State-of-the-art autonomous vehicles mainly rely on optical sensors to perceive their environment. However, the performance of these sensors worsens dramatically in environments where airborne particles are present. Sonar sensors rely on acoustic waves which are able to pass through these distortions. The data gathered from sonar could complement the distorted data of the optical sensors in these earlier mentioned environments. In this paper we will discuss the newest 3D in-air sonar sensor developed by CoSys-Lab: the micro Real Time Imaging Sonar (μRTIS). It is the smaller version of the previously developed embedded Real Time Imaging Sonar (eRTIS) and consists of a uniform rectangular array of 5 by 6 microphones compared to the pseudo-random 32 microphone array based on Poisson Disc-Sampling used with the eRTIS. In this paper we will discuss the used hardware of the μRTIS, followed by the implemented processing algorithms to form acoustic images from reflected ultrasonic data. Furthermore, we will discuss the spatial resolution that can be obtained with the presented hardware architecture using different beamforming algorithms. Finally, we will validate the real-life performance, comparing the acoustic images of the μRTIS when using Delay-And-Sum or MUltiple SIgnal Classification to solve the different directions of arrival.

Many popular advanced sonar systems provide accurate and reliable measurements containing crucial info needed by robotic applications such as range, bearing and reflection strength of the objects in the field of view. While these sensor systems provide these crucial pieces of information accurately, they are often limited by a lack of processing power and/or size which leads to them needing an external computing device to process all the information generated by the microphone array on the sensor. In this paper we present two versions of a novel fully embedded 3D sonar sensor which have different sensing architectures which enable 3D perception for robotic application in harsh conditions using ultrasound at low cost. Experimental results taken from an office environment will show the 3D localization capabilities and performance of the sensor, showing the sensor has a large field-of-view (FoV) with accurate 3D localization combined with real-time capabilities.

In this paper we present a flexible low-cost sonar sensor platform that can be used for a wide range of biomimetic sonar experiments and autonomous sonar navigation targeted at robotics applications. The navigation abilities of bats using ultrasound (sonar) in unknown cluttered environments are very effective and can be distilled into a sensor architecture and accompanying control methodology that lends itself to be implemented on cost efficient hardware. The sensor architecture and processing methodology of this sensing platform mimics that of bats. In this paper we specifically focused on the common big-eared bat (Micronycteris microtis) although this could be transferred to other bat species or even other echolocating animals since the experimental platform was designed for flexibility. Using this platform we were able to implement a control system using a subsumption architecture that features different behavior patterns based solely on the sonar sensor as a source of exteroceptive information. In order to validate the combination of our autonomous navigation control system and our developed sonar sensor platform, the hardware was mounted on the P3DX robotics platform that was introduced in an unknown testing environment and have it drive autonomously. These experiments were used to validate our assumption of the efficacy of these relatively simple biomimetic control mechanisms and thus alleviating the need for expensive sensing platforms for certain robotics applications.

Obtaining accurate data about the environment in which a robot is located is a crucial matter when it comes to autonomous navigation and other robotic applications. A popular method of acquiring this information is to use sonar-rings, where a robot is fitted with multiple simple ultrasound transducers pointed in the directions where an object can appear. However, in a time where accurate 3D data is gaining importance, other sensing modalities are becoming more popular because of the ability to measure dense 3D point clouds. In these point clouds not only the horizontal plane is measured, but objects in the elevation planes can also be registered, which can be very interesting and makes applications such as 3D SLAM or object recognition possible. In this paper we present a way to extract complex 3D point cloud data from the entire surrounding sphere using multiple interconnected eRTIS sensors. These advanced imaging sonar sensors offer the flexibility of the popular sonar-ring in combination with the benefits of some of the competing sensing modalities. The setup presented here uses less sonar sensors (and thus less external hardware) while obtaining more information from the complete frontal hemispheres of each individual sensor. This setup is discussed, along with the issues that arise when using complex imaging sonar sensors in a network, and is tested in an indoor and outdoor environment. At the end of this paper is a discussion of the obtained results.