Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm

Ángel Valera, Francisco Valero, Marina Vallés, Antonio Besa, Vicente Mata and Carlos Llopis-Albert
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Ángel Valera: Instituto de Automática e Informática Industrial (ai2), Universitat Politècnica de València, 46022 Valencia, Spain
Francisco Valero: Center of Technological Research in Mechanical Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Marina Vallés: Instituto de Automática e Informática Industrial (ai2), Universitat Politècnica de València, 46022 Valencia, Spain
Antonio Besa: Center of Technological Research in Mechanical Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Vicente Mata: Center of Technological Research in Mechanical Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Carlos Llopis-Albert: Center of Technological Research in Mechanical Engineering, Universitat Politècnica de València, 46022 Valencia, Spain

Sustainability, 2021, vol. 13, issue 3, 1-21

Abstract: Autonomous navigation is a complex problem that involves different tasks, such as location of the mobile robot in the scenario, robotic mapping, generating the trajectory, navigating from the initial point to the target point, detecting objects it may encounter in its path, etc. This paper presents a new optimal trajectory planning algorithm that allows the assessment of the energy efficiency of autonomous light vehicles. To the best of our knowledge, this is the first time in the literature that this is carried out by minimizing the travel time while considering the vehicle’s dynamic behavior, its limitations, and with the capability of avoiding obstacles and constraining energy consumption. This enables the automotive industry to design environmentally sustainable strategies towards compliance with governmental greenhouse gas (GHG) emission regulations and for climate change mitigation and adaptation policies. The reduction in energy consumption also allows companies to stay competitive in the marketplace. The vehicle navigation control is efficiently implemented through a middleware of component-based software development (CBSD) based on a Robot Operating System (ROS) package. It boosts the reuse of software components and the development of systems from other existing systems. Therefore, it allows the avoidance of complex control software architectures to integrate the different hardware and software components. The global maps are created by scanning the environment with FARO 3D and 2D SICK laser sensors. The proposed algorithm presents a low computational cost and has been implemented as a new module of distributed architecture. It has been integrated into the ROS package to achieve real time autonomous navigation of the vehicle. The methodology has been successfully validated in real indoor experiments using a light vehicle under different scenarios entailing several obstacle locations and dynamic parameters.

Keywords: autonomous navigation; obstacle detection and avoidance; collision-free trajectory; car-like mobile robot; sensors for autonomous vehicles (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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