Semi-autonomous Unmanned Ground Vehicle Research Articles

Design, Implementation, and Evaluation of a Semi-Autonomous, Vision-based, Modular Unmanned Ground Vehicle Prototype

In some traditional development processes, engineering teams communicate their subsystem interfaces without much overlap o their respective disciplines and processes. However, for a systems engineering-driven design, a holistic, multidisciplined approach is implemented from the ground up, with considerable overlap between the teams in every phase of the project. Approaching a system from a holistic perspective, rather than an isolated subsystem perspective, is a fundamental component to rapid prototype development and successful system integration. It is also required for full project-level concerns such as the data, security, safety, and sustainability operations. This paper presents the development of a prototype modular unmanned ground vehicle (UGV) used for fire detection and elimination. Taking a systems engineering approach, the mechatronics and control systems designs are performed first, then the system and the important subsystems are built and tested, and finally, the evaluation results are fed back for the next prototype iteration. The goal of this paper is to give engineering students and professionals an example of the process behind holistic development of a semi-autonomous UGV and to begin an inexpensive, readilymodified platform for engineers to build upon.

The (common) law of man over (civilian) vehicles unmanned

Considers (a) what response might the law make to autonomous or semi-autonomous unmanned ground vehicles (UGVs) on public roads; and (b) whether the use of unmanned vehicles, and especially the use of unmanned aerial vehicles (UAVs), will require the laws protecting privacy to be reviewed.

Human–Robot Interaction in the Context of Simulated Route Reconnaissance Missions

The goal of this research was to examine the ways in which human operators interact with simulated semiautonomous unmanned ground vehicles (UGVs), semiautonomous unmanned aerial vehicles (UAVs), and teleoperated UGVs (Teleop). Robotic operators performed parallel route reconnaissance missions with each platform alone and with all three platforms. When given all three platforms, participants failed to detect more targets than when given only the UAV or UGV; they were also less likely to complete their mission in the allotted time. Target detection during missions was the poorest with the Teleop alone, likely because of the demands of remote driving. Spatial ability was found to be a good predictor of target-detection performance. However, slowing sensor feed video frame rate or the imposition of a short response latency (250 ms) between Teleop control and Teleop reaction failed to affect target-detection performance significantly. Nevertheless, these video image manipulations did influence assessment of system usability.