LEE- Bilgisayar Mühendisliği Lisansüstü Programı
Bu topluluk için Kalıcı Uri
Gözat
Konu "Bağımsız robotlar" ile LEE- Bilgisayar Mühendisliği Lisansüstü Programı'a göz atma
Sayfa başına sonuç
Sıralama Seçenekleri
-
ÖgeA social navigation approach for mobile assistant robots(Lisansüstü Eğitim Enstitüsü, 2020) Kıvrak, Hasan ; Köse, Hatice ; 657666 ; Bilgisayar Mühendisliği Bilim DalıRobots are becoming a part of our lives and we expect robots to act in a similar way to avoid interference with our safety and social being. Robots which are employed in human-robot interactive social areas such as malls or hospitals should benefit from a compliant navigation approach that is built upon a level of human aware and socially intelligent behavior. This compliance is more than mere avoidance and requires legible robot motion so that rational agents as humans are should understand and predict the robot motion (eliminate uncertainty in robot behavior) to adapt their motions accordingly. Furthermore, the robot requires understanding social etiquettes and rules and anticipates social/ethical interactions as much as humans. Otherwise, no matter how efficiently the robot navigates from one point to another, it will be realized as an unsocial individual because of the possibility of violating people's social zones or blocking their way. Hence, the robot behavior will be realized as inhuman-like and affect the interaction quality with the humans negatively. Mobile robots with enhanced social skills by considering to interact with humans verbally or non-verbally (e.g. sign language) should have unified trajectory planning algorithms that not only calculate the shortest path while avoiding obstacles to the defined goal while navigating, but also have human awareness not to annoy any human. A large number of researchers are currently proposing socially aware navigation approaches. It is an active research field combining navigation, perception, and social intelligence. The primary motivation of all these approaches is increasing psychological safety and comfort in human-robot interactive social environments as much as possible. ROS is the de facto standard in research robotics and offers us the ability to use multiple platforms and languages and to incorporate standard solutions to robot problems. Therefore, we first integrated the Mantaro TeleMe2 telepresence robot into the ROS ecosystem to drive the robot autonomously through the newly proposed hardware architecture. Then, the robot is made ready to provide all the necessary nodes to perform social navigation by developing Teleme2 ROS packages from scratch. Robot navigation in an unknown dynamic environment prefers to solve localization and mapping problem concurrently. As a result, the robot uses simultaneous localization and mapping(SLAM) technique to localize itself (pose estimation) and map the environment as well as our socially-aware motion planning algorithm. For online motion planning, potential fields are a common approach for static environments. This approach is first adopted as a social force model (SFM) to describe the motion of pedestrians in very crowded escape scenarios. According to this model, human behavior is affected by some forces (think of a vector field over the space) for acceleration, deceleration and directional changes. The idea behind the model makes it a good candidate for local path planning and expected to generate more human-like trajectories for the robot. That enables a robot to imitate the comprehensibility of the inner dynamics of human motion efficiently dependent on its motion constraints. SFM-based motion planner is computationally light which is appropriate in an uncertain dynamic environment to re-plan frequently. The algorithm does not directly find a collision-free path for the robot. The technique outputs the desired acceleration vector through the dynamic interactions of the robot at each time step and integrates the acceleration into its motion in order to obtain the collision-free path. At every point in time, the robot looks at the resultant total force at the point and imposes/applies as a control law to determine the direction of travel and speed. SFM may be a good choice since we don't need to enforce that the robot exactly follows a reference path, but instead stays within limits guaranteeing people's safety and comfort. In the thesis, we propose a social navigation system under unknown environments by integrating SFM and SLAM. Except for SFM computational time efficiency, the application of conventional SFM to social robot navigation problems present shortcomings and limitations. One problem of the integration of two technologies is the noise of SLAM that causes undesired navigation of the social force model. We introduce the idea of multi-level mapping to filter the noise within reasonable computational cost. The other problem is that the robot may oscillate because it has no incentive not to do so due to sudden changes in force lengths, discontinues at some points and sensor noise. To this end, one solution is to ensure smoothing by constraining the change in forces. That way, you impose continuity in the steering. In addition, SFM-based local motion planner is used with A* global planner not to be stuck on local minima situations. The whole plan is not directly assigned to the robot since the global path has too many grid nodes and it is infeasible to follow the path in such a dynamic human uncertain environment. Therefore, the key path points of the global path are extracted by proposed subgoals selection algorithm. Extracted points are incrementally passed to the robot for smooth and legible robot navigation behavior. Finally, we conduct simulation and user experiments as well as evaluate the effect of the proposed idea. We verify the results in real environments as simulation environments have limitations with quantitative and qualitative evaluations. This study has been developed as a part of TUBITAK project 118E214. In the future, we will continue to develop the study further, for the social navigation of assistive robots in crowded environments such as hospitals and schools in accordance with the safety and social distance rules.