Path In Time Research Articles

Development of a Hybrid Path Planning Algorithm and a Bio-Inspired Control for an Omni-Wheel Mobile Robot.

This research presents a control structure for an omni-wheel mobile robot (OWMR). The control structure includes the path planning module and the motion control module. In order to secure the robustness and fast control performance required in the operating environment of OWMR, a bio-inspired control method, brain limbic system (BLS)-based control, was applied. Based on the derived OWMR kinematic model, a motion controller was designed. Additionally, an optimal path planning module is suggested by combining the advantages of A* algorithm and the fuzzy analytic hierarchy process (FAHP). In order to verify the performance of the proposed motion control strategy and path planning algorithm, numerical simulations were conducted. Through a point-to-point movement task, circular path tracking task, and randomly moving target tracking task, it was confirmed that the suggesting motion controller is superior to the existing controllers, such as PID. In addition, A*–FAHP was applied to the OWMR to verify the performance of the proposed path planning algorithm, and it was simulated based on the static warehouse environment, dynamic warehouse environment, and autonomous ballet parking scenarios. The simulation results demonstrated that the proposed algorithm generates the optimal path in a short time without collision with stop and moving obstacles.

SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study

The paper presents advanced computational solutions for selected sectors in the context of the optimization of technology processes as an innovation and progress in improving energy efficiency of smart cities. The main emphasis was placed on the sectors of critical urban infrastructure, including in particular the use of algorithmic models based on artificial intelligence implemented in supervisory control systems (SCADA-type, including Virtual SCADA) of technological processes involving the sewage treatment systems (including in particular wastewater treatment systems) and waste management systems. The novelty of the presented solution involves the use of predictive diagnostic tools, based on multi-threaded polymorphic models supporting decision making processes during the control of a complex technological process and objects of distributed network systems (smart water grid, smart sewage system, smart waste management system) and solving problems of optimal control for smart dynamic objects with logical representation of knowledge about the process, the control object and the control itself, for which the learning process consists of successive validation and updating of knowledge and the use of the results of this updating to make control decisions. The advantage of the proposed solution in relation to the existing ones lies in the use of advanced models of predictive diagnostics, validation and reconstruction of data, implemented in functional tools, allowing the stabilization of the work of technological objects through the use of FTC technology (fault tolerant control) and soft sensors, predictive measurement path diagnostics (sensors, transducers), validation and reconstruction of measurement data from sensors in the measuring paths in real time. The dedicated tools (Intelligent Real Time Diagnostic System − iRTDS) built into the system of a hierarchical, multi-threaded control optimizing system of SCADA system allow to obtain advanced diagnostics of technological processes in real time using HPC technology. In effect of the application of the proprietary iRTDS tool, we obtain a significant rise of energy efficiency of technological processes in key sectors of the economy, which in global terms, e.g., urban agglomeration, increases the economic efficiency.

An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments

The methods of task assignment and path planning have been reported by many researchers, but they are mainly focused on environments with prior information. In unknown dynamic environments, in which the real-time acquisition of the location information of obstacles is required, an integrated multi-robot dynamic task assignment and cooperative search method is proposed by combining an improved self-organizing map (SOM) neural network and the adaptive dynamic window approach (DWA). To avoid the robot oscillation and hovering issue that occurs with the SOM-based algorithm, an SOM neural network with a locking mechanism is developed to better realize task assignment. Then, in order to solve the obstacle avoidance problem and the speed jump problem, the weights of the winner of the SOM are updated by using an adaptive DWA. In addition, the proposed method can search dynamic multi-target in unknown dynamic environment, it can reassign tasks and re-plan searching paths in real time when the location of the targets and obstacle changes. The simulation results and comparative testing demonstrate the effectiveness and efficiency of the proposed method.

Global Path Planning and Path-Following for Wheeled Mobile Robot Using a Novel Control Structure Based on a Vision Sensor

This paper presents a novel design for the kinematic control structure of the wheeled mobile robot (WMR) path planning and path-following. The proposed system is focused on the implementation of practical real-time model-free algorithms based on visual servoing. The mainframe of this study is to implement a novel kinematic control structure based on visual sevoing and hybrid algorithms in real-time mobile robot applications. First, the structure of the proposed algorithm based on the visual information extracted from an overhead camera has been addressed. Then, the classification process of robot position and orientation, target, and obstacles has been addressed. Second, the path planning algorithms’ initial parameters and obstacles-free path coordinates have been determined by visual information extracted from images in real time. In this step, the interval type-2 fuzzy inference (IT2FIS) algorithm and various algorithms used in path planning have been compared and their performances have been analyzed. The third stage handled the path-following process using a novel control structure for keeping up the robot on the generated path. In this step, the proposed approach is compared with fuzzy Type-1/Type-2 and fuzzy-PID control algorithms, and their results have been analyzed statistically. The proposed system has been successfully implemented on several maps. The experimental results show that the developed design is valid in generating collision-free paths efficiently and consistently and able to guide the robot to follow the path in real time.

Computing Close to Optimal Weighted Shortest Paths in Practice

This paper proposes a new practical method for the weighted region problem (WRP). The objective of WRP is to find a minimum cost path between two vertices among different regions where each region incurs a traversal cost per unit distance. Currently, there is no practical algorithm that solves this problem exactly. Among the approximation methods that solve instances of WRP, there is a limited number of algorithms that compute paths whose lengths are close to optimal, which we call very-close optimum paths. However, they are considered as theoretical methods. On the other hand, algorithms for solving WRP that can be applied to practical data sets (using decomposition ideas or heuristics) are not guaranteed to find a very-close optimum path within an acceptable amount of time. In this paper, we consider an alternative method for solving WRP that exploits Snell's law of physical refraction. We compare the performance of our new algorithm with that of two existing algorithms, using at least 500 test cases for each such comparison. The experimental results show that our algorithm returns a very-close optimum weighted shortest path in reasonable time.

Unsupervised semantic clustering and localization for mobile robotics tasks

Due to its vast applicability, the semantic interpretation of regions or entities increasingly attracts the attention of scholars within the robotics community. The paper at hand introduces a novel unsupervised technique to semantically identify the position of an autonomous agent in unknown environments. When the robot explores a certain path for the first time, community detection is achieved through graph-based segmentation. This allows the agent to semantically define its surroundings in future traverses even if the environment’s lighting conditions are changed. The proposed semantic clustering technique exploits the Louvain community detection algorithm, which constitutes a novel and efficient method for identifying groups of measurements with consistent similarity. The produced communities are combined with metric information, as provided by the robot’s odometry through a hierarchical agglomerative clustering method. The suggested algorithm is evaluated in indoors and outdoors datasets creating topological maps capable of assisting semantic localization. We demonstrate that the system categorizes the places correctly when the robot revisits an environment despite the possible lighting variation.

Dynamic path planning for percutaneous procedures in the abdomen during free breathing.

Percutaneous procedures are increasingly used for the treatment of tumors in abdominal structures. Most of the time, these procedures are planned based on static preoperative images and do not take into account any motions, while breathing control is not always applicable. In this paper, we present a method to automatically adjust the planned path in real time according to the breathing. First, an estimation of the organs motions during breathing is performed during an observation phase. Then we propose an approach named Real Time Intelligent Trajectory (RTIT) that consists in finding the most appropriate moments to push the needle along the initially planned path, based on the motions and the distance to surrounding organs. We also propose a second approach called Real Time Straight Trajectory (RTST) that examines sixteen scenarios of needle insertion at constant speed, starting at eight different moments of the breathing cycle with two different speeds. We evaluated our methods on six 3D models of abdominal structures built using image datasets and a real-time simulation of breathing movements. We measured the deviation from the initial path, the target positioning error, and the distance of the actual path to risky structures. The path proposed by RTIT approach is compared to the best path proposed by RTST. We show that the RTIT approach is relevant and adapted to breathing movements. The modification of the path remains minimal while collisions with obstacles are avoided. This study on simulations constitutes a first step towards intelligent robotic insertion under real-time image guidance.

