow graphs and real‐world applications, demonstrated that both the proposed replication vector computation algorithm and the proposed checkpointing scheme reduced the resiliency overhead.     

Spatially constrained coordinated navigation for a multi-robot system

In this paper we present a method for navigating a multi-robot system through an environment while additionally maintaining a predefined set of constraints. Examples for constraints are the requirement to maintain a direct line-of-sight between robots or to ensure that the multi-robot system maintains communication. Our approach is based on graph structures that model movements and constraints separately, in order to cover different kind of robots and a large class of possible constraints. Additionally, the separation of movement and constraint graph allows us to use known graph algorithms like Steiner tree heuristics or the multi-point relay algorithm to solve the problem of finding a target configuration for the robots. To connect the movements of the robots with the given constraints, we introduce separated connection graphs which allow assembling valid navigation plans fast. This paper presents some theoretical insight into the problem of coordinated navigation for multi-robot systems with spatial constraints as well as a practical solution. Experiments in simulation and with real robots show the feasibility of the approach.     

Controlling mobile robots in distributed intelligent sensor network

Mobile robots need sufficient sensors and information on the environment in order to navigate. In this paper, we propose a system of mobile robots, which is controlled in a distributed intelligent sensor network. In such a networked space, the environment is divided by distributed sensors. Each area is monitored by a distributed sensor device, which connects with other distributed sensor devices and robots throughout the network. As a result, the mobile robots are able to accomplish tasks simply by following orders from the sensor devices in the networked environment, although the mobile robots are not self-contained with information on the environment and sensors for self-positioning and control. We test several situations to verify the proposed system.     

Coverage, Exploration and Deployment by a Mobile Robot and Communication Network

We consider the problem of coverage and exploration of an unknown dynamic environment using a mobile robot. The environment is assumed to be large enough such that constant motion by the robot is needed to cover the environment. We present an efficient minimalist algorithm which assumes that global information is not available (neither a map, nor GPS). Our algorithm deploys a network of radio beacons which assists the robot in coverage. The network is also used by the robot for navigation. The deployed network can also be used for applications other than coverage (such as multi-robot task allocation). Simulation experiments are presented which show the collaboration between the deployed network and mobile robot for the tasks of coverage/exploration, network deployment and maintenance (repair), and mobile robot recovery (homing behavior). We discuss a theoretical basis for our algorithm on graphs and show the results of the simulated scenario experiments.     

A generalized approach for the acceleration and deceleration ofindustrial robots and CNC machine tools

Many techniques for the acceleration and deceleration of industrial robots and computer numerical control (CNC) machine tools have been proposed in order to make industrial robots and CNC machine tools perform given tasks efficiently. Although the techniques selecting polynomial functions can generate various acceleration and deceleration characteristics, the major problem is the computational load. The digital convolution techniques are more efficient than the techniques selecting polynomial functions. However, neither velocity profiles of which the deceleration characteristics is independent from the acceleration characteristics nor those of which the acceleration interval is different from the deceleration interval can be generated by the digital convolution techniques. This paper proposes a generalized approach for generating velocity profiles that cannot be generated by the digital convolution techniques. According to the desired characteristics of acceleration and deceleration, each set of coefficients is calculated and is stored. Given a moving distance, and acceleration and deceleration intervals, a velocity profile having the desired characteristics of acceleration and deceleration can be efficiently generated by using these coefficients. Several velocity profiles generated by the proposed technique are applied to one single-axis control system     

RFID-based mobile robot guidance to a stationary target

Retrieving accurate location information about an object in real-time, as well as any general information pertinent to the object, is a key to enabling a robot to perform a task in cluttered, dynamically changing environment. In this paper, we address a novel technique for the guidance of mobile robots to help them identify, locate, and approach a target in our daily environments. To this end, we propose a standard for the use of radio-frequency identification (RFID) systems and develop a prototype that can be easily installed in the existing mobile robots.Specifically, when an RF signal is transmitted from an RF transponder, the proposed RFID system reads the transponder-encoded data and simultaneously picks up the direction of the transponder using the received signal strength pattern. Based on the angle of signal arrival, we develop the guidance strategies that enable a robot to find its way to the transponder position. Moreover, to cope with multi-path reflection and unexpected distortions of the signals that resulted from environmental effects, we present several algorithms for reconstructing the signals. We demonstrate that an off-the-self mobile robot equipped with the proposed system locates and approaches a stationary target object. Experimental results show that the accuracy of the proposed system operating at a frequency of 315 MHz falls within a reasonable range in our normal office environment.     

Task offloading based on deep learning for blockchain in mobile edge computing

Blockchain is an advanced technique to realize smart contracts, various transactions, and P2P crypto-currencies in the e-commerce society. However, the traditional blockchain does not consider a mobile environment to design a data offloading of the blockchain such that the blockchain results in high computational cost and huge data propagation delay. In this paper, to remedy the above problem, we propose a scalable blockchain and a task offloading technique based on the neural network of the mobile edge computing scenario. Experimental results show that our approach is very scalable in the mobile scenario.

     

Static Compiler Analyses for Application-specific Optimization of Task-Parallel Runtime Systems

Achieving high performance in task-parallel runtime systems, especially with high degrees of parallelism and fine-grained tasks, requires tuning a large variety of behavioral parameters according to program characteristics. In the current state of the art, this tuning is generally performed in one of two ways: either by a group of experts who derive a single setup which achieves good – but not optimal – performance across a wide variety of use cases, or by monitoring a system’s behavior at runtime and responding to it. The former approach invariably fails to achieve optimal performance for programs with highly distinct execution patterns, while the latter induces overhead and cannot affect parameters which need to be set at compile time. In order to mitigate these drawbacks, we propose a set of novel static compiler analyses specifically designed to determine program features which affect the optimal settings for a task-parallel execution environment. These features include the parallel structure of task spawning, the granularity of individual tasks, the memory size of the closure required for task parameters, and an estimate of the stack size required per task. Based on the result of these analyses, various runtime system parameters are then tuned at compile time. We have implemented this approach in the Insieme compiler and runtime system, and evaluated its effectiveness on a set of 12 task parallel benchmarks running with 1 to 64 hardware threads. Across this entire space of use cases, our implementation achieves a geometric mean performance improvement of 39%. To illustrate the impact of our optimizations, we also provide a comparison to current state-of-the art task-parallel runtime systems, including OpenMP, Cilk, HPX, and Intel TBB.

