Robot Operating System 2

DiLuoffo, Vincenzo; Michalson, William R.; Sunar, Berk

doi:10.1177/1729881418770011

Cited by 50 publications

(18 citation statements)

References 23 publications

(27 reference statements)

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“…In our experiments, the Nengo simulator has been used to implement the system [40]. Our mobile robot is controlled with the Robot Operating System (ROS) [41].…”

Section: Reproducing Fear Conditioning Using Mobile Robotsmentioning

confidence: 99%

Implementation of Associative Memory Learning in Mobile Robots Using Neuromorphic Computing

Zins¹,

Zhang²,

An³

2023

Neuromorphic Computing

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Fear conditioning is a behavioral paradigm of learning to predict aversive events. It is a form of associative learning that memorizes an undesirable stimulus (e.g., an electrical shock) and a neutral stimulus (e.g., a tone), resulting in a fear response (such as running away) to the originally neutral stimulus. The association of concurrent events is implemented by strengthening the synaptic connection between the neurons. In this paper, with an analogous methodology, we reproduce the classic fear conditioning experiment of rats using mobile robots and a neuromorphic system. In our design, the acceleration from a vibration platform substitutes the undesirable stimulus in rats. Meanwhile, the brightness of light (dark vs. light) is used for a neutral stimulus, which is analogous to the neutral sound in fear conditioning experiments in rats. The brightness of the light is processed with sparse coding in the Intel Loihi chip. The simulation and experimental results demonstrate that our neuromorphic robot successfully, for the first time, reproduces the fear conditioning experiment of rats with a mobile robot. The work exhibits a potential online learning paradigm with no labeled data required. The mobile robot directly memorizes the events by interacting with its surroundings, essentially different from data-driven methods.

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“…In our experiments, the Nengo simulator has been used to implement the system [40]. Our mobile robot is controlled with the Robot Operating System (ROS) [41].…”

Section: Reproducing Fear Conditioning Using Mobile Robotsmentioning

confidence: 99%

Implementation of Associative Memory Learning in Mobile Robots Using Neuromorphic Computing

Zins¹,

Zhang²,

An³

2023

Neuromorphic Computing

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“…An underlying assumption in many of the sources we reviewed was that computing devices are assumed to be housed within a controlled physical location and managed. Since autonomous robotic systems are not in such an environment, and may be exposed to physical attacks (invasive attacks involving physical manipulations on semiconductors like microprobing, and non-invasive attacks where side channel leakage can occur like power analysis) [20], this assumption is invalid and this type of system will need system level protection. System level protection can range from the highest level of security, tamper protection with countermeasures, to lowest level, evidence of tampering.…”

Section: System Level Trust Evaluationmentioning

confidence: 99%

“…This demonstrates that even a well-known library, like OpenSSL, can be compromised if the physical hardware is compromised. Since, in an autonomous robotic system, nodes may be captured, an assumption of physical security may be invalid [36].…”

Section: Software Level Trust Evaluationmentioning

confidence: 99%

A Survey on Trust Metrics for Autonomous Robotic Systems

DiLuoffo¹,

Michalson²

2023

AAIML

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This paper surveys the area of “Trust Metrics” related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful systemlevel trust metrics for evaluating complex robotic (and other) systems.

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“…The docking method uses the Rive function package of ROS to complete the graphic visualization operation and projects the point cloud obtained by Lidar onto the map established by ROS [7]. Lidar is one of the important devices for sensing the environment and using the principle of Lidar ranging and the controller and scanner to image the position and angle of laser emission.…”

Section: A Implement Of Wheelchair/nursing-bed Automatic Docking Schemementioning

confidence: 99%

“…the wheelchair adjusts its posture, turn both wheels at point D , then we can get(7) and(8).' B and ' C can be obtained from (5) (6) (7) (8).…”

mentioning

confidence: 99%

Wheelchair Automatic Docking Method for Body-Separated Nursing Bed Based on Grid Map

Zhu

Meng

et al. 2021

IEEE Access

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To improve the mode and precision of wheelchair/nursing-bed automatic docking, a novel central embedded wheelchair/nursing-bed automatic docking method based on grid map is proposed. Firstly, Laplace operator and Iterative Closest Point (ICP) algorithm are used to filter and match Lidar point cloud, and the linear features of V-shaped artificial landmark are fitted by Split-merge method and least square method. Then Extended Kalman Filter (EKF) is used to fuse Inertial Measurement Unit (IMU) and odometer data to realize the localization of the bed and wheelchair. Meanwhile, the grid map is used for path planning. Based on the center-line of the two rear wheels and the angular bisector of V-shaped artificial landmark, the wheelchair pose is adjusted in real-time to ensure that the wheelchair gradually approaches the bed along the angular bisector of V-shaped artificial landmark. The yaw angle is reduced by using the improved Proportion Integration Differentiation (PID). 9 sets of experimental data, ie. (x, y,  ) were collected at different starting positions during the docking process. The results show that the yaw angle of the wheelchair during the docking process is controlled within 2.5°, and the distance deviation between the final position and the ideal position of the wheelchair after docking is controlled within 0.02m. In the case of light interference with different luminous fluxes, the docking can still maintain good performance. The proposed docking algorithm has the robust performance of rapid response and low steady error, which can greatly improves the self-care ability of the bedridden elderly, and reduces the labor intensity of the nursing staff.

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Robot Operating System 2

Cited by 50 publications

References 23 publications

Implementation of Associative Memory Learning in Mobile Robots Using Neuromorphic Computing

Implementation of Associative Memory Learning in Mobile Robots Using Neuromorphic Computing

A Survey on Trust Metrics for Autonomous Robotic Systems

Wheelchair Automatic Docking Method for Body-Separated Nursing Bed Based on Grid Map

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