| Περίληψη: | Exploration and Mapping is a fundamental task in robotics as it provides important information about an unknown environment that can be crucial for many applications. Many areas are either too dangerous or infeasible for humans to explore. Therefore ground and aerial robots are often used to perform this task. In the past, the teleoperation of a single robot was the primary method of completing these types of missions. However, the improvements in the computational capability of mobile agents and the development of new algorithms have made it possible for them to operate autonomously and even collaborate to achieve a common goal.
This thesis aims to describe the complete algorithmic process required to solve the Autonomous Multi-Agent Exploration and Mapping problem and develop a system capable of successfully performing this task. For this purpose, a distributed planning pipeline that generates and optimizes exploration paths using Genetic Algorithms in real-time is implemented. The pipeline is then integrated into a set of simulated UAVs that are part of a more extensive ROS-based system. This system consists of a high-level controller in the form of Behavior Trees that is responsible for the coordination of the agents and the safe execution of tasks and low-level controllers that enable them to navigate autonomously. To implement all the required functionality, a set of software components were developed, which are publicly available.
The overall system is evaluated in simulation environments of different complexities. The experiments indicate that the proposed method can successfully explore and map an unknown environment and that the use of multiple agents is beneficial both in terms of computational efficiency and speed of exploration.
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