Developing a VR Socially Assistive Robot Simulator Employing Game Development Tools

Alves, Silas Franco dos Reis; Uribe-Quevedo, Álvaro; Chen, Delun; Morris, Jon

doi:10.1109/gem56474.2022.10017982

Cited by 2 publications

(8 citation statements)

References 17 publications

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“…By relying on visual inputs from cameras, these functionalities enable robots to interact effectively with their environments. Convolutional Neural Networks (CNN) serve as the backbone for these tasks within modern robotics, with the You Only Look Once (YOLO) suite of algorithms emerging as particularly dominant [31], [62], [103]. The YOLO algorithms' efficiency in facilitating real-time processing has made them popular in robotic applications.…”

Section: B Ai Approaches Adopted In Wmrsmentioning

confidence: 99%

“…• Autonomous Wheelchair [24], [96]- [98] • Rescue and healthcare services in disaster scenarios [99] • Monitoring and visualisation of ROS data in healthcare [100] • Assistance for elderly people in healthcare settings [101] • Human-vehicle interaction in healthcare [68], [102] • Long-term care facilities (LTCFs) [31], [103] • Autonomous pedestrian following (following medical staff) [104] • Medication delivery and vital signs monitoring [105] Warehouse and logistics…”

Section: Researchmentioning

confidence: 99%

“…They ensure that while a robot remains agile in its movements, it doesn't endanger its integrity or that of its environment. [116], ICP 3 + SVM 4 [132], 2D multi-SLAM [77], GMapping [87], [88], [105], FNN 5 [27], Visual-SLAM + RTAB-Map 6 [126], RTAB-Map [94], Cartographer [65], Monocular SLAM + RL [36], KimeraMulti [133] 2 -Object Detection YOLOv4 7 [62], [103], YOLOv3 [31], [59], [62], YOLOv2 [134], PointNet [135], CNN 8 [21], [23], [136], SVM [137], MobileNet [138], YOLOv2 + JPDA 9 + IMM 10 [139] 3 -Object Tracking…”

Section: Householdmentioning

confidence: 99%

“…On the other hand, HITL has found a somewhat broader application, although it still presents limited engagement [24], [28], [29], [34], [53], [89], [98], [102], [103], [112], [119], [124], [176]. Most of these studies interpret HITL as a human control mechanism rather than a human-mediated testing method [28], [89], [98], [102], [112], [119], [176].…”

Section: Simulator Typementioning

confidence: 99%

“…Most of these studies interpret HITL as a human control mechanism rather than a human-mediated testing method [28], [89], [98], [102], [112], [119], [176]. Other HITL applications are concerned with human collaboration with robots [29], [124] and social interactions [103]. Only a few pioneering works are leveraging HITL in the robot development process, such as [24], where a robot system learns from user demonstrations and [34] where human feedback trains autonomous methods for unsafe action prediction.…”

Section: Simulator Typementioning

confidence: 99%

See 4 more Smart Citations

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROSenabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use "one size fits all" approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

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Section: B Ai Approaches Adopted In Wmrsmentioning

confidence: 99%