Abstract
The application of mobile robots in autonomous navigation has contributed to the development of exploration tasks for the recognition of unknown environments. There are different methodologies for obstacles avoidance implemented in mobile robots; however, this research introduces a novel approach for a path planning of an unmanned ground vehicle (UGV) using the camera of a drone to get an aerial view that allows to recognize ground features through image processing algorithms for detecting obstacles and target them in a determined environment. After aerial recognition, a global planner with Rapidly-exploring Random Tree Star (RRT*) algorithm is executed, Dubins curves are the method used in this case for nonholonomic robots. The study also focuses on determining the compute time which is affected by a growing number of iterations in the RRT*, the value of step size between the tree’s nodes and finally the impact of a number of obstacles placed in the environment. This project is the initial part of a larger research about a Collaborative Aerial-Ground Robotic System.
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Daniel Tenezaca, B., Canchignia, C., Aguilar, W., Mendoza, D. (2020). Implementation of Dubin Curves-Based RRT* Using an Aerial Image for the Determination of Obstacles and Path Planning to Avoid Them During Displacement of the Mobile Robot. In: Rocha, Á., Pereira, R. (eds) Developments and Advances in Defense and Security. Smart Innovation, Systems and Technologies, vol 152. Springer, Singapore. https://doi.org/10.1007/978-981-13-9155-2_17
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DOI: https://doi.org/10.1007/978-981-13-9155-2_17
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