Neonavigation meta-package: 2-D/3-DOF seamless global-local planner for ROS — Development and field test on the representative offshore oil plant

Watanabe, Atsushi; Endo, Daisuke; Yamauchi, Genki; Nagatani, Keiji

doi:10.1109/ssrr.2016.7784282

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…LiDAR was utilized for self-localization by the adaptive Monte Carlo localization method on a previously built environmental map using the simultaneous localization and mapping method. Thus, it could automatically drive on pre-designed routes using a path-following controller [38].…”

Section: A Autonomous Personal Mobility Vehiclementioning

confidence: 99%

Subjective Vertical Conflict Model With Visual Vertical: Predicting Motion Sickness on Autonomous Personal Mobility Vehicles

Liu,

Inoue,

Wada

2024

IEEE Trans. Intell. Transport. Syst.

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Passengers of level 3-5 autonomous personal mobility vehicles (APMV) can perform non-driving tasks, such as reading books and smartphones, while driving. It has been pointed out that such activities may increase motion sickness, especially when frequently avoiding pedestrians or obstacles in shared spaces. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Among them, models based on subjective vertical conflict (SVC) theory, which describes vertical changes in direction sensed by human sensory organs v.s. those expected by the central nervous system, have been actively developed. To model motion sickness due to conflict between visual vertical information and vestibular sensation, we proposed a 6 DoF SVC-VV model which added a visually perceived vertical block into a conventional 6 DoF SVC model to predict visual vertical directions from image data simulating the visual input of a human. In a driving experiment, 27 participants rode on the APMV and experienced slalom driving with two visual conditions: looking ahead (LAD) and working with a tablet device (WAD). We verified that passengers got motion sickness while riding the APMV, and the symptoms were severer when especially working on it, by simulating the frequent pedestrian avoidance scenarios of the APMV in the experiment. In addition, the results of the experiment demonstrated that the proposed 6 DoF SVC-VV model could describe the increased motion sickness experienced when the visual vertical and gravitational acceleration directions were different.

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Section: A Autonomous Personal Mobility Vehiclementioning

confidence: 99%

Subjective Vertical Conflict Model With Visual Vertical: Predicting Motion Sickness on Autonomous Personal Mobility Vehicles

Liu,

Inoue,

Wada

2024

IEEE Trans. Intell. Transport. Syst.

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“…Besides of the integrated navigation package, the ROS operating system is equipped with many planners that can be utilized together with machine learning methods [7][8][9]. The principle tooling of the global planners includes visibility graphs, based on image skeleton establishment, probabilistic roadmap planners, rapid exploration of random trees, state lattices and navigation functions.…”

Section: Related Workmentioning

confidence: 99%

“…Along with the widely used global planners, some solutions also exist, intended for more specific applications, or insufficiently studied. So, in [7] a novel planner is described, developed for complicated and narrow environments. To search for the target point, the authors proposed to use the A* algorithm.…”

Section: Related Workmentioning

confidence: 99%

Algorithm of Target Point Assignment for Robot Path Planning Based on Costmap Data

Kuznetsov

Kozyr

Levonevskiy

2021

Smart Innovation, Systems and Technologies

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Most mobile robots which use the operating systems (ROS) employ costmap-based path planner for navigation in environment. Besides the systems, in which robotic devices function in completely autonomous manner, also such systems exist, where user specifies in a self-contained interface the target point for robot motion, without using any costmap-related data. In such settings, the robot sometimes cannot reach the user-specified point because it is treated as an obstacle on the costmap. To solve this problem an algorithm was developed, which accepts the coordinates of the target point, specified through user interface, and finds possible target points in vicinity of the specified one, regarding the predefined distance constraints. Conceptually, the search of possible points consists in looping through costmap cells of the global cost planner in orthogonally related directions from the obtained point and in defining of the preferable target points, upon reaching which the task is considered to be completed. In this paper, the algorithm was tested as part of the navigational module of the robot. Experiments were performed, particularly, in the Gazebo simulation environment; robot model TURTLEBOT3 Waffle Pi was also utilized. The experimental results showed that this module can be combined with any planner. When the value of validation step was set at the minimum value of 0.01 m, it was required no more than 55 ms to find a target point. The minimum duration of such search was 1 ms.

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Autonomous service robot for human-aware restock, straightening and disposal tasks in retail automation

et al. 2022

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Neonavigation meta-package: 2-D/3-DOF seamless global-local planner for ROS — Development and field test on the representative offshore oil plant

Cited by 7 publications

References 6 publications

Subjective Vertical Conflict Model With Visual Vertical: Predicting Motion Sickness on Autonomous Personal Mobility Vehicles

Subjective Vertical Conflict Model With Visual Vertical: Predicting Motion Sickness on Autonomous Personal Mobility Vehicles

Algorithm of Target Point Assignment for Robot Path Planning Based on Costmap Data

Autonomous service robot for human-aware restock, straightening and disposal tasks in retail automation

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