A research group of a Spanish university has developed the cooperaTive ExploRation Routing Algorithm (TERRA), a planning system that implements a paradigm for a novel cooperation between a team of heterogeneous robots, such as Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), to carry out a large-scale exploration with a high level of autonomy.
Over the last decade, there has been a strong scientific and industrial concern in robotic cooperation. From problems of surveillance in industrial, commercial or domestic environments, or rescue and help in catastrophic areas, till problems of efficient package delivery in companies like Amazon or DHL, require mathematical optimization algorithms that solve these problems optimally and efficiently through the deployment of cooperative robot teams.
The paradigm implemented by TERRA represents the interactions among the robots to carry out the designated tasks. TERRA defines the UGV as a moving charging station that transports one or several UAVs to certain secure locations from which the UAVs can access the target tasks. In this way, the UGV makes available to the UAV its transport capacity and mobility on different terrains, so that the UAV can reach the objective aerial tasks. Also, the UAV can use the UGV as a charging station to recharge its battery and continue the exploration.
TERRA is a route planner that develops a five-stage strategy to design a cooperative route to a specific exploration scenario.
The first stage of TERRA is to find the safe areas from which the UAVs can access the objectives. For this, TERRA implements computational geometry algorithms to systematically find the best locations that allow access to every objective.
The second stage focuses on optimizing the number of safe areas chosen in the previous stage, guaranteeing the total coverage of the objectives. For this, TERRA implements set optimization algorithms that find an optimal solution to this problem.
The third stage consists of finding the optimal two-dimensional route for the UGV, along all the previously selected safe areas. The objective of this stage is to minimize the distance traveled between the safe zones, and so, minimize the total time of the mission. TERRA implements a specialized genetic algorithm to solve the discrete optimization problem known as the Traveling Salesman Problem.
The fourth stage aims to compute the three-dimensional route for the UGV. For this, TERRA implements an artificial intelligence algorithm specifically developed to find the route in three dimensions with the lowest energy expenditure for the UGV.
The fifth stage consists of computing the route in three dimensions for the UAV. In this stage, TERRA implements a search algorithm specifically designed to find the fastest and most efficient route for the UAV.
Finally, the five-stage strategy carried out by TERRA allows it to be a highly scalable, robust and efficient system, in addition to being able to focus on the optimization of various resources within the mission, such as energy and time.
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Also research cooperation agreements among the companies that may have a potential interest in this technology.