Efficient path planning and minimization of path movement costs for collision-free faster robot movement are very important in the field of robot automation. Several path planning algorithms have been explored to fulfill these requirements. Among them, the A-star (A*) algorithm performs better than others because of its heuristic search guidance. However, the performance, effectiveness, and searching time complexity of this algorithm mostly depends on the robot motion block to search for the goal by avoiding obstacles. With this challenge kept in mind, this paper proposes an efficient robot motion block with different block sizes for the A* path planning algorithm. The proposed approach reduces robots’ path cost and time complexity to find the goal position as well as avoid obstacles. In this proposed approach, grid-based maps are used where the robot’s next move is decided by searching eight directions among the surrounding grid points. However, the proposed robot motion blocks size has a significant effect on path cost and time complexity of the A* path planning algorithm. For the experiment and to validate the efficiency of the proposed approach, an online benchmarked dataset is used. The proposed approach is applied on thousands of different grid maps with various obstacles, starting, and goal positions. The obtained results from the experiment show that the presented robot motion blocks reduce the robot’s pathfinding time complexity and number of search nodes by maintaining a minimum path cost towards the goal position.

Efficient knowledge sharing, computation load minimization, and collision-free movement are very important issues in the field of multi-robot automation. Several cloud robot architectures have been investigated to fulfill these requirements. However, the performance of the cloud-robot architectures created to date are suboptimal due to the lack of efficient data management for multi-robotic systems. With this point in mind, this paper proposes an efficient cloud multi-robot framework with cloud database model for mobile robot applications to facilitate multi-robot management, communication, and resource sharing. In this proposed architecture, the cloud framework is comprised with cloud data analysis, cloud database management, and cloud service management. The data analysis serves different data processing and decision-making tasks for generating the next robot action based on robot sensors’ data with the help of a data access components layer. A multistage cloud database model distributes, stores, and accesses different categories of data related to robot sensors and environments. And cloud service facilitates multi-robot management, communication, and resource sharing in the cloud framework. Additionally, as a use case, a cloud-based convolutional neural network (CNN) model is introduced for learning and recognizing robot application data. The obtained results of our tests indicate that the proposed cloud-robot architecture provides efficient computation power, communications, and knowledge sharing for managing multi-mobile robot systems.

Computation offloading for cloud robotics is receiving considerable attention in academic and industrial communities. However, current solutions face challenges: 1) traditional approaches do not consider the characteristics of networked cloud robotics (NCR) (e.g., heterogeneity and robotic cooperation); 2) they fail to capture the characteristics of tasks in a robotic streaming workflow (RSW) (e.g., strict latency requirements and varying task semantics); and 3) they do not consider quality-of-service (QoS) issues for cloud robotics. In this paper, we address these issues by proposing a QoS-aware RSW allocation algorithm for NCR with joint optimization of latency, energy efficiency, and cost, while considering the characteristics of both RSW and NCR. We first propose a novel framework that combines individual robots, robot clusters, and a remote cloud for computation offloading. We then formulate the joint QoS optimization problem for RSW allocation in NCR while considering latency, energy consumption, and operating cost, and show that the problem is NP-hard. Next, we construct a data flow graph based on the characteristics of RSW and NCR, and transform the RSW allocation problem into a mixed-integer linear programming problem. To obtain a near-optimal solution in reasonable time, we also develop a heuristic algorithm. Experiments comparing our approach with others demonstrate significant performance gains, with improved QoS and reduced execution times.

This paper describes a generalized software interface for formation flying by drones from different manufacturers. Conventional research into formation flight assumes that the drones all have the same power and functionality. However, consider a disaster response, where we might assemble a platoon of drones to sense the environment and to search for survivors by combining the different functions of drones provided by different manufacturers. The difficulties of controlling formation flight by such a variety of drones include both different mechanical specifications and different interfaces from the manufacturers for activating the same command. In this research, we construct a generalized interface for drones from each manufacturer using OpenRTM-aist. We can then assemble these drones and establish formation flight by using a virtual leader-follower system. The leader and the follower positions are calculated by using speed and rotation data from feedback information such as the GPS, velocity and rotation data from each individual machine. We also investigate good features of flight commands that can express the attributes of the representative motion of the drones. From our experiments, we show that we can establish formation flight using drones of different power and from multiple manufacturers.

The objective of this paper is to understand an aspect of human social interaction in public bulletin board systems (BBSs). We try to answer the question of why and how a long and hot chain of articles often emerges in BBSs. This paper presents the following three contributions. (1) empirical results: we measured and analyzed actual BBS logs, and found that the number of articles submitted by each individual in an article chain follows a lognormal distribution. (2) model: to investigate why the distribution emerges through individual activities, we developed a simple model of voluntary submission activity for each individual. With only this simple mechanism, the lognormal distribution shown in actual data is reproduced. (3) analytical solution: we showed that the model generates a lognormal distribution when the number of members in a BBS community is constant, and the individuals in a community are homogeneous

When we use a search engine in Internet, we often cannot retrieve a document we look for, due to the difficulty of finding adequate keywords. One of the ways to solve the problem is to search adequate keywords interacting with a user: a metasearch engine displays keywords and pages, a user evaluate them. Iterating the process, the metasearch engine narrows the keywords, getting closer to what a user looks for. For realizing it, we construct a graph called a linked document space, in which nodes and links represent the pages and the similarities, respectively. Then, the metasearch engine searches every promising page considering relations between pages, following the evaluation from a user. This paper presents a search method in the linked document space, called the socially topology agents (STA), which are inspired by human social relation and swarm intelligence. STA is applied to a multi-peak large-scale search problem, and it is shown that STA can find most of peaks quickly, as well as the aggregation and distribution of the agents in the space are controlled by a user command. They mean STA is feasible as the metasearch engine

The objective of this research is to develop a wearable power assist device which helps a person to lift up a heavy object. The device is designed to support him by holding his upper body weight and reducing his inner force. It turns to the reduction of a compression force of his lower back discs, which is a major factor of a lower back injury. In a lifting-up motion, he twists and bends his body, which means three-dimensional motion analysis is necessary for the compression force analysis. However, most of related works on the compression force analysis have been carried out in a vertical plane. For understanding the characteristics of a lifting-up motion better, this paper presents three-dimensional analysis of it. Using a motion capture system, we measured a sequence of body positions with and without the power assist device in different object location and mass, as well as surface EMG (electro-myogram) signals. The compression force of the lower back discs is estimated by a three-dimensional biomechanical human body model utilizing the position data. The results show that the range of the twist angle of his body is about 30 degree in maximum and the compression force increases about 10% due to the twist angle. With respect to the effect of the power assist device, the magnitude of the surface EMG signals of spine muscles is decreased when wearing the power assist device. It means the proposed power assist device gives a good support to him.

A power assisting device for lower back flexion and extension, when carrying a heavy load is presented in this paper. When people lift up something heavy, they use both hands. So the development of a controller without using hands is necessary. We attempt to control the device by a voluntary human motion. First, lifting up and putting down motions are analyzed using a human model. Results show that torques in a hip joint and a knee joint are characteristic for control the device. It means that EMG signals in front and back thigh muscles can become feature values because the muscles located there connect the hip joint and the knee joint. After that, a controller of the device using the EMG signals is developed. Finally, artificial neural networks (ANN) are introduced as a solution of the problem of individual differences.

In this paper, we propose a power assist device for lower back flexion and extension, when carrying a heavy load. To see the effect of the device, we model a human body and analyze a compression force in his lower back, as well as the evaluation of a supported force at a hand position. A prototype of the device is manufactured and a controller of the device is developed, which can follow a voluntary human motion assisting his strength.