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.
Highlights
In recent years, the fields of application of robotics have grown enormously
The planner used in this work is The Elastic Band (TEB) developed by the Dortmunt University and provided by the teb_local_planner Robot Operating System (ROS) package
This paper has presented the development and its implementation of an autonomous navigation system for mobile robots
Summary
The fields of application of robotics have grown enormously. in addition to typical industrial applications (pick and place, arc or spot welding, machine tending, etc.), nowadays robots are being adopted in fields such as medicine [1,2], along with assistance robots [3,4], rescue robots [5], robots that replace human operators in inaccessible or dangerous environments [6], etc. A middleware can be defined as “the software layer that lies between the operating system and applications on each side of a distributed computing system in a network” and provides a common programming abstraction through distributed systems [16], improving portability, reliability and reducing the complexity of the systems [17] In this regard, the use of robot control middleware avoids the problems and limitations of traditional approaches. Player [25] or Brics [26] In this sense, because ROS is component-based free software middleware that has many tools and functionalities useful in the development of robotic applications, ROS outperforms other algorithms when applied to vehicle control, the merging of data from multiple sensors and the timestamping of various devices [27].
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