Feature Aggregation in Joint Sound Classification and Localization Neural Networks

This study addresses the application of deep learning techniques in joint sound signal classification and localization networks. Current state-of-the-art sound source localization deep learning networks lack feature aggregation within their architecture. Feature aggregation enhances model performance by enabling the consolidation of information from different feature scales, thereby improving feature robustness and invariance. This is particularly important in SSL networks, which must differentiate direct and indirect acoustic signals. To address this gap, we adapt feature aggregation techniques from computer vision neural networks to signal detection neural networks. Additionally, we propose the Scale Encoding Network (SEN) for feature aggregation to encode features from various scales, compressing the network for more computationally efficient aggregation. To evaluate the efficacy of feature aggregation in SSL networks, we integrated the following computer vision feature aggregation sub-architectures into a SSL control architecture: Path Aggregation Network (PANet), Weighted Bi-directional Feature Pyramid Network (BiFPN), and SEN. These sub-architectures were evaluated using two metrics for signal classification and two metrics for direction-of-arrival regression. PANet and BiFPN are established aggregators in computer vision models, while the proposed SEN is a more compact aggregator. The results suggest that models incorporating feature aggregations outperformed the control model, the Sound Event Localization and Detection network (SELDnet), in both sound signal classification and localization. The feature aggregation techniques enhance the performance of sound detection neural networks, particularly in direction-of-arrival regression.

Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning

In recent years, advancements have been made towards the goal of using chaotic coverage path planners for autonomous search and traversal of spaces with limited environmental cues. However, the state of this field is still in its infancy as there has been little experimental work done. Current experimental work has not developed robust methods to satisfactorily address the immediate set of problems a chaotic coverage path planner needs to overcome in order to scan realistic environments within reasonable coverage times. These immediate problems are as follows: (1) an obstacle avoidance technique which generally maintains the kinematic efficiency of the robot's motion, (2) a means to spread chaotic trajectories across the environment (especially crucial for large and/or complex-shaped environments) that need to be covered, and (3) a real-time coverage calculation technique that is accurate and independent of cell size. This paper aims to progress the field by proposing algorithms that address all of these problems by providing techniques for obstacle avoidance, chaotic trajectory dispersal, and accurate coverage calculation. The algorithms produce generally smooth chaotic trajectories and provide high scanning coverage of environments. These algorithms were created within the ROS framework and make up a newly developed chaotic path planning application. The performance of this application was comparable to that of a conventional optimal path planner. The performance tests were carried out in environments of various sizes, shapes, and obstacle densities, both in real-life and Gazebo simulations.

Online search of unknown terrains using a dynamical system-based path planning approach

Surveillance and exploration of large environments is a tedious task. In spaces with limited environmental cues, random-like search appears to be an effective approach as it allows the robot to perform online coverage of environments using a simple design. One way to generate random-like scanning is to use nonlinear dynamical systems to impart chaos into the robot's controller. This will result in generation of unpredictable but at the same time deterministic trajectories, allowing the designer to control the system and achieve a high scanning coverage. However, the unpredictability comes at the cost of increased coverage time and lack of scalability, both of which have been ignored by the state-of-the-art chaotic path planners. This study introduces a new scalable technique that helps a robot to steer away from the obstacles and cover the entire space in a short period of time. The technique involves coupling and manipulating two chaotic systems to minimize the coverage time and enable scanning of unknown environments with different properties online. Using this technique resulted in 49% boost, on average, in the robot's performance compared to the state-of-the-art planners. While ensuring unpredictability in the paths, the overall performance of the chaotic planner remained comparable to optimal systems.