Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

Zhou, Hang; Chou, Wu; Tuo, Wanchen; Rong, Yongfeng

doi:10.3390/s20226697

Cited by 11 publications

(4 citation statements)

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“…This work presents, similarly to [31], a decoupled mobile manipulation control for greenhouse related tasks using the ROS de-facto algorithms [27] and [28]. The navigation is based on latest robotic solutions which have proven to successfully use Galileo Satellites combined with IMU, odometry and range laser sensors for localization [8].…”

Section: Related Workmentioning

confidence: 99%

A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator

et al. 2021

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To meet the demands of a rising population greenhouses must face the challenge of producing more in a more efficient and sustainable way. Innovative mobile robotic solutions with flexible navigation and manipulation strategies can help monitor the field in real-time. Guided by Integrated Pest Management strategies, robots can perform early pest detection and selective treatment tasks autonomously. However, combining the different robotic skills is an error prone work that requires experience in many robotic fields, usually deriving on ad-hoc solutions that are not reusable in other contexts. This work presents Robotframework, a generic ROS-based architecture which can easily integrate different navigation, manipulation, perception, and high-decision modules leading to a faster and simplified development of new robotic applications. The architecture includes generic real-time data collection tools, diagnosis and error handling modules, and user-friendly interfaces. To demonstrate the benefits of combining and easily integrating different robotic skills using the architecture, two flexible manipulation strategies have been developed to enhance the pest detection in its early state and to perform targeted spraying in simulated and field commercial greenhouses. Besides, an additional use-case has been included to demonstrate the applicability of the architecture in other industrial contexts.INDEX TERMS Precision agriculture, robotic control architecture, mobile manipulator, pest detection and treatment, greenhouse.

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Section: Related Workmentioning

confidence: 99%

A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator

et al. 2021

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“…IgnoreMask means when there is no target, the IoU of the prediction frame and the real frame are calculated, and the largest IoU is selected as the IoU of the predicted and the real value. An IoU threshold is set, and when its maximum IoU is less than this threshold, it will be added to the loss function calculation as Equation (7). L ciou means location loss.…”

Section: The Loss Of Yolov4mentioning

confidence: 99%

“…The other is to combine machine learning or deep learning to achieve a more intelligent grasp and better completion. Accordingly, a combination of identification and tracking [6,7] is highly required. As we all know, people are able to recognize the type and location of objects at first glance.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid YOLOv4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment

et al. 2021

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In this paper, we propose a robotic arm grasping system suitable for complex environments. For a robotic arm, in order to achieve its accurate grasp of the target object, not only the vision but also a certain tracking ability should be provided. To improve the grasp quality, we propose a robotic arm grasping system using YOLOv4 combined with a particle filter (PF) algorithm, which can be applied in a nonlinear and non-Gaussian environment. Firstly, the coordinates of the bounding box in the image can be obtained through the YOLOv4 object detection algorithm. Secondly, the coordinates in the world system can be obtained through the eye-to-hand calibration system. Thirdly, a PF model can be established based on the coordinate changes of the target object. Finally, according to the predicted output of the PF, the robotic arm and the target object can reach the specific position at the same time and complete the grab. As the target object, the bowl is applied to experiments for the sake of achieving a more convincing demonstration. The experimental results show that the robotic arm grasping system proposed in this paper can accomplish the successful grasp at a rate of nearly 88%, even at a higher movement speed, which is of great significance to robot applications in various fields.

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“…The use of Artificial Intelligence for mobile manipulation and cooperative tasks is also a recurrent research topic, as it adds mechanisms to tune and optimize control parameters. Zhou et al [ 19 ] propose a mobile manipulation method integrating deep-learning-based multiple-object detection to track and grasp dynamic objects efficiently. Following this same path, Wang et al [ 20 ] present a novel mobile manipulation system that decouples visual perception from the deep reinforcement learning control, improving its generalization from simulation training to real-world testing.…”

Section: Related Workmentioning

confidence: 99%

Path Driven Dual Arm Mobile Co-Manipulation Architecture for Large Part Manipulation in Industrial Environments

Ibarguren

Daelman

2021

Sensors

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Collaborative part transportation is an interesting application as many industrial sectors require moving large parts among different areas of the workshops, using a large amount of the workforce on this tasks. Even so, the implementation of such kinds of robotic solutions raises technical challenges like force-based control or robot-to-human feedback. This paper presents a path-driven mobile co-manipulation architecture, proposing an algorithm that deals with all the steps of collaborative part transportation. Starting from the generation of force-based twist commands, continuing with the path management for the definition of safe and collaborative areas, and finishing with the feedback provided to the system users, the proposed approach allows creating collaborative lanes for the conveyance of large components. The implemented solution and performed tests show the suitability of the proposed architecture, allowing the creation of a functional robotic system able to assist operators transporting large parts on workshops.

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Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

Cited by 11 publications

References 50 publications

A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator

A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator

A Hybrid YOLOv4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment

Path Driven Dual Arm Mobile Co-Manipulation Architecture for Large Part Manipulation in Industrial Environments

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