Grant: Research Tools and Instruments

Multi-robot systems have benefits such as being more robust to failures and being faster in accomplishing the tasks. In a distributed and multi-agent system, the whole team is more robust since the failure of one of the robots does not halt the entire mission. In applications such as surveying large areas or deploying first responders in natural disasters, multi-robots systems are able to achieve the objectives faster and more efficiently. Topics such as task allocation, formation control, and coordination in multi-robot systems are being actively investigated by researchers. With the advent of the recent machine learning and deep learning methods, promising results are being presented to address the existing problems such as time and space complexity in multi-agent systems. One of the key areas in multi-agent systems is multiple-robot simultaneous localization and mapping (SLAM), motivated by the fact that exploration and mapping tasks can be done faster and more accurately by multiple robots than by a single robot. Many collaboration-based operations need to be completed fast and autonomously and require localization and mapping. Some of these applications include fire fighting, cleaning operations (like removing marine oil spills), underwater exploration, rescue operations, and maintenance investigations. An important advantage of team-based SLAM is that different types of robots, equipped with different sensors, can provide a better understanding of the environment. The proposed equipment, a large-scale heterogeneous team of robots, is essential for research in multiple-robot SLAM and also for research in other programs such as formation control, task allocation, etc. The proposed equipment is urgently needed to be used in novel research programs such as using haptics and manipulation-based SLAM in multiple-robot SLAM, multi-robot active neural SLAM, developing multi-robot disinfection systems to prevent the spread of COVID-19, and improving the computational complexity of multi-robot systems using machine learning methods. The proposed equipment will be used to verify the algorithmic designs in real-world scenarios with dynamic and moving objects.