     

Frugal incentive mechanism in periodic mobile crowdsensing

Nowadays, numerous incentive mechanisms of mobile crowdsensing have been designed to attract extensive user participation, but most of these mechanisms focus only on independent task scenarios, where the sensing tasks are independent of each other. On the contrary, we focus on a periodical task scenario, where each user participates in the same type of sensing tasks periodically. In this paper, we consider the long‐term user participation incentive in a general periodical mobile crowdsensing system from a frugality payment perspective. We explore the issue under both semi‐online (the intraperiod interactive process is synchronous while the interperiod interactive process is sequential and asynchronous during each period) and online user arrival models (the previous 2 interactive processes are sequential and asynchronous). In particular, we first propose a semi‐online frugal incentive mechanism by introducing a Lyapunov method. Moreover, we also extend it to an online frugal incentive mechanism, which satisfies the long‐term participation constraint and approximate optimality. Finally, extensive simulations show that our mechanisms satisfy the above theoretical properties.     

Incremental Coevolution With Competitive and Cooperative Tasks in a Multirobot Environment

Coevolution has been receiving increased attention as a method for simultaneously developing the control structures of multiple agents. Our ultimate goal is the mutual development of skills through coevolution. The coevolutionary process is, however, often prone to settle into suboptimal strategies. The key to successful coevolution has thus far been unclear. This paper discusses how several robots can emerge cooperative and competitive behavior through coevolutionary processes. In order to realize successful coevolution, we propose two ideas: multiple schedules for incremental evolution and fitness sharing based on the method of importance sampling. To examine this issue, we conducted a series of computer simulations. We have chosen a simplified soccer game consisting of two or three robots as a testbed for analyzing a problem in which both competitive and cooperative tasks are involved. We show that the proposed fitness evaluation allows robots to evolve robust behaviors in cooperative and competitive situations.     

Interoperable vision component for object detection and 3D pose estimation for modularized robot control

Finding objects and tracking their poses are essential functions for service robots, in order to manipulate objects and interact with humans. We present novel algorithms for local feature matching for object detection, and 3D pose estimation. Our feature matching algorithm takes advantage of local geometric consistency for better performance, and the new 3D pose estimation algorithm solves the pose in a closed-form using homography, followed by a non-linear optimization step for stability. Advantages of our approach include better performance, minimal prior knowledge for the target pattern, and easy implementation and portability as a modularized software component. We have implemented our approach along with both CPU and GPU-based feature extraction, and built an interoperable component that can be used in any Robot Technology (RT)-based control system. Experiment shows that our approach produces very robust results for the estimated 3D pose, and maintain very low false positive rate. It is also fast enough to be used in on-line applications. We integrated our vision component in an autonomous robot system with a search-and-grasp task, and tested it with several objects that are found in ordinary domestic environment. We present the details of our approach, the design of our modular component design, and the results of the experiments in this paper.     

Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing

An efficient task scheduling approach shows promising way to achieve better resource utilization in cloud computing. Various task scheduling approaches with optimization and decision‐making techniques have been discussed up to now. These approaches ignored scheduling conflict among the similar tasks. The conflict often leads to miss the deadlines of the tasks. The work studies the implementation of the MCDM (multicriteria decision‐making) techniques in backfilling algorithm to execute deadline‐based tasks in cloud computing. In general, the tasks are selected as backfill tasks, whose role is to provide ideal resources to other tasks in the backfilling approach. The selection of the backfill task is challenging one, when there are similar tasks. It creates conflict in the scheduling. In cloud computing, the deadline‐based tasks have multiple parameters such as arrival time, number of VMs (virtual machines), start time, duration of execution, and deadline. In this work, we present the deadline‐based task scheduling algorithm as an MCDM problem and discuss the MCDM techniques: AHP (Analytical Hierarchy Process), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to avoid similar task scheduling conflicts. We simulate the backfilling algorithm along with three MCDM mechanisms to avoid scheduling conflicts among the similar tasks. The synthetic workloads are considered to study the performance of the proposed scheduling algorithm. The mechanism suggests an efficient VM allocation and its utilization for deadline‐based tasks in the cloud environment.     

Autonomic mobile sensor network with self-coordinated task allocation and execution

This paper describes a distributed layered architecture for resource-constrained multirobot cooperation, which is utilized in autonomic mobile sensor network coverage. In the upper layer, a dynamic task allocation scheme self-organizes the robot coalitions to track efficiently across regions. It uses concepts of ant behavior to self-regulate the regional distributions of robots in proportion to that of the moving targets to be tracked in a nonstationary environment. As a result, the adverse effects of task interference between robots are minimized and network coverage is improved. In the lower task execution layer, the robots use self-organizing neural networks to coordinate their target tracking within a region. Both layers employ self-organization techniques, which exhibit autonomic properties such as self-configuring, self-optimizing, self-healing, and self-protecting. Quantitative comparisons with other tracking strategies such as static sensor placements, potential fields, and auction-based negotiation show that our layered approach can provide better coverage, greater robustness to sensor failures, and greater flexibility to respond to environmental changes.