On the development and enhancement of artificial intelligence algorithms for swarm robots in real world applications

Abstract

Swarm robotics is an area where using artificial intelligence (AI) can show a great deal of improvement. Obstacle avoidance, object detection, mapping and navigation are some the major algorithms required for successful execution of various tasks in the field of robotics. There is a challenge in applying these algorithms in a manner that swarm robots can use effectively. These five areas can be further researched to provide a platform for real world applications. This research aims to tackle the challenges involved in applying the aforementioned algorithms to swarm robotics and comparing the results with single robot systems. These techniques can be optimized by leveraging the advantage of swarm robots communication and scalability. The proposed algorithms were tested and validated using swarm robots along with profiling and simulations. For obstacle avoidance, two algorithms were devoloped. The first used a novel and modified force field method and the second used artificial neural networks (ANN). The results showed that the modified force field method performed better for static environments while ANNs worked better for dynamic environments. For object detection, the proposed algorithm uses an image classifier developed using ANN. The image classifier was trained to identify blocks of various colours using a convolutional neural network technique. This algorithm was then applied to swarm robotics using two proposed methods and results showed that multiple robots viewing objects from different angles provided better results as compared to single robot systems. This was validated with a 97% accuracy. In two dimension (2D) mapping, the proposed algorithm was developed using simultaneous localization and mapping (SLAM). The results showed that a single robot can require upto 3.5x more time for covering a given area compared to a swarm size of ten robots. This research shows a great deal of contribution in applying swarm robotics for surveilance purposes by showcasing the ability for swarm robotics to coordinate and execute the required task in an efficient time frame. The proposed three-dimension (3D) mapping algorithm used octomaps and occupancy grids to map out an image taken from a camera mounted on swarm robots. The images were obtained from various angles using multiple swarm robots. AI algorithms with a focus on swarm robotics are developed and enhanced for real world applications including fire-fighting, surveillance, fault analysis and construction. Results showed that swarm robots were able to complete a given task by up to six times faster as compared to a single robot. The overall contribution of this research lays a platform for further applications by showcasing the effectiveness of robotic algorithms in a swarm robot environment.