Distributed Exact Shortest Paths in Sublinear Time

The distributed single-source shortest paths problem is one of the most fundamental and central problems in the message-passing distributed computing. Classical Bellman-Ford algorithm solves it inO(n) time, wherenis the number of vertices in the input graphG. Peleg and Rubinovich [49] showed a lower bound of ˜Ω(D+ √n) for this problem, whereDis the hop-diameter ofG.Whether or not this problem can be solved inO(n) time whenDis relatively small is a major open question. Despite intensive research [10, 17, 33, 41, 45] that yielded near-optimal algorithms for theapproximatevariant of this problem, no progress was reported for the original problem.In this article, we answer this question in the affirmative. We devise an algorithm that requiresO((nlogn)5/6) time, forD=O(√nlogn), andO(D1/3⋅ (nlogn)2/3) time, for largerD. This running time is sublinear innin almost the entire range of parameters, specifically, forD=o(n/ log2n).We also generalize our result in two directions. One is when edges have bandwidthb≥ 1, and the other is thes-sources shortest paths problem. For both problems, our algorithm provides bounds that improve upon the previous state-of-the-art in almost the entire range of parameters. In particular, we provide an all-pairs shortest paths algorithm that requiresO(n5/3⋅ log2/3n) time, even forb= 1, for all values ofD.We also devise the first algorithm with non-trivial complexity guarantees for computing exact shortest paths in themultipass semi-streamingmodel of computation.From the technical viewpoint, our distributed algorithm computes a hopsetG′′of a skeleton graphG′ofGwithout first computingG′itself. We then conduct a Bellman-Ford exploration inG′∪G′′, while computing the required edges ofG′on the fly. As a result, our distributed algorithm computesexactlythose edges ofG′that it really needs, rather than computing approximately the entireG′.

Research on the Load Distribution Strategy to Meet the QoE Requirements for Conversational Real-Time HD Video Service

A reliable transmission with QoE (Quality of Experience) guarantee is crucial for internet conversational service applications. However, due to the limited network bandwidth bottleneck effect and the drawback of transmission technology, there exists no mature and open QoE technical solution for this service. In this paper, we focused our attention on a load distribution strategy for multipath relay transmission to meet the QoE requirements of conversational real-time HD video services. It consisted of three stages: First, a series of relay nodes was deployed in the backbone network, and a software defined overlay network was constructed to perform the multipath relay transmission for the service. Second, by an analysis of the QoE feature, a bijection was built for each quantitative QoE and its MOS (Mean Opinion Score) score. Finally, considering the influence of the coupling relation between paths on the service quality in multipath relay transmission, fuzzy cooperative game theory was used to design the service load distribution strategy. Many experiments showed that compared with state-of-the-art methods in the single-path transmission scene, the strategy we designed can dynamically adjust the load distribution of each sub-path according to the change in QoS (Quality of Service) information of the transmission path in real time. While meeting the strict real-time constraints of the service, it can effectively avoid the impact of network random congestion on the service QoE.

On the path of time: temporal motion in typological perspective

ABSTRACTThe Moving Ego and Moving Time metaphors have provided a fertile testing ground for the psychological reality of space–time metaphors. Despite this, little research has targeted the linguistic patterns used in these two mappings. To fill that gap, the current study uses corpus data to examine the use of motion verbs in two typologically different languages, English and Spanish. We first investigated the relative frequency of the two metaphors. Whereas we observed no difference in frequency in the Spanish data, our findings indicated that in English, Moving Time expressions are more prevalent than are Moving Ego expressions. Second, we focused on the patterns of use of the verbs themselves, asking whether well-known typological patterns in the expression of spatial motion would carry over to temporal motion. Specifically, we examined the frequencies of temporal uses of path and manner verbs in English and in Spanish. Contra the patterns observed in space, we observed a preference for path verbs in both languages, with this preference more strongly evident in English than in Spanish. In addition, our findings revealed greater use of motion verbs in temporal expressions in Spanish compared to English. These findings begin to outline constraints on the aspects of spatial conceptualization that are likely to be reused in the conceptualization of time.

