Herbert Peremans

A high-resolution sensor based on tri-aural perception

The authors present a high-resolution sensor composed of three ultrasonic sensors, one transmitter/receiver and two extra receivers, which allows a significant improvement in the information-extraction process. With this sensor the position (distance and bearing) of all isolated objects in an approximately 25 degrees field of view can be determined using information contained in one single snapshot of a moderately complex scene. Within limits, the sensor system can also discriminate between different types of reflectors, in particular, walls and edges, based on their radius of curvature. These results are all based on the determination of the arrival times of the echoes present at the three receivers. A noise model that accounts for the measured variations of the arrival times is used to derive limits on the resolution of the results provided by the sensor. Based on this model it is shown that, to a large extent, the sensor results are impervious to measurement variations common to all three receivers. Results obtained in a realistic environment are compared with those obtained from a conventional time-of-flight sensor. >

Accurate ranging of multiple objects using ultrasonic sensors

The authors propose a measurement setup consisting of a number of ultrasonic sensors used in parallel to perform triangulation measurements. The sensor system is based on two ideas. The first idea was to use signal processing techniques borrowed from existing radar and sonar systems. This allows the accurate determination of the position of multiple objects. Processing data in real time demands a fairly powerful processing system. The second idea was to assign a microprocessor to each transducer. To support the use of multiple sensors in the final measurement setup, transputers were used as processing elements as they offer easy scalability because of their serial links. This sensor measured the distance to multiple objects very accurately and with a resolution of 2 cm. It is shown that these techniques could be implemented in a cost-effective manner.<<ETX>>

Simulated head related transfer function of the phyllostomid bat Phyllostomus discolor

UNLABELLED This paper presents a calculation of the head related transfer function (HRTF) for the frontal hemisphere of the phyllostomid bat Phyllostomus discolor using an acoustic field simulation tool based on the boundary element method. From the calculated HRTF results, binaural interaural intensity differences (IIDs) are derived. THE RESULTS Region of highest sensitivity, HRTF patterns, and IID patterns are shown to be in good agreement with earlier experimental measurements on other specimens of the same bat species, i.e., the differences are within the interspecies variability range. Next, it is argued that the proposed simulation method offers distinct advantages over acoustic measurements on real bat specimens. To illustrate this, it is shown how computer manipulation of the virtual morphology model allows a more detailed comprehension of bat spatial hearing by investigating the effects of different head parts on the HRTF. From this analysis it is concluded that for this species the pinna has a significantly larger effect on the HRTF and IID patterns than the head itself. This conclusion argues in favor of a series of recent simulation studies based on pinna morphology only [R. Muller, J. Acoust. Soc. Am. 116, 3701-3712 (2004); Muller et al., ibid 119, 4083-4092 (2006)].

Tri-aural perception on a mobile robot

To overcome the problems associated with the use of ultrasonic sensors for navigation purposes, the authors propose a measurement system composed of three ultrasonic sensors, one transmitting and three receiving, placed on a moving vehicle. By triangulation, this tri-aural sensor is able to determine the position, both distance and bearing, of the objects in the field of view. However, based on the information from one single snapshot, the sensor cannot distinguish between corners and walls. A statistical test that combines consecutive sightings of the same object to determine whether it is a wall or a corner is derived. This test is formulated as a sequential test which guarantees that the object will be recognized after the minimal number of measurements given a predetermined error probability. Preliminary experimental data show the feasibility of the approach.<<ETX>>

Simulating the morphological feasibility of adaptive beamforming in bats

It has been suggested that it is advantageous for bats to adapt their emission beam pattern depending on the situation. Hartley [9] has proposed that bats could steer the direction in which they emit most energy by controlling the phase relationship between the sound emerging from both nostrils. In this paper, we evaluate based on simulations, whether such an adaptive mechanism would be viable in FM bats given their specialized facial morphology. We find that these bats could indeed relocate the center of their emission beam pattern using a phased array mechanism. Furthermore, we list two ways in which this would help bats localizing target objects.

