Ciência da Computação
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/342
Navegar
3 resultados
Resultados da Pesquisa
- Proposal of a new model for social navigation based on extraction of social contexts from ontology in service robots(2019-10-05) PIMENTEL, F.; Plinio Thomaz Aquino Junior© 2019 IEEE.With the growing presence of service robots in social environments, research into the development of social behavior in robots is becoming increasingly necessary. Service robotics and social navigation are research areas that have been growing in recent years. However, helping humans with everyday tasks as well as people who need special attention, the elderly and children are still challenging issues in both the domestic and commercial environments. In this paper we present the current problem of social navigation in service robots, a state-of-the-art review of social navigation models, and we propose a new social navigation model using context extraction from ontology. This is expected to improve both the naturalness and the sociability of the robot as well as the comfort of the human being. This project intends to use current detection and learning techniques and tools such as OpenPose, as well as semantic mapping and deep learning. Navigation experiments were performed in simulated environment and selection of a navigation method. Also started implementing people tracking using OpenPose with satisfactory preliminary results. It is hoped that with this project, we can collaborate with the field of social navigation research using ontology approach.
- Performance evaluation of ROS local trajectory planning algorithms to social navigation(2019-10-05) PIMENTEL, F.; Plinio Thomaz Aquino Junior© 2019 IEEE.Accuracy and safety are necessary characteristics in social navigation and still constitute a challenge. The ROS Navigation Stack (RNS) allows the variation of local path planning methods through plugins for navigation. This paper brings you the comparison of those methods, which are directly connected with the safety and naturalness of the robot. Therefore, four different methods were compared by varying the sensors and the simulated environment. A thousand experiments were performed for each combination using the standard parameters of each method in a total of 24000 experiments. This paper concluded that the Elastic Band (EBand) method presents more safety and accuracy than the Dynamic window approach (DWA), method commonly used in several robots that participate in RoboCup@home, so it is more suitable for social navigation - reaching 90% accuracy in some cases and collision rate below 5%.
- Evaluation of ROS Navigation Stack for Social Navigation in Simulated Environments(2021-08-05) PIMENTEL, F. A. M.; Plinio Thomaz Aquino Junior© 2021, The Author(s), under exclusive licence to Springer Nature B.V.Accuracy and safety are necessary characteristics in social navigation. These characteristics still constitute a challenge in this area. Yet, human comfort is the main goal in interactions involving human beings. The ROS Navigation Stack (RNS) allows the variation of local path planning methods. This paper consists in a comparative study of methods related to social navigation. This study promotes better social navigation on Home Environment Robot Assistant (HERA). This is a robot platform developed by FEI University Center. This work evaluated various parameter combinations: type of environments, types of obstacles, local and global planning algorithms and costmaps. The work also evaluated people in static, dynamic and interacting ways. This study observed aspects of safety, accuracy of estimated time and space. Other aspects observed are the smooth trajectory realized and respect for personal space. The experiments performed 1000 attempts for 37 combinations of methods, environments and sensors. In total, the experiments counted 37000 attempts. With these experiments, was possible to select a configuration for the navigation system. The point to the Timed Elastic Band (TEB) as a local planner and a proxemic costmap as a good combination. The results reach 97.6% of success in a more complex environment with this combination.