Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within a multidimensional space. The particles explore the space in an attempt to find minima or maxima to the optimization problem. The motion of the particles is linked, and the overall behavior of the particle swarm is controlled by several parameters. PSO has been proposed as a control strategy for physical swarms of robots that are localizing a source; the robots are analogous to the virtual particles. However, previous attempts to achieve this have shown that there are inherent problems. This paper addresses these problems by introducing a modified version of PSO, as well as introducing new guidelines for parameter selection. The proposed algorithm links the parameters to the velocity and acceleration of each robot, and demonstrates obstacle avoidance. Simulation results from both MATLAB and Gazebo show close agreement and demonstrate that the proposed algorithm is capable of effective control of a robotic swarm and obstacle avoidance.

Subwavelength motion estimation is vital for the production of focused synthetic aperture sonar (SAS) imagery. The required precision is obtainable from the sonar data itself through a process termed micronavigation. Along-track micronavigation is achieved by a similar technique to that used in correlation velocity logs (CVLs), where sparse estimates of the spatial coherence function are interpolated to estimate the location of the peak coherence and hence estimate the interping vehicle motion. However, along-track micronavigation estimates made using this technique are biased, which limits the utility of these measurements for long-term navigation of autonomous underwater vehicles (AUVs). Three sources of along-track motion estimation bias are considered in this article. First, imperfect temporal registration between the signals results in coherence estimates that are negatively biased as a function of the temporal offset. Second, the sparse estimates of the spatial coherence function are obtained by cross-correlation of complex baseband signals, a process which is known to result in positively biased coherence estimates, especially when the true coherence is low. Finally, mismatches between the underlying spatial coherence function and the interpolation kernel used to estimate the peak coherence location also result in along-track micronavigation bias. In this article, we describe and evaluate three methods for reducing along-track micronavigation bias. We introduce a temporal registration of the signals before coherence estimation, which reduces the impact of negative coherence bias due to temporal offsets. The remaining coherence estimation bias is reduced by combining multiple coherence estimates in a Bayesian coherence estimator. Additionally, an improved interpolation kernel is derived with a significantly improved fit compared to the current gold standard Gaussian interpolation kernel. The improvements in along-track micronavigation accuracy are demonstrated using two simulated data sets, which both allow comparison with ground truth. The first involves direct simulation of the spatial coherence from a given interping geometry using the pulse-echo formulation of the van Cittert–Zernike theorem, while the second involves simulation of raw sonar echo data using a point-scatterer model. Using these simulations, a reduction in along-track micronavigation bias of 48.5%–99.5% is demonstrated, with reductions in along-track micronavigation error standard deviation of up to 34%. This improvement expands the potential for SAS-equipped AUVs to reduce their long-term navigation drift, facilitating longer underwater transits, improved target localization, and reduced track misalignment in repeat-pass operations.

Porous and composite piezoelectric ceramics are of interest for underwater ultrasonic transducers due to their improved voltage sensitivity and acoustic matching with water, compared to their dense counterparts. Commonly, these materials are fabricated by dice-and-fill of sintered blocks of polycrystalline piezoceramic, which results in a high volume of waste. The freeze-casting technique offers a low waste and scalable alternative to the dice-and-fill method to produce porous piezoceramics with highly orientated, anisometric pores. In this paper, we have fabricated underwater ultrasonic transducers from freeze-cast lead zirconate titanate (PZT) with a range of porosities. The porous PZT samples were characterised in terms of their piezoelectric and dielectric properties before being encapsulated for acoustic performance testing in water. Off resonance, the on-axis receive sensitivity of the manufactured devices was approximately -200 ± 10 dB re 1 V/μPa; the transmit voltage response (TVR) was in the range of approximately 115 ± 5 dB re 1 μPa/V at 60 kHz to 132.5 ± 2.5 dB re 1 μPa/V at 180 kHz. The most porous transducer devices (0.51, 0.43, and 0.33 pore fraction) exhibited primarily a thickness-mode resonance, whereas the least porous transducers (0.29 pore fraction and the dense benchmark) exhibited an undesired radial mode, which was observed as an additional resonant peak in the electrical impedance measurements and lateral off-axis lobes in the acoustic beampatterns. Our results show that the acoustic sensitivities and TVRs of the porous freeze-cast transducers are comparable to those of a dense pressed transducer. However, the freeze-cast transducers with porosity exceeding 0.30 pore fraction were shown to achieve an effective structure with aligned porosity that suppressed undesired radial-mode resonances.

Accurate time delay estimation between signals is crucial for coherent imaging systems such as synthetic aperture sonar (SAS) and synthetic aperture radar (SAR). In such systems, time delay estimates resulting from the cross-correlation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery. In this article, an algorithm is introduced for the detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit 1-D and 2-D models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase center (RPC) micronavigation. Phase wrap errors are then corrected by recalculating the phase wrap number using the best-fitting model. The approach is demonstrated using the data collected by the 270–330 kHz SAS of the NATO Centre for Maritime Research and Experimentation Minehunting unmanned underwater vehicle for Shallow water Covert Littoral Expeditions. Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using 1-D and 2-D models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2-D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1-D and 2-D RANSAC methods outperform the branch-cuts method, with improvements of 29%–125% and 30%–150%, respectively, compared to 16%–134% for the branch-cuts method for this data set.

OctoMap is an efficient probabilistic mapping framework to build occupancy maps from point clouds, representing 3D environments with cubic nodes in the octree. However, the map update policy in OctoMap has limitations. All the nodes containing points will be assigned with the same probability regardless of the points being noise, and the probability of one such node can only be increased with a single measurement. In addition, potentially occupied nodes with points inside but traversed by rays cast from the sensor to endpoints will be marked as free. To overcome these limitations in OctoMap, the current work presents a mapping method using the context of neighbouring points to update nodes containing points, with occupancy information of a point represented by the average distance from a point to its k-Nearest Neighbours. A relationship between the distance and the change in probability is defined with the Cumulative Density Function of average distances, potentially decreasing the probability of a node despite points being present inside. Experiments are conducted on 20 data sets to compare the proposed method with OctoMap. Results show that our method can achieve up to 10% improvement over the optimal performance of OctoMap.

The accumulation of navigation errors (drift) is a problem in many applications of autonomous underwater vehicles (AUVs), particularly during long-duration underwater surveys. Traditional methods for correcting drift require either surfacing of the vehicle for a global navigation satellite systemupdate or use of an independent acoustic positioning system. These methods may not be desirable or possible due to mission constraints. We propose a solution to this problem completely underwater and without the aid of external navigation systems. The approach is based on an operational concept that uses a modified paired-track survey pattern combined with through-the-sensor navigation corrections from a seafloor imaging sonar. We describe the operational concept, derive a model for its performance limits, validate this model, and demonstrate the concept with real experiments at sea. Using this approach, we provide an opportunity to use either coherent or incoherent through-the-sensor positioning corrections for a mission length increase of only the product of the intratrack spacing and the number of track pairs. We show results from a proof-of-principle experiment using data collected by the ${\text{300-kHz}}$ synthetic aperture sonar of the NATO Centre for Maritime Research and Experimentation’s Minehunting Unmanned underwater vehicle for Shallow water Covert Littoral Expeditions.