Baryon preclustering at the freeze-out of heavy-ion collisions and light-nuclei production

Following the idea of nucleon clustering and light-nuclei production in relativistic heavy-ion collisions close to the QCD critical-end point, we address the quantum effects affecting the interaction of several nucleons at finite temperature. For this aim we use the $K$-harmonics method to four-nucleon states ($\alpha$ particle), and also develop a novel semiclassical "flucton" method at finite temperature, based on certain classical paths in Euclidean time, and apply it to two- and four-particle configurations. To study possible effects on the light-nuclei production close to the QCD critical point, we also made such calculations with modified internuclear potentials. For heavy-ion experiments, we propose new measurements of light-nuclei multiplicity ratios which may show enhancements due to baryon preclustering. We point out the special role of the $\mathcal{O}(50)$ four-nucleon excitations of $\alpha$-particle, feeding into the final multiplicities of $d,t$, $^3$He and $^4$He, and propose to directly look for their two-body decays.

Effective cache replacement strategy (ECRS) for real-time fog computing environment

Fog Computing (FC) utilizes the resources close to the edge of the network. It supports real time applications such as healthcare, industrial systems, and intelligent traffic signs. FC needs data to be cached in various intermediate nodes to be easily found by the network. Therefore, an efficient caching scheme is essential. Data caching can improve the data availability in FC by reducing access latency and bandwidth. As nodes in FC may have similar tasks and share common interests, cooperative caching can be used to reduce the bandwidth, latency, and power consumption. The originality of this paper is concentrated on introducing an Effective Cache Replacement Strategy (ECRS) and routing algorithm for real-time FC environment with a novel cache replacement and prefetching policies. ECRS is composed of two main modules which are: (i) Path Finding Procedure: to ensure that there is a route between each pair of FNs. And (ii) Data Searching Procedure: to find the required data for the given task. ECRS divides the network into fog regions and each region has a master node that manages the communication in the fog region. Unlike other caching techniques that employ reactive routing protocols, ECRS employs a novel built-in table driven routing strategy with no additional penalties. Such behavior significantly minimizes the query delay. The secret lies in collecting the routing information during the message request forwarding, then fill the routing tables accordingly. ECRS has been compared against recent cooperative caching strategies. Experimental results have shown that ECRS achieved the maximum Cache Hit Ratio, while minimizing Average Query Delay (AQD), Average Hop Count, and the Power Consumption of the network. These results have been achieved due to the high accuracy of using fuzzy and the efficiency of using graph based routing algorithm as the graph can be easily used for finding the shortest paths in fast time. Unlike previous algorithms, ECRS achieves the least AQD. Accordingly, ECRS is a suitable algorithm in the case of real-time systems in FC which leads to load balancing.

Table Cleaning Task by Human Support Robot Using Deep Learning Technique.

This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.

"Two sides of the same coin": constant motor learning speeds up, whereas variable motor learning stabilizes, speed-accuracy movements.

