Multi-floor navigation method for mobile robot transportation based on StarGazer sensors in life science automation

Abdulla, Ali A.; Liu, Hui; Stoll, Norbert; Thurow, Kerstin

doi:10.1109/i2mtc.2015.7151306

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…Figure 14 shows the test setup used to verify the proposed method of calculating marker position [ 26 ]. Ten markers were attached to a corridor at equal intervals of 180 cm.…”

Section: Verification Of Proposed Methodsmentioning

confidence: 99%

“…Therefore, the hierarchical marker is suitable for maximizing the recognition distance in the case of a camera moving horizontally with respect to the ground, such as in a wheeled mobile robot. Figure 14 shows the test setup used to verify the proposed method of calculating marker position [26]. Ten markers were attached to a corridor at equal intervals of 180 cm.…”

Section: Recognition Distance Of the Proposed Markersmentioning

confidence: 99%

See 1 more Smart Citation

Marker-Based Method for Recognition of Camera Position for Mobile Robots

Gwak

Yang

Park

et al. 2021

Sensors

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Position recognition is one of the core technologies for driving a robot because of differences in environment and rapidly changing situations. This study proposes a strategy for estimating the position of a camera mounted on a mobile robot. The proposed strategy comprises three methods. The first is to directly acquire information (e.g., identification (ID), marker size and marker type) to recognize the position of the camera relative to the marker. The advantage of this marker system is that a combination of markers of different sizes or having different information may be used without having to update the internal parameters of the robot system even if the user frequently changes or adds to the marker’s identification information. In the second, two novel markers are proposed to consider the real environment in which real robots are applied: a nested marker and a hierarchical marker. These markers are proposed to improve the ability of the camera to recognize markers while the camera is moving on the mobile robot. The nested marker is effective for robots like drones, which land and take off vertically with respect to the ground. The hierarchical marker is suitable for robots that move horizontally with respect to the ground such as wheeled mobile robots. The third method is the calculation of the position of an added or moved marker based on a reference marker. This method automatically updates the positions of markers after considering the change in the driving area of the mobile robot. Finally, the proposed methods were validated through experiments.

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“…Figure 14 shows the test setup used to verify the proposed method of calculating marker position [ 26 ]. Ten markers were attached to a corridor at equal intervals of 180 cm.…”

Section: Verification Of Proposed Methodsmentioning

confidence: 99%

Section: Recognition Distance Of the Proposed Markersmentioning

confidence: 99%

Marker-Based Method for Recognition of Camera Position for Mobile Robots

Gwak

Yang

Park

et al. 2021

Sensors

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show abstract

“…The robot will then implement the collision avoidance path by searching for the regions that the robot can pass through. Then, it will select the region that is closest to its original path obtained from the global navigation system [13].…”

Section: Cooperative Collision Avoidancementioning

confidence: 99%

Human Robot Interaction for Hybrid Collision Avoidance System for Indoor Mobile Robots

Ghandour¹,

Liu²,

Stoll³

et al. 2017

Adv. sci. technol. eng. syst. j.

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In this paper, a novel approach for collision avoidance for indoor mobile robots based on human-robot interaction is realized. The main contribution of this work is a new technique for collision avoidance by engaging the human and the robot in generating new collisionfree paths. In mobile robotics, collision avoidance is critical for the success of the robots in implementing their tasks, especially when the robots navigate in crowded and dynamic environments, which include humans. Traditional collision avoidance methods deal with the human as a dynamic obstacle, without taking into consideration that the human will also try to avoid the robot, and this causes the people and the robot to get confused, especially in crowded social places such as restaurants, hospitals, and laboratories. To avoid such scenarios, a reactive-supervised collision avoidance system for mobile robots based on human-robot interaction is implemented. In this method, both the robot and the human will collaborate in generating the collision avoidance via interaction. The person will notify the robot about the avoidance direction via interaction, and the robot will search for the optimal collision-free path on the selected direction. In case that no people interacted with the robot, it will select the navigation path autonomously and select the path that is closest to the goal location. The humans will interact with the robot using gesture recognition and Kinect sensor. To build the gesture recognition system, two models were used to classify these gestures, the first model is Back-Propagation Neural Network (BPNN), and the second model is Support Vector Machine (SVM). Furthermore, a novel collision avoidance system for avoiding the obstacles is implemented and integrated with the HRI system. The system is tested on H20 robot from DrRobot Company (Canada) and a set of experiments were implemented to report the performance of the system in interacting with the human and avoiding collisions.

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“…Path planning can be defined as a motion control from start point to the goal point with collision avoidance taking the shortest path to the destination. Many algorithms have been proposed and developed for this purpose [29][30][31][32]. Compared to the standard ones, the path planning issue has more challenges for the mobile robot transportation in Life Science Laboratories as follows: since the proposed path planning method will be applied in the real robot transportation system, the realtime planning results are desired.…”

Section: Path Planningmentioning

confidence: 99%

A New Robust Method for Mobile Robot Multifloor Navigation in Distributed Life Science Laboratories

Abdulla

Liu

Stoll

et al. 2016

Journal of Control Science and Engineering

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A new robust method is proposed for multifloor navigation in distributed Life Science Laboratories. This method proposes a solution for many technical issues including (a) mapping and localization with ceiling landmarks and a StarGazer module for achieving an accurate and low cost multifloor navigation system, (b) a new method for path planning to navigate across multiple floor environments called backbone method and embedded transportation management system, (c) elevator environment handler with the necessary procedures to interact with the elevator presenting a new approach for elevator entry button and internal buttons detection, and (d) communication system to get an expandable network; this method utilizes a TCP/IP network for the communication. Many experiments in real Life Science Laboratories proved the efficient performance of the developed multifloor navigation system in life science environment.

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Multi-floor navigation method for mobile robot transportation based on StarGazer sensors in life science automation

Cited by 10 publications

References 16 publications

Marker-Based Method for Recognition of Camera Position for Mobile Robots

Marker-Based Method for Recognition of Camera Position for Mobile Robots

Human Robot Interaction for Hybrid Collision Avoidance System for Indoor Mobile Robots

A New Robust Method for Mobile Robot Multifloor Navigation in Distributed Life Science Laboratories

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