In recent years the development of Simultaneous Localization and Mapping (SLAM) techniques have enabled social robots to autonomously navigate in routine human workplaces. However, most common SLAM techniques are developed for mapping and localization in static worlds. In this paper, we have developed and analyzed a novel augmentation of the FastSLAM algorithm, including a person tracking module using 2D LiDAR sensor data. Utilizing this module, the SLAM algorithm is capable of filtering measurements coming from walking people who produce noises due to their intrinsic dynamic and unstableness. This augmentation was developed and then tested on our socially assistive mobile robot platform, Arash, while moving in populated environments utilizing Robotic Operating System (ROS) as a middleware. This new approach demonstrated a clearer representation of the mapped environment and therefore more accurate localization and navigation compared to its static SLAM predecessor.
Interaction quality improvement in a social robotic platform can be achieved through intention detection/prediction of the user. In this research, we tried to study the effect of intention prediction during a human-robot game scenario. We used our humanoid robotic platform, RASA. Rock-Paper-Scissors was chosen as our game scenario. In the first step, a Leap Motion sensor and a Multilayer Perceptron Neural Network is used to detect the hand gesture of the human-player. On the next level, in order to study the intention prediction’s effect on our human-robot gaming platform, we implemented two different playing strategies for RASA. One of the strategies was to play randomly, while the other one used Markov Chain model, to predict the next move. Then 32 players with the ages between 20 to 35 were asked to play Rock-Paper-Scissors with RASA for 20 rounds in each strategy mode. Participants did not know about the difference in the robot’s decision-making strategy in each mode and the intelligence of each strategy modes as well as the Acceptance/Attractiveness of the robotic gaming platform were assessed quantitatively through a questionnaire. Finally, paired T-tests indicated a significant difference between the random playing strategy and the other strategy predicting players’ intention during the game.
Simultaneous Localization And Mapping (SLAM) is regarded as one of the most significant advances in robotics in recent decades. It enables robots to map their surrounding environments without the need for external equipment and simultaneously determine their position on this map. In the present study, this technique was implemented on Arash social robot to enable it to estimate its position in the environment with decent accuracy and potentially be able to automatically move towards the targets determined on the map using a path planning algorithm. At first, Arash robot mechanically was redesigned for this technique, and then the hardware and software needed to high-level and low-level control of the robot were developed. By conducting the literature review of SLAM techniques, the FastSLAM algorithm was found to be suitable for employing on Arash robot, and it was implemented using Robotic Operating System (ROS). Then, to improve the quality of the SLAM technique in crowded and dynamic environments, a person detection and tracking module were implemented on the Arash robot. In the experiments, it was observed that this module improves the quality of mapping and pose estimation of the robot in a crowded environment by filtering the data of moving people from the laser rangefinder sensor and publishing these data to the FastSLAM algorithm.
This article presents the thorough design procedure, specifications, and performance of a mobile social robot friend Arash for educational and therapeutic involvement of children with cancer based on their interests and needs. Our research focuses on employing Arash in a pediatric hospital environment to entertain, assist, and educate children with cancer who suffer from physical pain caused by both the disease and its treatment process. Since cancer treatment causes emotional distress, which can reduce the efficiency of medications, using social robots to interact with children with cancer in a hospital environment could decrease this distress, thereby improving the effectiveness of their treatment. Arash is a 15 degree-of-freedom low-cost humanoid mobile robot buddy, carefully designed with appropriate measures and developed to interact with children ages 5–12 years old. The robot has five physical subsystems: the head, arms, torso, waist, and mobile-platform. The robot’s final appearance is a significant novel concept; since it was selected based on a survey taken from 50 children with chronic diseases at three pediatric hospitals in Tehran, Iran. Founded on these measures and desires, Arash was designed, built, improved, and enhanced to operate successfully in pediatric cancer hospitals. Two experiments were devised to evaluate the children’s level of acceptance and involvement with the robot, assess their feelings about it, and measure how much the robot was similar to the favored conceptual sketch. Both experiments were conducted in the form of storytelling and appearance/performance evaluations. The obtained results confirm high engagement and interest of pediatric cancer patients with the constructed robot.
Technology is inevitable, and its role for clinical therapists and specialists cannot be ignored. The promising movement towards computer-based interventions, specifically the use of tablets as an effective and newly developed learning device for children with autism spectral disorder (ASD) highlights the role of technology in addressing the shortcomings of conventional therapy methods. In this paper, we present a new application, named as Xylotism, which is an interactive game to improve learning and teach music to children with autism spectrum disorder. The game can be played with/without parents/therapists’ involvement, which increases its usefulness and effectiveness. We have investigated the acceptability and eligibility of the application among twenty children with autism spectrum disorder. To have a fair comparison a similar experiment has been performed utilizing a real Xylophone. The initial results are promising, since all participants accepted to play with Xylotism application and fifteen out of the twenty of them preferred to use the virtual Xylophone. We also video recorded the activities and coded the children’s behavior, and their performance in playing rhythms were measured. The results suggest that the acceptance of Xylotism application is fairly compatible to the real Xylophone and it has the potential to improve the skill of playing rhythms in children with ASD.