A Searching Space Constrained Partial to Full Registration Approach With Applications in Airport Trolley Deployment Robot

Pan, Jin; Mai, Xiaochun; Wang, Chaoqun; Min, Zhe; Wang, Jiankun; Hu, Cheng; Li, Tingguang; Lyu, Erli; Liu, Li; Meng, Max Q.-H.

doi:10.1109/jsen.2020.3042665

Cited by 11 publications

(5 citation statements)

References 34 publications

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“…For planning and control, there are some previous efforts at the trolley collection task. In [5], [11], the adopted method is visual servoing. The main disadvantage of such method is that it does not consider obstacle avoidance and safety, leaving another critical task on the to-do list upon field deployment.…”

Section: A Related Workmentioning

confidence: 99%

Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control

Xiao¹,

Luan²,

Zhao³

et al. 2021

Preprint

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Autonomous mobile manipulation robots that can collect trolleys are widely used to liberate human resources and fight epidemics. Most prior robotic trolley collection solutions only detect trolleys with 2D poses or are merely based on specific marks and lack the formal design of planning algorithms. In this paper, we present a novel mobile manipulation system with applications in luggage trolley collection. The proposed system integrates a compact hardware design and a progressive perception and planning framework, enabling the system to efficiently and robustly collect trolleys in dynamic and complex environments. For the perception, we first develop a 3D trolley detection method that combines object detection and keypoint estimation. Then, a docking process in a short distance is achieved with an accurate point cloud plane detection method and a novel manipulator design. On the planning side, we formulate the robot's motion planning under a nonlinear model predictive control framework with control barrier functions to improve obstacle avoidance capabilities while maintaining the target in the sensors' field of view in close distances. We demonstrate our design and framework by deploying the system on actual trolley collection tasks, and their effectiveness and robustness are experimentally validated. (Video 1

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Section: A Related Workmentioning

confidence: 99%

Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control

Xiao¹,

Luan²,

Zhao³

et al. 2021

Preprint

Self Cite

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“…In the above equation (2), u is the dimension of the state vector of the input layer, v refers to the output layer, k represents a constant within [1,10]. Therefore, it can be known that the number of neurons in the hidden layer is in the range of [4,13]. In order to maximize the training effect of the model, the number of neurons is selected as 13 in this study.…”

Section: Non-convexitymentioning

confidence: 99%

“…It is the pinnacle of mechatronics achievements and can represent the high-tech level of a country [3]. The Stanford Research Institute firstly began to study the autonomous path planning capabilities of autonomous mobile robots in complex environments in the 1960s [4]. At present, an important development direction for various countries in the world is related to the research and development of robots.…”

Section: Introductionmentioning

confidence: 99%

The Direction Analysis on Trajectory of Fast Neural Network Learning Robot

2021

IEEE Access

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This study is to efficiently apply artificial neural network (ANN) to the robotics, so as to provide experimental basis for mobile robots to learn the optimal trajectory planning strategy. An algorithm model is innovatively proposed based on back propagation neural network (BPNN) and reinforcement learning (Q-Learning) by combining the motion space, selective strategy, and reward function design. The simulation experiment environment is set and the ROS mobile robot is adopted for simulation experiments. The algorithm proposed in this study is compared with other neural network algorithms from the perspectives of accuracy, precision, recall, and F1. It can be found that the accuracy of algorithm proposed was at least 5.47% higher than that of the model algorithm proposed by other scholars, and the values of precision, recall, and F1 were at least 5.5% higher. The results show that the mobile robot could find the shortest trajectory and the best trajectory in a discrete obstacle environment, no matter the more or less the discrete obstacles or the large or small the space. Therefore, compared to the advanced model algorithms proposed by other scholars in related fields, the robot trajectory planning based on the improved BPNN combined with Q-Learning constructed in this study could realize better results, and can be used in practical applications with robot trajectory planning, providing practical value for the field of machine vision.

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“…To fuse images from different modalities, the key salient points such as boundary and edge are selected to represent the image. Point set registrationcitepomerleau2015review is utilized to merge multiple data source or map new measurement to prior model [19].…”

Section: Related Workmentioning

confidence: 99%

Globally Learnable Point Set Registration Between 3D CT and Multi-view 2D X-ray Images of Hip Phantom

Pan

Min

Zhang

et al. 2021

2021 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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2D-3D registration is a crucial step in Image-Guided Intervention, such as spine surgery, total hip replacement, and kinematic analysis. To find the information in common between pre-operative 3D CT images and intraoperative X-ray 2D images is vital to plan and navigate. In a nutshell, the goal is to find the movement and rotation of the 3D body's volume to make them reorient with the patient body in the 2D image space. Due to the loss of dimensionality and different sources of images, efficient and fast registration is challenging. To this end, we propose a novel approach to incorporate a point set Neural Network to combine the information from different views, which enjoys the robustness of the traditional method and the geometrical information extraction ability. The pre-trained Deep BlindPnP captures the global information and local connectivity, and each implementation of view-independent Deep BlindPnP in different view pairs will select top-priority pairs candidates. The transformation of different viewpoints into the same coordinate will accumulate the correspondence. Finally, a POSEST-based module will output the final 6 DoF pose. Extensive experiments on a real-world clinical dataset show the effectiveness of the proposed framework compared to the single view. The accuracy and computation speed are improved by incorporating the point set neural network.

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A Searching Space Constrained Partial to Full Registration Approach With Applications in Airport Trolley Deployment Robot

Cited by 11 publications

References 34 publications

Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control

Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control

The Direction Analysis on Trajectory of Fast Neural Network Learning Robot

Globally Learnable Point Set Registration Between 3D CT and Multi-view 2D X-ray Images of Hip Phantom

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