Haptic teleoperation of mobile robots for augmentation of operator perception in environments with low-wireless signal

Owen-Hill, Alexander; Parasuraman, Ramviyas; Ferré, Manuel

doi:10.1109/ssrr.2013.6719329

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…Besides, the haptic rendering method was generated directly from the point cloud model. An algorithm integrated haptic information with velocity control was proposed to enhance the human operator's perception capability on the slave side [46]. In order to maximise the perception of the operator in the case of macro–micro interactions with regard to soft tissues, a kinaesthetic perception scheme, constraint conditions were used to balance the stability and performance [47].…”

Section: Key Technologies and Challengesmentioning

confidence: 99%

“…In order to maximise the perception of the operator in the case of macro–micro interactions with regard to soft tissues, a kinaesthetic perception scheme, constraint conditions were used to balance the stability and performance [47]. In [46, 47], those methods have enhanced perception capability between the human operator and the slave. In the teleoperated system, the human factor should be taken into consideration.…”

Section: Key Technologies and Challengesmentioning

confidence: 99%

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Combined perception, control, and learning for teleoperation: key technologies, applications, and challenges

Luo

Yang

2020

Cognitive Computation and Systems

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Teleoperation provides a promising way for human-robot collaboration in the unknown or unstructured environments to perform a cooperative task. It enables humans to complete a task at a remote side and combines both the human's intelligence and the robots' capabilities in a collaborative task. Therefore, it is necessary to conduct cross researches in terms of robotics, artificial intelligence, sensors, and mechatronics. This study covers the state-of-the-arts research in terms of perception, control, and learning. In this review, key technologies about information perception mechanism, control algorithms, human-robot interface, and robot learning strategies for teleoperation are introduced. Then, a comprehensive survey is summarised in representative applications in teleoperation, and the existing challenges and potential directions of the development are discussed.

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Section: Key Technologies and Challengesmentioning

confidence: 99%

Section: Key Technologies and Challengesmentioning

confidence: 99%

Combined perception, control, and learning for teleoperation: key technologies, applications, and challenges

Luo

Yang

2020

Cognitive Computation and Systems

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“…Communication loss and sub-optimal performance can be costly and can restrict many robot tasks, where data shared between multiple stations or devices are crucial [6]. Similarly, the data transfer needs would differ based on the specific tasks, where a balance of throughput and message latency is important [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Bi-directional Wi-Fi Network Performance on Mobile Robots in Indoor Environments

Pandey¹,

Parasuraman²

2021

Preprint

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This paper proposes a framework to measure the important metrics (throughput, delay, packet retransmits, signal strength, etc.) to determine Wi-Fi network performance of mobile robots supported by the Robot Operating Systems (ROS) middleware. We analyze the bidirectional network performance of mobile robots and connected vehicles through an experimental setup, where a mobile robot is communicating vital sensor data such as video streaming from the camera(s) and LiDAR scan values to a command station while it navigates an indoor environment through teleoperated velocity commands received from the command station. The experiments evaluate the performance under 2.4 GHz and 5 GHz channels with different placement of Access Points (AP) with up to two network devices on each side. The discussions and insights from this study apply to the general vehicular networks and the field robotics community, where the wireless network plays a key role in enabling the success of robotic missions.

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“…the DoA) that would increase this signal strength, and thereby the communication quality (delay, packet loss, etc.) which is known to affect teleoperation task performance [6], [7].…”

Section: Introductionmentioning

confidence: 99%

A New UGV Teleoperation Interface for Improved Awareness of Network Connectivity and Physical Surroundings

Parasuraman¹,

Caccamo²,

Båberg³

et al. 2017

Journal of Human-Robot Interaction

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A reliable wireless connection between the operator and the teleoperated unmanned ground vehicle (UGV) is critical in many urban search and rescue (USAR) missions. Unfortunately, as was seen in, for example, the Fukushima nuclear disaster, the networks available in areas where USAR missions take place are often severely limited in range and coverage. Therefore, during mission execution, the operator needs to keep track of not only the physical parts of the mission, such as navigating through an area or searching for victims, but also the variations in network connectivity across the environment.In this paper, we propose and evaluate a new teleoperation user interface (UI) that includes a way of estimating the direction of arrival (DoA) of the radio signal strength (RSS) and integrating the DoA information in the interface. The evaluation shows that using the interface results in more objects found, and less aborted missions due to connectivity problems, as compared to a standard interface.The proposed interface is an extension to an existing interface centered on the video stream captured by the UGV. But instead of just showing the network signal strength in terms of percent and a set of bars, the additional information of DoA is added in terms of a color bar surrounding the video feed. With this information, the operator knows what movement directions are safe, even when moving in regions close to the connectivity threshold.

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Haptic teleoperation of mobile robots for augmentation of operator perception in environments with low-wireless signal

Cited by 11 publications

References 18 publications

Combined perception, control, and learning for teleoperation: key technologies, applications, and challenges

Combined perception, control, and learning for teleoperation: key technologies, applications, and challenges

Empirical Analysis of Bi-directional Wi-Fi Network Performance on Mobile Robots in Indoor Environments

A New UGV Teleoperation Interface for Improved Awareness of Network Connectivity and Physical Surroundings

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