## 2 Navigation and Localization
### Acoustic Navigation
#### 2.2.1 Sonar  
There exist different methods to employ a SONAR for AUV navigation. Two basic configurations are the side-scan SONAR [10] and the Forward-Looking SONAR (FLS) [38]. Both of them are used to detect objects which can be: seabed changes, rocks, other vehicles, and even marine species. When an AUV is in operation, it must be able to detect these objects to update its navigation trajectory and avoid collisions, which is known as obstacle avoidance. For the side-scan SONAR, the transducer device scans laterally when attached to the AUV, as illustrated in Figure 4. A series of acoustic pings are transmitted and then received, the time of the returns and the speed of sound in water is used to determine the existence of features located perpendicular to the direction of motion. The FLS uses a searchlight approach, steering the sonar beam scanning forward of the vessel and streaming soundings on a continuous basis. FLS can be placed at different locations on the vehicle, as shown in Figure 5, to ensure that the AUV can detect obstacles from different directions. Two-dimensional images can be produced which survey the ocean and the features on it. These images, while indicating what exists on the ocean or seafloor, do not contain localization information either relative or global. Traditional obstacle avoidance planning methods include potential field, Bandler and Kohout (BK) products, particle swarm optimization, fuzzy controller, etc. [PERSON] et al. [39] designed an obstacle avoidance algorithm for an AUV. The obstacle detection system disposes of a SONAR and its use guarantees the safety of the AUV while navigating. Obstacle avoidance is performed based on a fuzzy reactive architecture for different forward speeds of the vehicle. The algorithm was validated under a computational simulation environment running in MATLAB. During the simulated route, the vehicle  
Figure 4: AUV equipped with two side-scan Sound Navigation and Ranging (SONARs). Figure 5: Forward Looking SONAR (FLS) placed at vertical and horizontal orientations. remained at a minimum distance of 5 m of the obstacles, reducing its reference forward speed of 1 m/s to values between 0.02 m/s and 0.4 m/s; thus, safe navigation around obstacles was achieved without losing the trajectory of navigation and reaching all the waypoints. [PERSON] et al. [40] proposed an obstacle avoidance methodology based in the data collected from two FLS placed in horizontal and vertical orientation. FLS data is processed to provide obstacle detection information in the _xz_-and _xy_-planes, respectively. For the horizontal obstacle avoidance, authors used a two-layer algorithm. The first process of the algorithm is based on BK products of fuzzy relation, as a preplanning method; and the second is a reactive approach based on potential field and edge detection methods. In case that the horizontal approach fails finding a path to safely avoid the obstacle, a reactive vertical approach is activated. The sonar used in the experimentation has a detection range of up to 137 m and operated at a 450 kHz frequency. During the test, the mission definition for the AUV was to move from a starting point to a target point. Despite the maximum range of the FLS, decisions were made when an obstacle was within 40 m of the AUV. [PERSON] et al. [41] implemented a Recurrent Neural Network (RNN) with Convolution (CRNN) for underwater obstacle avoidance. Offline training and testing were adopted to modify the neural network parameters of the AUV autonomous obstacle avoidance learning system, so self-learning is applied to the collision avoidance planning. Combining this learning system with FLS simulation data enables online autonomous obstacle avoidance planning in an unknown environment. Simulation results showed that the planning success rate was 98% and 99% for the proposed CRNN algorithms; meanwhile, it was 88% and 96% for the RNN algorithms.