The learning and use of traversability affordance using range images on a mobile robot

Date

2007-04-14

Author

Ugur, Emre
Dogar, Mehmet R.
Cakmak, Maya
Şahin, Erol

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

326
views

0
downloads

We are interested in how the concept of affordances can affect our view to autonomous robot control, and how the results obtained from autonomous robotics can be reflected back upon the discussion and studies on the concept of affordances. In this paper, we studied how a mobile robot, equipped with a 3D laser scanner, can learn to perceive the traversability affordance and use it to wander in a room filled with spheres, cylinders and boxes. The results showed that after learning, the robot can wander around avoiding contact with non-traversable objects (i.e. boxes, upright cylinders, or lying cylinders in certain orientation), but moving over traversable objects (such as spheres, and lying cylinders in a rollable orientation with respect to the robot) rolling them out of its way. We have shown that for each action approximately 1% of the perceptual features were relevant to determine whether it is afforded or not and that these relevant features are positioned in certain regions of the range image. The experiments are conducted both using a physics-based simulator and on a real robot.

Subject Keywords

Mobile robots, Animals, Robot control, Robot sensing systems, Psychology, Organisms, Shape, Robotics and automation, Object detection, Leg

URI

https://hdl.handle.net/11511/40279

DOI

https://doi.org/10.1109/robot.2007.363571

Conference Name

IEEE International Conference on Robotics and Automation

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

DEVELOPMENT OF AN EXTENSIBLE HETEROGENEOUS SWARM ROBOT PLATFORM Bilaloğlu, Cem; Turgut, Ali Emre; Şahin, Erol; Department of Mechanical Engineering (2022-1-13) This thesis introduces Kobot -- an extensible heterogeneous swarm robot platform. Kobot platform uses a common hardware and software architecture based on off-the-shelf components, 3-D printing, and open-source software that evolves with state of the art. Robots built using this common architecture range from wheeled to flying robots and formed a heterogeneous swarm. The common architecture enabled developing and testing systems for the lightweight flying robots on resourceful ground robots. As a result, Ko...
DEVELOPMENT OF A SOCIAL REINFORCEMENT LEARNING BASED AGGREGATION METHOD WITH A MOBILE ROBOT SWARM Gür, Emre; Turgut, Ali Emre; Şahin, Erol; Department of Mechanical Engineering (2022-9-09) In this thesis, the development of a social, reinforcement learning-based aggregation method is covered together with the development of a mobile robot swarm of Kobot- Tracked (Kobot-T) robots. The proposed method is developed to improve efficiency in low robot density swarm environments especially when the aggregated area is difficult to find. The method is called Social Reinforcement Learning, and Landmark-Based Aggregation (SRLA) and it is based on Q learning. In this method, robots share their Q tables ...
Human aware navigation of a mobile robot in crowded dynamic environments Hacınecipoğlu, Akif; Konukseven, Erhan İlhan; Department of Mechanical Engineering (2019) As mobile robots start operating in dynamic environments crowded with humans, human-aware and human-like navigation is required to make these robots navigate safely, efficiently and in socially compliant manner. People can navigate in an interactive and cooperative fashion so that, they are able to find their path to a destination even if there is no clear path leading to it. This is clearly a dexterity of humans. But the mobile robots which have to navigate in such environments lack this feature. Even perf...
Control of a differentially driven mobile robot using radial basis function based neural networks Bayar, Gökhan; Konukseven, Erhan İlhan; Buǧra Koku, A. (2008-12-01) This paper proposes the use of radial basis function neural networks approach to the solution of a mobile robot orientation adjustment using reinforcement learning. In order to control the orientation of the mobile robot, a neural network control system has been constructed and implemented. Neural controller has been charged to enhance the control system by adding some degrees of award. Making use of the potential of neural networks to learn the relationships, the desired reference orientation and the error...
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...

Citation Formats

E. Ugur, M. R. Dogar, M. Cakmak, and E. Şahin, “The learning and use of traversability affordance using range images on a mobile robot,” presented at the IEEE International Conference on Robotics and Automation, Rome, ITALY, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40279.