The aim of this study was to determine the time course of the trade-off between speed and accuracy, intraindividual variability, and movement transfer and retention (4weeks after learning) of speed-accuracy tasks. The participants in this study were healthy adults randomly divided into three groups (control versus constant versus variable). They were aged 19-24years, and 30 (15 men and 15 women) were in each group. Participants had to perform various tasks with the right dominant hand: (a) simple reaction test; (b) maximal velocity measurement; and (c) a speed-accuracy task. During constant and variable learning, the trade-off in a speed-accuracy task in specific situations shifted toward improved motor planning and motor execution speed, and to reduced intraindividual variability. However, during variable learning, the maximal velocity and variability of motor planning time did not change. Constant learning effectively transferred into variable tasks in terms of reaction time, average velocity and maximal velocity, and these effects were greater than those associated with variable learning. However, the effects of constant learning did not transfer fully into the performance variability of variable movements. Variable learning effectively transferred into constant tasks for the coefficient of variation of the path of movement, average velocity, maximal velocity and reaction time. The retention effect depended neither on learning nor task specificity (constant versus variable tasks). Constant learning speeds up but does not stabilize speed-accuracy movements in variable tasks; whereas, variable learning stabilizes but does not speed up speed-accuracy movements in constant tasks.

High-Performance CML approximate full adders for image processing application of laplace transform

PurposeThe high demand for fast, energy-efficient, compact computational blocks in digital electronics has led the researchers to use approximate computing in applications where inaccuracy of outputs is tolerable. The purpose of this paper is to present two ultra-high-speed current-mode approximate full adders (FA) by using carbon nanotube field-effect transistors.Design/methodology/approachInstead of using threshold detectors, which are common elements in current-mode logic, diodes are used to stabilize voltage. Zener diodes and ultra-low-power diodes are used within the first and second proposed designs, respectively. This innovation eliminates threshold detectors from critical path and makes it shorter. Then, the new adders are employed in the image processing application of Laplace filter, which detects edges in an image.FindingsSimulation results demonstrate very high-speed operation for the first and second proposed designs, which are, respectively, 44.7 per cent and 21.6 per cent faster than the next high-speed adder cell. In addition, they make a reasonable compromise between power-delay product (PDP) and other important evaluating factors in the context of approximate computing. They have very few transistors and very low total error distance. In addition, they do not propagate error to higher bit positions by generating output carry correctly. According to the investigations, up to four inexact FA can be used in the Laplace filter computations without a significant image quality loss. The employment of the first and second proposed designs results in 42.4 per cent and 32.2 per cent PDP reduction compared to when no approximate FA are used in an 8-bit ripple adder.Originality/valueTwo new current-mode inexact FA are presented. They use diodes as voltage regulators to design current-mode approximate full-adders with very short critical path for the first time.

Effects of a dissipative coupling to the momentum of a particle in a double well potential

Double well potentials offer the possibility of coherent state preparation and therefore constitute important building blocks in the analysis of quantum information and quantum engineering devices. Here we present a study of the coherent tunneling in a parabolic double well potential in presence of different dissipative interactions. Specifically, we investigate the effects of an environmental coupling to the momentum and/or to the position of a particle in the potential. Using the semiclassical approximation to calculate instanton paths in Euclidean time, we find that momentum dissipation enhances the coherent tunnel splitting. In presence of both types of dissipation, the momentum dissipation shifts the critical coupling strength of the dissipative phase transition induced by the position dissipation.

Forming and mechanical properties of wire arc additive manufacture for marine propeller bracket

The marine propeller bracket is a complex core component of large ship power system. Wire Arc additive manufacturing is an effective method to manufacture propeller bracket, which consists of the hub, the support and cross arm. For obtaining high forming precision, offset filling method for printing hub and scanning filling method for printing support and cross arm are adopted. Central deviations of deposition layer are acquired by a 3D scanner and a program written by Matlab software. Adjusting print path in real time to improve forming precision of bracket. The cross section center offset of each deposition layer is within ±0.6mm while the dimensional errors of bracket are within ±0.8 mm. The microstructure of bracket is composed of ferrite, lath bainite and VC precipitates. The tensile strength of printed bracket is 796 MPa, the yield strength is 660 MPa and the impact toughness at −20°C is 53 J.