Merging Ultrasonic Sensor Readings Into A Consistent Environment Model

The algorithm presented in this paper constructs a geometric model of the environment using ultrasonic sensors. To do this in a reliable way, it has to take different error sources into account. Unlike other approaches, where a low-level, pixel based, probabilistic model is constructed to represent the uncertainty arising from false measurements, a high level, geometric, model is constructed. It is shown that a high level model, besides being faster to construct, is more appropriate for taking into account the typical characteristics of ultrasonic sensors. The algorithm detects and eliminates inconsistent measurements by combining evidence gathered from different points of view. This is made possible by extracting from the measurements not only information concerning the position of obstacles, but also information about regions that must be empty when seen from a certain angle. To conclude, some examples of the behaviour of this algorithm in real-world situations are presented.

Mobile robot navigation based on flexibility maps of the environment

We propose a unified mathematical framework for local navigation and position localization of a mobile robot which is based on the use of flexibility maps of the environment. Flexibility maps contain both the current knowledge of the robot about the positions of objects as well as the information required to calculate its future path. The major benefit of our approach is that several navigation tasks in mobile robotics can be incorporated elegantly into a single mathematically well founded framework.

A correlation coprocessor for accurate real-time ultrasonic ranging of multiple objects using the transputer

In mobile robotics one often uses ranging sensors because they are cheap and provide relatively accurate ranging data. In their simplest form, ultrasonic sensors provide the distance to the nearest discernible object with an angular uncertainty of 10° to 20°. Although this information is suitable for collision avoidance, it is absolutely inadequate for more complex tasks like building an environment map. One has to consider more sophisticated methods. In this paper we briefly describe a radar-inspired method to extract more information from a single ultrasonic reading than is possible with the traditional approach. In addition, we describe a coprocessor ASIC design that significantly accelerates the required signal processing task on a low-cost transputer system. The coprocessor behaves like an ‘intelligent’ A/D converter in that it performs the actual acquisition of the sensors analogue signal, as well as a simple correlation operation. It is connected to the transputer by means of a serial link, which permits effective parallelization of the signal processing steps. The resulting system has been evaluated and performs according to expectations.

Steps toward triaural perception

By virtue of their low cost and simplicity, ultrasonic sensors are widely used in rangefinding applications. However, when a more detailed map of the environment is needed, as is the case in mobile robotics, the simple-minded use of a single ultrasonic sensor is often insufficient. In this paper we propose a measurement system composed of three ultrasonic sensors, one transmitting and three receiving, to overcome these problems. By combining the information from the three sensors we can accurately determine the position, both in distance and bearing, of all objects in the field of view taking one single snapshot of the scene. Within the limits derived in this paper, it is even possible to discriminate between different types of reflectors. To get these results we have to find the reflections belonging to the same object in the signals received by the three sensors, a problem analogous to the correspondence problem in stereo- vision. We present a maximum likelihood approach as a solution to this problem. A realistic scene will be used to compare our sensor with the traditional ultrasonic ranging sensor.

A Transputer-based Neural Network To Determine Object Positions From Tri-aural Ultrasonic Sensor Data

For mobi le robot navigation purposes, we use a n object posit ion in easurernent s yst e m . TIL e in easureiii erit system is composed of two parts, a signal acquistion part and the signal procssing pari. The first pard consists of three u1t ras o ri ic s ens o rs, on e t ra nsni it t ing a n d three receiving. In the second part,the topic of this paper, we use a transputer based neural network t o process the acoustical signals from t h e receivers. We shotu how to combine ultrasonic sensor data with a n artific ia l neural network to estimate object positions (distance r and bearing 6) . Acoustic signals f rom the three sensors are preprocessed ana! then p a s s e d through t h e neural network. The object positions are determined b y the neural network. Experimental results on object position measurement show that a neural network of the multilayer perceptron type can be trained to do this task, and that the resulting systeiu is reasonably sensitive and accurate.