Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm

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.

[1]  Angel Valera,et al.  Event based distributed kalman filter for limited resource multirobot cooperative localization , 2019, Asian Journal of Control.

[2]  Francisco Valero,et al.  Efficient trajectory of a car-like mobile robot , 2019, Ind. Robot.

[3]  Towards a definition of the Internet of Things ( IoT ) , 2015 .

[4]  Markus Vincze,et al.  How Social Robots Make Older Users Really Feel Well - A Method to Assess Users' Concepts of a Social Robotic Assistant , 2012, ICSR.

[5]  Francisco Valero,et al.  Influence of the Friction Coefficient on the Trajectory Performance for a Car-Like Robot , 2017 .

[6]  Xuemin Shen,et al.  Connected Vehicles: Solutions and Challenges , 2014, IEEE Internet of Things Journal.

[7]  Takanori Shibata,et al.  Therapeutic Seal Robot as Biofeedback Medical Device: Qualitative and Quantitative Evaluations of Robot Therapy in Dementia Care , 2012, Proceedings of the IEEE.

[8]  Marina Valles,et al.  Event-Based Localization in Ackermann Steering Limited Resource Mobile Robots , 2014, IEEE/ASME Transactions on Mechatronics.

[9]  Seiga Kiribayashi,et al.  Redesign of rescue mobile robot Quince , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[10]  Vincent Dupourqué,et al.  A robot operating system , 1984, ICRA.

[11]  Jianda Han,et al.  Quadratic programming-based approach for autonomous vehicle path planning in space , 2012 .

[12]  Toshiharu Mukai,et al.  Development of a nursing-care assistant robot RIBA that can lift a human in its arms , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Yaonan Wang,et al.  Obstacle Classification and 3D Measurement in Unstructured Environments Based on ToF Cameras , 2014, Sensors.

[14]  Alexander Carballo,et al.  A Survey of Autonomous Driving: Common Practices and Emerging Technologies , 2019, IEEE Access.

[15]  Jiafu Tang,et al.  An optimization model for software component selection under multiple applications development , 2011, Eur. J. Oper. Res..

[16]  Carlos Llopis-Albert,et al.  Optimization approaches for robot trajectory planning , 2018 .

[17]  Gerd Wanielik,et al.  Vehicle localization in urban environments using feature maps and aerial images , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[18]  Joel W. Burdick,et al.  Robot Motion Planning in Dynamic, Uncertain Environments , 2012, IEEE Transactions on Robotics.

[19]  Yue Cao,et al.  Toward Efficient Electric-Vehicle Charging Using VANET-Based Information Dissemination , 2017, IEEE Transactions on Vehicular Technology.

[20]  Herman Bruyninckx,et al.  Open robot control software: the OROCOS project , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[21]  Javier Alonso-Mora,et al.  Planning and Decision-Making for Autonomous Vehicles , 2018, Annu. Rev. Control. Robotics Auton. Syst..

[22]  Mehrdad Dianati,et al.  A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications , 2018, IEEE Internet of Things Journal.

[23]  Andreas Pott,et al.  BRICS - Best practice in robotics , 2010, ISR/ROBOTIK.

[24]  Richard T. Vaughan,et al.  The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems , 2003 .

[25]  Francisco A. Candelas-Herías,et al.  Framework for Fast Experimental Testing of Autonomous Navigation Algorithms , 2019 .

[26]  Marin Marinov,et al.  A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles , 2020, Sustainability.

[27]  Diego Alonso,et al.  Generación Automática de Software para Sistemas de Tiempo Real: Un Enfoque basado en Componentes, Modelos y Frameworks , 2012 .

[28]  Francisco Valero,et al.  Assessment of the Effect of Energy Consumption on Trajectory Improvement for a Car-like Robot , 2019, Robotica.

[29]  William D. Smart,et al.  Middleware for Robots , 2002 .

[30]  David González,et al.  Continuous curvature planning with obstacle avoidance capabilities in urban scenarios , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[31]  J. Karl Hedrick,et al.  A Multi-Level Modularized System Architecture for Mobile Robotics , 2010 .

[32]  Grant D. Huang,et al.  Robot-assisted therapy for long-term upper-limb impairment after stroke. , 2010, The New England journal of medicine.

[33]  Seppo Kuikka,et al.  Robotic software frameworks and software component models in the development of automated handling of individual natural fibers , 2014 .

[34]  Gerhard P. Hancke,et al.  A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods , 2020, IEEE Access.

[35]  Javier Ruiz-del-Solar,et al.  Object recognition using local invariant features for robotic applications: A survey , 2016, Pattern Recognit..

[36]  Xiaohui Li,et al.  A unified approach to local trajectory planning and control for autonomous driving along a reference path , 2014, 2014 IEEE International Conference on Mechatronics and Automation.

[37]  Christos Katrakazas,et al.  Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions , 2015 .

[38]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[39]  Towards Efficient Electric Vehicle Charging Using VANET-Based Information Dissemination , 2020 .

[40]  F. Valero,et al.  Sustainability and optimization in the automotive sector for adaptation to government vehicle pollutant emission regulations , 2020 .

[41]  Andreas Eidehall,et al.  On path planning methods for automotive collision avoidance , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).