Automated clash resolution for reinforcement steel design in concrete frames via Q-learning and Building Information Modeling

The design of reinforcing steel bars (rebars) is critical to reinforced concrete (RC) structures. Generally, a good number of rebars are required by a design code, particularly at member connections. As such, rebar clashes (i.e., collisions and congestions) would be inevitable. It would be impractical, labor-intensive, and error-prone to avoid all possible clashes manually or even using standard design software. The building information modeling (BIM) technology has been utilized by the present architecture, engineering, and construction (ACE) industry for clash-free rebar designs. However, most existing BIM-based approaches offer the clash resolution strategy for moving components with an optimization algorithm, and are only applicable to the RC structures with regular shapes. In particular, the optimized path of rebars cannot be adjusted to avoid the obstacles, thus limiting the practical applications. Furthermore, most existing studies lack the learning from design code and constructibility constraints to realize automatic and intelligent arrangement and adjustment of rebars for avoiding the obstacles encountered in complex RC joints and frame structures. Considering these shortcomings, the authors have recently proposed an immediate reward-based multi-agent reinforcement learning (MARL) system with BIM, towards automatic clash-free rebar designs of RC joints without clashes. However, as the immediate reward is required in the MARL system for guiding the learning of a rebar design, it will not succeed in clash-free rebar designs of complex RC structures where immediate reward is often unavailable. In this study, this study further extends the previous work with Q-learning (a model-free reinforcement learning algorithm) for more realistic path planning considering both immediate and delayed rewards in clash-free rebar designs for real-world RC structures. In particular, the rebar design problem is treated as a path-planning problem of multi-agent system, where each rebar is deemed as an intelligence reinforcement learning agent. Next, by employing the Q-learning as the reinforcement learning engine, the particular form of state, action, and immediate and delayed rewards for the reinforcement MARL for automatic rebar designs considering more actual constructible constraints and design codes can be developed. Comprehensive experiments on three typical beam-column joints and a two-story RC building frame were conducted to evaluate the efficiency of the proposed method. The study results of paths of rebar designs, success rates, and average time confirm that the proposed framework with MARL and BIM is effective and efficient.

Design of Treatment System for Poor Peripheral Circulation Using Air Wave Pressure Based on STM32 Microprocessor

This study designs an intermittent pneumatic pressurization device with STM32 series single chip as the core. The working state of the air pump and the plurality of air chambers is controlled by the IO port of the single chip microcomputer, and the circulating inflation of the plurality of air bags is realized. The pressure monitoring system consists of a silicon piezoresistive pressure sensor, a high-precision operational amplifier and a 12-bit AD converter which monitors the gas pressure of each gas path in real time to ensure the safety of the equipment. The system is easy to operate, simple in function, and has strong practicability.

Rate-Induced Tipping in Discrete-Time Dynamical Systems

We develop a definition of rate-induced tipping (R-tipping) in discrete-time dynamical systems (maps) and prove results giving conditions under which R-tipping will or will not happen. Specifically, we study (possibly noninvertible) maps with a time-varying parameter subject to a parameter shift. We show that each stable path has a unique associated solution (a local pullback attractor) which stays near the path for all negative time. When the parameter changes slowly, this local pullback attractor stays near the path for all time, but if the parameter changes quickly, the local pullback attractor may move away from the path in positive time; this is the phenomenon of R-tipping. We demonstrate that forward basin stability is an insufficient condition to prevent R-tipping in maps of any dimension but that forward inflowing stability is sufficient. Furthermore, we show that R-tipping will happen when there is a certain kind of forward basin instability, and we prove precisely what happens to the local pullback attractor as the rate of the parameter change approaches infinity. We then highlight the differences between discrete- and continuous-time systems by showing that when a map is obtained by discretizing a flow, the pullback attractors for the map and flow can have dramatically different behavior; there may be R-tipping in one system but not in the other. We finish by applying our results to demonstrate R-tipping in the 2-dimensional Ikeda map.