Trajectory Planning For Car-like Robots Through Curve Parametrization And Genetic Algorithm Optimization With Applications To Autonomous Parking



Car-like mobile robots, Trajectory generation, Parallel parking, Genetic algorithm


Parallel parking a car is a difficult task and may be frustrating and stressful for the driver, while commonly causes traffic jam. One way to mitigate such negative effects is to provide vehicles with self-driving capabilities. As a cornerstone of a mobile robot's ability to move autonomously stands trajectory planning, which despite many works in the literature, is still considered an open problem especially with regards to nonholonomic vehicles such as car-like robots. Based on this scenario, this work presents a trajectory planning algorithm to parallel park car-like mobile robots based on polynomial parametrization and genetic algorithm optimization. The aim is to define a law of motion to lead the vehicle from an initial pose near a parking space to a final pose within the latter in a smooth way, with no interruption and avoiding any obstacles in the way. Simulation results validate the feasibility of the proposed algorithm which lays the foundation to broader studies.


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Author Biographies

Renan Vieira, State University of Rio de Janeiro (UERJ), Rio de Janeiro, Brazil

Born in Rio de Janeiro, Brazil, in 1991. He graduated in Electrical Engineering with an emphasis on Electronic Systems at the State University of Rio de Janeiro, Rio de Janeiro, Brazil, in 2020. He is currently pursuing a Masters in Control and Automation at the University of the State of Rio de Janeiro. His research interests include modeling and controlling mobile robots, electric vehicles and intelligent systems.

Eduardo Argento, Union of the Pontifical Catholic University (PUC), Rio de Janeiro, Brazil

Eduardo V. Argento is graduated in Electric Engineering with emphasis in Electronic Systems by the University of the state of Rio de Janeiro in 2020. Nowadays pursues a Master's Degree in Electric Engineering with focus in decision support methods at Pontifical Catholic University of Rio de Janeiro. His areas of interest are: control and automation, robotics, specially mobile, electronics and computational intelligence.

Téo Revoredo, State University of Rio de Janeiro (UERJ), Rio de Janeiro, Brazil

Electrical Engineer (emphasys in electronic systems) graduated at the State University of Rio de Janeiro (2003), Master in Electrical Engineering - Control Systems (process control and robotics) at the Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (2007) and Doctorate in Mechanical Engineering - Acoustics, Vibrations and Dynamics at the Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (2011) including 1 year at the Laboratory of Automation and Operational Research at ENAC/France. Currently Associate Professor at the Department of Electronics and Telecommunication Engineering and researcher at the Graduate Program in Electronic Engineering (PEL) at the State University of Rio de Janeiro. Member of the Institute of Electrical and Electronics Engineers (IEEE) and of the IEEE Robotics and Automation Society (RAS) on which currently advises the RAS Student Chapter at UERJ. Areas of interest include advanced control systems, mobile robotics, automation, electronics, instrumentation, micro-generation energy systems with renewable sources.


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How to Cite

Vieira, R., Argento, E., & Revoredo, T. (2021). Trajectory Planning For Car-like Robots Through Curve Parametrization And Genetic Algorithm Optimization With Applications To Autonomous Parking. IEEE Latin America Transactions, 20(2), 309–316. Retrieved from