To improve the path efficiency for dynamic obstacle avoidance algorithms of Pioneer robots, this paper proposes an approach to create deep deterministic policy gradient (DDPG) smart agents with recurrent neural networ...
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Aiming at solving the problem of insufficient plunge depth by the deformation of friction stir welding robots, this paper presents a method for predicting and compensating for the end deformation of friction stir weld...
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According to the parameters of the ABB-IRB2400 robot model, the D-H parameter method is used to establish the robot’s linkage coordinate system and determine the parameter values. The coordinate transformation matrix...
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Cloud detection can address data quality and analytical accuracy issues caused by cloud cover in satellite imagery, significantly enhancing image usability. Existing cloud detection methods still have the problem of d...
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Since the Low Earth Orbit (LEO) satellite communication system has the advantages of low latency, global coverage, and rapid reconstruction, it has become the main networking method of satellite networks [1]. Routing ...
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ISBN:
(数字)9798350373523
ISBN:
(纸本)9798350373530
Since the Low Earth Orbit (LEO) satellite communication system has the advantages of low latency, global coverage, and rapid reconstruction, it has become the main networking method of satellite networks [1]. Routing is the basis for realizing satellite communications. Because the complexity of the shortest path algorithm basically determines the computational complexity of the routing algorithm, the shortest path algorithm is the core of the routing problem. This paper attempts to use the laws of LEO satellite network topology to calculate the shortest path between the source satellite and the destination satellite. First, we derive the function of inter-satellite link (ISL) length with time and establish the geometric model of the satellite network. Then, on the basis of this model, we establish the shortest path algorithm between source-destination pairs (SD pairs) - the symmetric geometry algorithm (SGA). In addition, in order to ensure the optimality of the path, we extend the applicable scene of the model from the two-dimensional plane to the three-dimensional sphere. The simulation test results show that compared with the traditional Dijkstra’s algorithm, SGA can greatly improve the calculation speed of the shortest path while ensuring the optimality of the path.
To enhance the safety and longevity of large ships, employing high-pressure water jet technology for rust removal is crucial in this article, the qualitative relationship among the key parameters of high-pressure wate...
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Existing neurosurgical robots are prone to interfering with each other in a narrow operating space due to the overlapping of several arms, which poses a great safety risk during surgery. In this paper, a 7-DOF dual-ar...
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The integration of multi-agent reinforcement learning (MARL) into complex systems has paved new ways for collaborative problem-solving. However, traditional approaches to MARL frequently encounter the challenge of ach...
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ISBN:
(数字)9798350368604
ISBN:
(纸本)9798350368611
The integration of multi-agent reinforcement learning (MARL) into complex systems has paved new ways for collaborative problem-solving. However, traditional approaches to MARL frequently encounter the challenge of achieving ef-ficient communication among agents, essential for coordinated action. This paper introduces a region division and leader-follower(RDLF) communication algrithm with the MARL frame-work. RDLF divides the environment into several regions, each managed by a leader agent that coordinates the actions of fol-lower agents and handling inter-region communication. This hier-archical structure reduces unnecessary communication, enhancing learning efficiency. Experimental results in multi-particle en-vironments demonstrate RDLF's superiority over existing MARL algorithms, especially with increasing agent numbers. RDLF effectively addresses scalability and communication challenges in large-scale multi-agent systems, providing a robust foundation for its application in complex and dynamic environment.
Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design o...
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Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel, and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization (NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples cons
This paper presents a novel learning-based method for a robotic manipulator to achieve liquid pouring across different liquid levels using only visual sensors. Previous works have relied on either online reinforcement...
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ISBN:
(数字)9798331509644
ISBN:
(纸本)9798331509651
This paper presents a novel learning-based method for a robotic manipulator to achieve liquid pouring across different liquid levels using only visual sensors. Previous works have relied on either online reinforcement learning or imitation learning, which are limited by sim-to-real gaps or data efficiency. In this paper, we propose to combine supervised learning and offline reinforcement learning, utilizing a human demonstration dataset containing only successful pouring at the highest level. Specifically, our approach employs supervised learning for a visual classifier, transforming the visual input into a categorical distribution over liquid levels. The offline reinforcement learning method is applied to a binary-conditioned control policy, which takes a binary signal and the robot's proprioception as inputs to determine the action. The binary signal indicates whether the desired liquid level has been reached based on the target liquid level. Through experiments, our method demonstrates superior effectiveness compared to a state-of-the-art imitation learning algorithm. Moreover, tests in unseen scenarios and multiple liquid level commands verify the generalization and transferability of our approach.
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