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"Robotics industry"
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Industrial automation and robotics : techniques and applications
\"This book discusses the radical technological changes occurring due to Industry 4.0, with a focus on offering a better understanding of the Fourth Industrial Revolution. It also presents a detailed analysis of interdisciplinary knowledge, numerical modeling and simulation, and the application of cyber-physical systems, where information technology and physical devices create synergic systems leading to unprecedented efficiency. The theoretical results, practical solutions, and guidelines presented are valuable for both researchers working in the area of engineering sciences and practitioners looking for solutions to industrial problems\"-- Provided by publisher.
BOOK
Correction: Novel deep reinforcement learning based collision avoidance approach for path planning of robots in unknown environment
[This corrects the article DOI: 10.1371/journal.pone.0312559.].
Journal Article
Correction: Robot or human? Manoeuvring switching intention after robot service failure
[This corrects the article DOI: 10.1371/journal.pone.0333616.].
Journal Article
Correction: Albustanji et al. Robotics: Five Senses plus One—An Overview. Robotics 2023, 12, 68
Error in Figure [...]
Journal Article
Correction: Șomîtcă et al. Improving Path Accuracy of Mobile Robots in Uncertain Environments by Adapted Bézier Curves. Electronics 2022, 11, 3568
In the original publication [...]
Journal Article
Inverse Graphs in Im/I-Polar Fuzzy Environments and Their Application in Robotics Manufacturing Allocation Problems with New Techniques of Resolvability
The inverse in crisp graph theory is a well-known topic. However, the inverse concept for fuzzy graphs has recently been created, and its numerous characteristics are being examined. Each node and edge in m-polar fuzzy graphs (mPFG) include m components, which are interlinked through a minimum relationship. However, if one wants to maximize the relationship between nodes and edges, then the m-polar fuzzy graph concept is inappropriate. Considering everything we wish to obtain here, we present an inverse graph under an m-polar fuzzy environment. An inverse mPFG is one in which each component’s membership value (MV) is greater than or equal to that of each component of the incidence nodes. This is in contrast to an mPFG, where each component’s MV is less than or equal to the MV of each component’s incidence nodes. An inverse mPFG’s characteristics and some of its isomorphic features are introduced. The α-cut concept is also studied here. Here, we also define the composition and decomposition of an inverse mPFG uniquely with a proper explanation. The connectivity concept, that is, the strength of connectedness, cut nodes, bridges, etc., is also developed on an inverse mPF environment, and some of the properties of this concept are also discussed in detail. Lastly, a real-life application based on the robotics manufacturing allocation problem is solved with the help of an inverse mPFG.
Journal Article
Fast and smooth human motion imitation integrating deep predictive learning with model predictive control
To expand the use of robots to assist and replace workers in tasks, the robot needs to deal with not only repetitive and simple tasks but also complex and delicate tasks with high speed and high accuracy. In recent years, imitation learning has been used in several studies to enable robots to learn complex human-like motion with little learning cost. However, in the imitation learning framework, it is difficult to make teaching data that takes into account optimal acceleration/deceleration, force, and constraints of the robot from a control perspective. In this paper, we propose a control scheme to track a fast and smooth imitation motion by implementing a model predictive control (MPC) scheme. To accelerate and smooth human teaching motions, we designed an MPC that follows a reference trajectory output from a motion generator learned by using deep predictive learning (DPL). By adopting this approach, it is possible to suppress excessive accelerations and decelerations while maintaining the ability to follow the target imitation motion. This allows for an increase in the robot’s motion speed while preserving the task success rate. Through simulations of an object grasping task and actual environments of a door-opening task, we evaluated the effectiveness of the proposed control scheme.
Journal Article
Correction: Social robots in research on social and cognitive development in infants and toddlers: A scoping review
[This corrects the article DOI: 10.1371/journal.pone.0303704.].
Journal Article