Dynamic Model Learning and Manipulation Planning for Objects in Hospitals Using a Patient Assistant Mobile (PAM)Robot

Novin, Roya Sabbagh; Yazdani, Amirmehdi; Hermans, Tucker

doi:10.1109/iros.2018.8593989

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…While the authors primarily focused on the prediction part and did not provide results for the mass estimation part, their work is one of the first to employ bigdata methods on the inertial parameter estimation problem. Finally, in [60] the authors use a Bayesian Regression Model to learn the inertial parameters of a hospital walker, by tracking the motion of a real robot pushing the walker in 39 trials. They used the learned model for manipulation planning, prediction and control of the walker motion, achieving low errors.…”

Section: Exploratory Methodsmentioning

confidence: 99%

Estimation and exploitation of objects ’ inertial parameters in robotic grasping and manipulation: A survey

Mavrakis

Stolkin

2020

Robotics and Autonomous Systems

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Inertial parameters characterise an object's motion under applied forces, and can provide strong priors for planning and control of robotic actions to manipulate the object. However, these parameters are not available a-priori in situations where a robot encounters new objects. In this paper, we describe and categorise the ways that a robot can identify an object's inertial parameters. We also discuss grasping and manipulation methods in which knowledge of inertial parameters is exploited in various ways. We begin with a discussion of literature which investigates how humans estimate the inertial parameters of objects, to provide background and motivation for this area of robotics research. We frame our discussion of the robotics literature in terms of three categories of estimation methods, according to the amount of interaction with the object: purely visual, exploratory, and fixed-object. Each category is analysed and discussed. To demonstrate the usefulness of inertial estimation research, we describe a number of grasping and manipulation applications that make use of the inertial parameters of objects. The aim of the paper is to thoroughly review and categorise existing work in an important, but under-explored, area of robotics research, present its background and applications, and suggest future directions. Note that this paper does not examine methods of identification of the robot's inertial parameters, but rather the identification of inertial parameters of other objects which the robot is tasked with manipulating.

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Section: Exploratory Methodsmentioning

confidence: 99%

Estimation and exploitation of objects ’ inertial parameters in robotic grasping and manipulation: A survey

Mavrakis

Stolkin

2020

Robotics and Autonomous Systems

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“…To generate each trajectory, we sample two points, a point close to the initial location as the start state and a point close to the target location as the end state (Figure 5A). We use a common optimization-based method to find a trajectory based on the current state and the desired state (Figure 5B; Sabbagh Novin et al, 2018; Ratliff et al, 2009). We assume that patients will take a minimum length path and avoid obstacles during walking.…”

Section: Methodsmentioning

confidence: 99%

Development of a Novel Computational Model for Evaluating Fall Risk in Patient Room Design

Novin

Taylor

Hermans

2020

HERD

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Objectives: This study proposes a computational model to evaluate patient room design layout and features that contribute to patient stability and mitigate the risk of fall. Background: While common fall risk assessment tools in nursing have an acceptable level of sensitivity and specificity, they focus on intrinsic factors and medications, making risk assessment limited in terms of how the physical environment contributes to fall risk. Methods: We use literature to inform a computational model (algorithm) to define the relationship between these factors and the risk of fall. We use a trajectory optimization approach for patient motion prediction. Results: Based on available data, the algorithm includes static factors of lighting, flooring, supportive objects, and bathroom doors and dynamic factors of patient movement. This preliminary model was tested using four room designs as examples of typical room configurations. Results show the capabilities of the proposed model to identify the risk associated with different room layouts and features. Conclusions: This innovative approach to room design evaluation and resulting estimation of patient fall risk show promise as a proactive evidence-based tool to evaluate the relationship of potential fall risk and room design. The development of the model highlights the challenge of heterogeneity in factors and reporting found in the studies of patient falls, which hinder our understanding of the role of the built environment in mitigating risk. A more comprehensive investigation comparing the model with actual patient falls data is needed to further refine model development.

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Remote Control of a Wheeled Robot by Visible Light for Support in Infectious Disease Hospitals

Tsunoda

Premachandra

2021

IEEE Access

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The shortage of medical personnel is now a problem and is expected to worsen in the future. Meanwhile, in the case of infectious diseases such as new coronavirus infections, it is very important that nurses and other medical staff treat patients as remotely as possible, which helps to prevent nosocomial infections and is also important for maintaining the medical system. One way to address these issues is to use mobile robots as assistants to automate the transport of patients and supplies. Wireless communication is necessary to control such robots remotely, but doing so with radio waves is undesirable in the medical field because of the impact on patients and medical equipment. Therefore, this paper proposes a teleoperation system for a wheeled mobile robot using visible light with a camera and an array of LEDs, an approach that affects neither patients nor medical equipment. This paper presents the development of visible light communication for this system by introducing a novel blinking pattern for the receiver and novel light-weight algorithm for the transmitter. According to a wide range of experiments, the proposed teleoperation system performs well enough for the remote control of wheeled robots in hospitals.

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Dynamic Model Learning and Manipulation Planning for Objects in Hospitals Using a Patient Assistant Mobile (PAM)Robot

Cited by 6 publications

References 28 publications

Estimation and exploitation of objects ’ inertial parameters in robotic grasping and manipulation: A survey

Estimation and exploitation of objects ’ inertial parameters in robotic grasping and manipulation: A survey

Development of a Novel Computational Model for Evaluating Fall Risk in Patient Room Design

Remote Control of a Wheeled Robot by Visible Light for Support in Infectious Disease Hospitals

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