Intent communication in navigation through the use of light and screen indicators

Shrestha, Moondeep C.; Kobayashi, Ayano; Onishi, Tomoya; Uno, Erika; Yanagawa, Hayato; Yokoyama, Yukihiro; Kamezaki, Mitsuhiro; Schmitz, Alexander; Sugano, Shigeki

doi:10.1109/hri.2016.7451837

Cited by 22 publications

(13 citation statements)

References 3 publications

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“…Other related works have used turn and display indicators on the robot to communicate navigational intent (Chadalavada et al, 2016; Schaefer et al, 2017; Szafir et al, 2015). These techniques were found to improve human trust and confidence in robot actions; however, they did not express high detail in the motion plan (Shrestha et al, 2016a,b).…”

Section: Related Workmentioning

confidence: 99%

Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays

Rosen

Whitney

Phillips

et al. 2019

The International Journal of Robotics Research

103

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Efficient motion intent communication is necessary for safe and collaborative work environments with co-located humans and robots. Humans efficiently communicate their motion intent to other humans through gestures, gaze, and other non-verbal cues, and can replan their motions in response. However, robots often have difficulty using these methods. Many existing methods for robot motion intent communication rely on 2D displays, which require the human to continually pause their work to check a visualization. We propose a mixed-reality head-mounted display (HMD) visualization of the intended robot motion over the wearer’s real-world view of the robot and its environment. In addition, our interface allows users to adjust the intended goal pose of the end effector using hand gestures. We describe its implementation, which connects a ROS-enabled robot to the HoloLens using ROS Reality, using MoveIt for motion planning, and using Unity to render the visualization. To evaluate the effectiveness of this system against a 2D display visualization and against no visualization, we asked 32 participants to label various arm trajectories as either colliding or non-colliding with blocks arranged on a table. We found a 15% increase in accuracy with a 38% decrease in the time it took to complete the task compared with the next best system. These results demonstrate that a mixed-reality HMD allows a human to determine where the robot is going to move more quickly and accurately than existing baselines.

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Section: Related Workmentioning

confidence: 99%

Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays

Rosen

Whitney

Phillips

et al. 2019

The International Journal of Robotics Research

103

View full text Add to dashboard Cite

show abstract

“…Chadalavada et al [49] proposed using an LED projector to communicate the internal state of automatic guided vehicles. We have also presented the results of shoulder-based light and display signals [50,51].…”

Section: Inducement For Conveying Intent In Robot Navigationmentioning

confidence: 99%

“…By referring to observations of human behaviors and conventional navigation studies [29,30], we derived six inducement methods considering typical behaviors of a mobile robot with an arm. Light and display signals [50,51] and using gaze and face expression [42,44] can be regarded as a visual inducement. In this study, we adopt humanlike inducements by considering the sociality.…”

Section: Outputs: Trajectory and Inducementmentioning

confidence: 99%

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Kamezaki

Kobayashi

Yokoyama

et al. 2019

Int J of Soc Robotics

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Human-aware navigation is an essential requirement for autonomous robots in human-coexisting environments. The goal of conventional navigation is to find a path for a robot to pass through safely and efficiently without colliding with human. Note that if such a path cannot be found, the robot stops until a path is clear. Thus, such collision-avoidance based passive navigation does not work in a congested or narrow space. To avoid this freezing problem, the robot should induce humans to make a space for passing by an adequate inducement method, such as body movement, speech, and touch, depending on the situation. A robot that deliberately clears a path with such actions may make humans uncomfortable, so the robot should also utilize inducements to avoid causing negative feelings. In this study, we propose a fundamental framework of interactive navigation with situation-adaptive multimodal inducement. For a preliminary study, we target a passing scenario in a narrow corridor where two humans are standing and adopt a model-based approach focusing on common parameters. The suitable inducement basically varies depending on the largest space through which a robot can pass, distance between the robot and a human, and human behavior such as conversing. We thus develop a situation-adaptive inducement selector on the basis of the relationship between human-robot proximity and allowable inducement strength, considering robot efficiency and human psychology. The proposed interactive navigation system was tested across some contextual scenarios and compared with a fundamental path planner. The experimental results indicated that the proposed system solved freezing problems, provided a safe and efficient trajectory, and improved humans' psychological reaction although the evidence was limited to robot planner and hardware design we used as well as certain scenes, contexts, and participants.

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“…However, having this visualization does not significantly affect the performance when executing tasks using the robot [23]. When using a visual representation of robot motion intent, the most prominent solution is to show the robot's movement using arrows [24][25][26]. In addition, most of these approaches rely on Augmented Reality to overlay the visual representation on the user's real environment.…”

Section: Related Workmentioning

confidence: 99%

Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms

et al. 2022

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Robot arms are one of many assistive technologies used by people with motor impairments. Assistive robot arms can allow people to perform activities of daily living (ADL) involving grasping and manipulating objects in their environment without the assistance of caregivers. Suitable input devices (e.g., joysticks) mostly have two Degrees of Freedom (DoF), while most assistive robot arms have six or more. This results in time-consuming and cognitively demanding mode switches to change the mapping of DoFs to control the robot. One option to decrease the difficulty of controlling a high-DoF assistive robot arm using a low-DoF input device is to assign different combinations of movement-DoFs to the device’s input DoFs depending on the current situation (adaptive control). To explore this method of control, we designed two adaptive control methods for a realistic virtual 3D environment. We evaluated our methods against a commonly used non-adaptive control method that requires the user to switch controls manually. This was conducted in a simulated remote study that used Virtual Reality and involved 39 non-disabled participants. Our results show that the number of mode switches necessary to complete a simple pick-and-place task decreases significantly when using an adaptive control type. In contrast, the task completion time and workload stay the same. A thematic analysis of qualitative feedback of our participants suggests that a longer period of training could further improve the performance of adaptive control methods.

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Intent communication in navigation through the use of light and screen indicators

Cited by 22 publications

References 3 publications

Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays

Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms

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