Purpose -The purpose of this paper is to introduce the design and the multi-mode locomotion function of the new reconfigurable modular robotic system -UBot system -which combines the advantages from the chain-based and lattice-based self-reconfigurable robots. Design/methodology/approach -The UBot modules the authors have designed are based on the universal joint and of cubic shape with two rotational joints and reliable automatic connecting mechanism. The modules are compact and flexible enough for locomotion and reconfiguration. The system can move in different modes to satisfy different terrains, through changing the modules' local connections and rotation of modules' joints. Findings -The UBot system can flexibly move in the modes of cross, loop, quadruped, snake-type and other type of locomotion modes. All the locomotion has been implemented in the physical experiments. Originality/value -The UBot module is the new reconfigurable module which has two joints in one unit of regular cubic space and four reliable automatic connecting surfaces. A group of the modules is able to change its connective configuration by changing their local connections and has functionality of the corresponding traditional robotic system. Since it can travel through terrains that may not be fully characterized ahead of time, the system can be used in a large variety of tasks, such as transportation, assembly, inspection and exploration.
The design and implementation of a novel modular Self-Reconfigurable Robot (SRR) called UBot is reviewed in this paper. Firstly, the philosophy of hardware design is presented. The module is designed with criteria such as cubic-shape, homogeneity, and strong connections to fulfill the requirements of complex three-dimensional reconfiguration and locomotion. Each robotic module has two degrees of freedom and four connecting surfaces with hook-type connecting mechanism. A group of modules can transform between different configurations by changing their local connections, achieve complicated modes of motion and accomplish a large variety of tasks. Secondly, a 3D dynamics simulator for UBot SRR is developed, where robot locomotion and transfiguration simulation could be done. A worm-like robot evolution is performed with results of a variety of high-performance locomotion patterns. Finally, Experiments are performed about autonomous docking, multi-mode locomotion and self-reconfiguration. The validity of docking method, CPG-network control and reconfiguration planning method is verified through locomotion and transformation tests of configurations such as snake-type, quadruped walking-type, omni-directional cross-type and loop-type.
This paper proposes a calibration method for continuous measurements with a double ball bar (DBB) used to identify the position-dependent geometric errors (PDGEs) of the rotary axes of five-axis machine tools. The different DBB installation modes for the rotary axes of the spindle and workbench are established, and the same initial DBB installation position is used for multiple tests. This approach minimizes the number of required DBB installations, which increases the measurement efficiency of the PDGEs of the rotary axes and reduces installation errors. PDGEs identification based on the adaptive least absolute shrinkage and selection operator (LASSO) method is proposed. By assigning coefficients to the PDGEs polynomial, the ill-conditioned problem of the identification process can be effectively avoided, thereby improving the identification accuracy. The measurement and identification methods proposed in this paper are verified by experiments on a five-axis machine tool. After compensation, the PDGEs are obviously reduced and the accuracy indexes of the circular trajectory tests performed under multiaxis synchronous control are obviously improved.
This paper proposes a multi-agent-based collaborative virtual manufacturing environment (VME) to save energy consumption and improve efficiency in the manufacturing process. In order to achieve the high autonomy of the manufacturing system, a multi-agent system (MAS) is designed to build a collaborative VME. In this new VME environment, edge computing is embedded to strengthen the cyber resource utilization and system economy. Moreover, an efficient communication channel between networks is proposed. The subsequent cooperation and collaboration protocols among agents are designed to ensure flexible and process-oriented operations. Furthermore, the fuzzy resolution algorithm is employed to resolve the competition conflicts among function-similar MASs in the distributed manufacturing scenario. Lastly, a simulation and case study are performed to evaluate the performance of the proposed VME in Internet of Things (IoT)-based manufacturing. The analysis results have demonstrated the feasibility and effectiveness of the proposed VME system.
This study attempts to optimize the scheduling decision to save production cost (e.g., energy consumption) in a distributed manufacturing environment that comprises multiple distributed factories and where each factory has one flow shop with blocking constraints. A new scheduling optimization model is developed based on a discrete fruit fly optimization algorithm (DFOA). In this new evolutionary optimization method, three heuristic methods were proposed to initialize the DFOA model with good quality and diversity. In the smell-based search phase of DFOA, four neighborhood structures according to factory reassignment and job sequencing adjustment were designed to help explore a larger solution space. Furthermore, two local search methods were incorporated into the framework of variable neighborhood descent (VND) to enhance exploitation. In the vision-based search phase, an effective update criterion was developed. Hence, the proposed DFOA has a large probability to find an optimal solution to the scheduling optimization problem. Experimental validation was performed to evaluate the effectiveness of the proposed initialization schemes, neighborhood strategy, and local search methods. Additionally, the proposed DFOA was compared with well-known heuristics and metaheuristics on small-scale and large-scale test instances. The analysis results demonstrate that the search and optimization ability of the proposed DFOA is superior to well-known algorithms on precision and convergence.
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