Adaptive and intelligent robot task planning for home service: A review

Li, Haizhen; Ding, Xilun

doi:10.1016/j.engappai.2022.105618

Cited by 8 publications

(2 citation statements)

References 103 publications

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“…In a 40 * 40 map, 20 intelligent robots [5] are randomly initialized within the range of [-15, 15]. The changes in the cluster robots at different time intervals T are shown in figure 1.…”

Section: Flocking Algorithm Simulation Experimentsmentioning

confidence: 99%

Research and Simulation of Robot Cluster Motion Based on Flocking Algorithm

Wang,

Li,

Cui

2023

Advances in Transdisciplinary Engineering

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The research on robot cluster control and collaborative motion has gradually become an important issue in the research of cluster robots A clustering control and collaborative motion algorithm based on the Flocking algorithm is proposed to address the above issues, which mainly includes three parts: robot cluster path planning, cluster object avoidance, and cluster formation control Firstly, a stable cluster formation is observed through the cluster control of the Flocking algorithm, which enables it to have the ability of cluster collaborative motion Based on this, an improved A * algorithm can be used to plan a cluster movement path At the same time, in the collaborative motion of robot clusters, dynamic and static objects can be avoided through the DWA algorithm, there quickly reaching the destination Solve the problem of cluster control and collaborative motion of robots in complex environments, and finally verify the effectiveness and rationality of the algorithm through simulation experiments.

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“…In a 40 * 40 map, 20 intelligent robots [5] are randomly initialized within the range of [-15, 15]. The changes in the cluster robots at different time intervals T are shown in figure 1.…”

Section: Flocking Algorithm Simulation Experimentsmentioning

confidence: 99%

Research and Simulation of Robot Cluster Motion Based on Flocking Algorithm

Wang,

Li,

Cui

2023

Advances in Transdisciplinary Engineering

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“…In recent years, robotics and artificial intelligence have developed rapidly, and robots now play an indispensable role in factory workshops or daily life [1][2][3]. Traditional industrial robotic arms are designed for heavy loads and huge operating spaces.…”

Section: Introductionmentioning

confidence: 99%

Research on LFD System of Humanoid Dual-Arm Robot

Cui,

Kou,

Chen

et al. 2024

Symmetry

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Although robots have been widely used in a variety of fields, the idea of enabling them to perform multiple tasks in the same way that humans do remains a difficulty. To solve this, we investigate the learning from demonstration (LFD) system with our independently designed symmetrical humanoid dual-arm robot. We present a novel action feature matching algorithm. This algorithm accurately transforms human demonstration data into task models that robots can directly execute, considerably improving LFD’s generalization capabilities. In our studies, we used motion capture cameras to capture human demonstration actions, which included combinations of simple actions (the action layer) and a succession of complicated operational tasks (the task layer). For the action layer data, we employed Gaussian mixture models (GMM) for processing and constructing an action primitive library. As for the task layer data, we created a “keyframe” segmentation method to transform this data into a series of action primitives and build another action primitive library. Guided by our algorithm, the robot successfully imitated complex human tasks. Results show its excellent task learning and execution, providing an effective solution for robots to learn from human demonstrations and significantly advancing robot technology.

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