Feedback Motion Planning For a Dynamic Car Model via Random Sequential Composition

Autonomous cars and car-like robots have gained huge popularity recently due to the recent advancements in technology and AI industry. Motion and path planning is one of the most fundamental problems for such systems. In the literature, kinematic models are widely adopted for planning and control for these type of robots due to their simplicity (control and analysis) and fewer computational requirements. Though, applicability of kinematic models are limited to very low speeds or some specific cases, which can be easily violated in real life scenarios. Furthermore, most of the dynamical car models found in the literature assume that they are driven only in forward direction, at constant high speeds. In this study, we present a car model that captures the dynamics of both forward and backward driving, at low and high speeds. After creating the car model, we addressed the motion planning problem on this model, where we adopted a framework which combines Sequential Composition of Controllers (SCC) and Rapidly Exploring Random Trees (RRT). We performed simulations to show the effectiveness and robustness of our method, and results are promising for future experimental studies.


Bingöl, Ulaş Süreyya; Ankaralı, Mustafa Mert; Hacınecipoğlu, Akif; Department of Electrical and Electronics Engineering (2022-5-12)
Autonomous navigation gained significant attention as it is necessary for an over- growing use and integration of autonomous systems to numerous fields such as self- driving cars, logistics, and agricultural robots. These applications require robots to navigate from one location to another, which requires them to estimate their position accurately. Localization loss scenarios are instances when localization accuracy dete- riorates to a level that endangers the task. Reaching the false end goals, being stuck...
Trajectory planning and tracking for autonomous vehicles
Çiçek, Haluk Levent; Schmidt, Klaus Verner; Department of Electrical and Electronics Engineering (2022-12-27)
Finding appropriate paths is an essential issue for the development of autonomous vehicles and robots. Hereby, it has to be considered that autonomous vehicles cannot follow sharp corners, as they cannot turn on a single point. Therefore, it is important to compute smooth paths that have additional desirable properties such as minimum length and sufficient distance from obstacles. Furthermore, practical applications require the computation of such paths in real time. This thesis develops a general method...
Optimal control of a half circular compliant legged monopod
Özkan Aydın, Yasemin; Leblebicioğlu, Mehmet Kemal; Saranlı, Afşar; Department of Electrical and Electronics Engineering (2013)
Legged robots have complex architecture because of their nonlinear dynamics and unpredictable ground contact characteristics. They can be also dynamically stable and exhibit dynamically dexterous behaviors like running, jumping, flipping which require complex plant models that may sometimes be difficult to build. In this thesis, we focused on half circular compliant legged monopod that can be considered as a reduced-order dynamical model for the hexapod robot, called RHex. The main objective of this thesis ...
Semi-supervised iterative teacher-student learning for monocular depth estimation
Süvari, Cemal Barışkan; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2021-2-18)
Advances in robotics area and autonomous vehicles have increased the need for accurate depth measurements. Depth estimation is one of the oldest problems of computer vision area. While the depth can be estimated by using many methods, finding a cheap and efficient way of doing it was studied for many years. Although, depth measurements using Lidar sensors or RGB-D cameras provides accurate results, due to cost and narrow applicability they are not very effective. On the other hand, using deep learning archi...
Implementation of a closed-loop action generation system on a humanoid robot through learning by demonstration
Tunaoğlu, Doruk; Şahin, Erol; Department of Computer Engineering (2010)
In this thesis the action learning and generation problem on a humanoid robot is studied. Our aim is to realize action learning, generation and recognition in one system and our inspiration source is the mirror neuron hypothesis which suggests that action learning, generation and recognition share the same neural circuitry. Dynamic Movement Primitives, an efficient action learning and generation approach, are modified in order to fulfill this aim. The system we developed (1) can learn from multiple demonstr...
Citation Formats
M. Özcan and M. M. Ankaralı, “Feedback Motion Planning For a Dynamic Car Model via Random Sequential Composition,” 2019, Accessed: 00, 2020. [Online]. Available: