Self-localization method for mobile robot using acoustic beacons

Ogiso, Satoki; Kawagishi, T.; Mizutani, Koichi; Wakatsuki, Naoto; Zempo, Keiichi

doi:10.1186/s40648-015-0034-y

Cited by 34 publications

(20 citation statements)

References 20 publications

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“…Position and heading are calculated using triangulation [24,25,26]. Two ultrasonic range sensors placed at the wheelchair side (facing the AP) were used to determine the distance from the sensors to the AP side (tether distance).…”

Section: Methodsmentioning

confidence: 99%

Ultrasonic Tethering to Enable Side-by-Side Following for Powered Wheelchairs

Pingali

Lemaire

Baddour

2018

Sensors

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In social situations, people who use a powered wheelchair must divide their attention between navigating the chair and conversing with people. These conversations could lead to increased mental stress when navigating and distraction from maneuvering the chair. As a solution that maintains a good conversation distance between the wheelchair and the accompanying person (Social Following), a wheelchair control system was developed to provide automated side-by-side following by wirelessly connecting the wheelchair to the person. Two ultrasonic range sensors and three piezoelectric ultrasonic transducers were used to identify the accompanying person and determine their position and heading. Identification involved an ultrasonic beacon worn on the person’s side, at hip level, and receivers on the wheelchair. A drive control algorithm maintained a constant conversation distance along the person’s trajectory. A plug-and-play prototype was developed and connected to a Permobil F5 Corpus wheelchair with a modified Eightfold Technologies SmartChair Remote. Results demonstrated that the system can navigate a wheelchair based on the accompanying person’s trajectory, which is advantageous for users who require hands-free wheelchair control during social activities.

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Section: Methodsmentioning

confidence: 99%

Ultrasonic Tethering to Enable Side-by-Side Following for Powered Wheelchairs

Pingali

Lemaire

Baddour

2018

Sensors

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“…The predictions are subsequently updated by inferring information from the DoA estimates in (10). As derived in Appendix A, the predicted and detection PHDs in (19) and 20thus are:…”

Section: B Probabilistic Source Triangulationmentioning

confidence: 99%

“…Very few contributions address the application of SLAM to audio signals, and can be broadly classified into two categories. The first category [18]- [19] applies visual SLAM techniques, e.g., Fast-SLAM [20], to acoustic Times-of-Arrivals (TOAs). By virtue of the universal presence of immovable fixtures in visual scenes, FActored Solution To Simultaneous Localization and Mapping (FastSLAM) aligns the observer using permanently visible landmarks.…”

Section: Introductionmentioning

confidence: 99%

Acoustic SLAM

Evers

Naylor

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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An algorithm is presented that enables devices equipped with microphones, such as robots, to move within their environment in order to explore, adapt to, and interact with sound sources of interest. Acoustic scene mapping creates a threedimensional (3D) representation of the positional information of sound sources across time and space. In practice, positional source information is only provided by Direction-of-Arrival (DoA) estimates of the source directions; the source-sensor range is typically difficult to obtain. DoA estimates are also adversely affected by reverberation, noise, and interference, leading to errors in source location estimation and consequent false DoA estimates. Moreover, many acoustic sources, such as human talkers, are not continuously active, such that periods of inactivity lead to missing DoA estimates. Withal, the DoA estimates are specified relative to the observer's sensor location and orientation. Accurate positional information about the observer therefore is crucial. This paper proposes Acoustic Simultaneous Localization and Mapping (aSLAM), which uses acoustic signals to simultaneously map the 3D positions of multiple sound sources while passively localizing the observer within the scene map. The performance of aSLAM is analyzed and evaluated using a series of realistic simulations. Results are presented to show the impact of the observer motion and sound source localization accuracy.

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“…Early work by Wang [113] used a speaker mounted on a ground robot and an array of 24 microphones distributed around a room to localize the robot using the SRP method under a near-field assumption. More recently, an inversion of this approach was demonstrated by Ogiso [114], in which four speakers were Chapter 1. Introduction placed around a room, and self-localization of a robot equipped with a microphone array was performed via an EKF with DOA measurements.…”

Section: Acoustic Localizationmentioning

confidence: 99%

Underwater and out of sight: Towards ubiquity in underwater robotics

Rypkema¹

2019

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The Earth's oceans holds a wealth of information currently hidden from us. Effective measurement of its properties could provide a better understanding of our changing climate and insights into the creatures that inhabit its waters. Autonomous underwater vehicles (AUVs) hold the promise of penetrating the ocean environment and uncovering its mysteries; and progress in underwater robotics research over the past three decades has resulted in vehicles that can navigate reliably and operate consistently, providing oceanographers with an additional tool for studying the ocean.Unfortunately, the high cost of these vehicles has stifled the democratization of this technology. We believe that this is a consequence of two factors. Firstly, reliable navigation on conventional AUVs has been achieved through the use of a sophisticated sensor system, namely the Doppler velocity log (DVL)-aided inertial navigation system (INS), which drives up vehicle cost, power use and size. Secondly, deployment of these vehicles is expensive and unwieldy due to their complexity, size and cost, resulting in the need for specialized personnel for vehicle operation and maintenance.The recent development of simpler, low-cost, miniature underwater robots provides a solution that mitigates both these factors; however, removing the expensive DVL-aided INS means that they perform poorly in terms of navigation accuracy. We address this by introducing a novel acoustic system that enables AUV self-localization without requiring a DVL-aided INS or on-board active acoustic transmitters. We term this approach Passive Inverted Ultra-Short Baseline (piUSBL) positioning. The system uses a single acoustic beacon and a time-synchronized, vehicle-mounted, passive receiver array to localize the vehicle relative to this beacon. Our approach has two unique advantages: first, a single beacon lowers cost and enables easy deployment; second, a passive receiver allows the vehicle to be low-power, low-cost and small, and enables multi-vehicle scalability.Providing this new generation of small and inexpensive vehicles with accurate navigation can potentially lower the cost of entry into underwater robotics research and further its widespread use for ocean science. We hope that these contributions in low-cost underwater navigation will enable the ubiquitous and coordinated use of robots to explore and understand the underwater domain.

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Self-localization method for mobile robot using acoustic beacons

Cited by 34 publications

References 20 publications

Ultrasonic Tethering to Enable Side-by-Side Following for Powered Wheelchairs

Ultrasonic Tethering to Enable Side-by-Side Following for Powered Wheelchairs

Acoustic SLAM

Underwater and out of sight: Towards ubiquity in underwater robotics

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