Hospitalization and isolation can be a traumatic experience for immunocompromised children, especially because they are separated from their families and friends. Social robots have been proposed as a way to improve the quality of care for children hospitalized in isolation by providing alternative means of social interaction and support. Remote control of such robots in a hospital setting, particularly where safety is a major concern, can be a daunting task for young patients. This paper introduces a risk-based blended control approach for mobile robots in hospital-like indoor spaces. Our approach implements a modified Tangent Bug algorithm called User-Centric Tangent Bug for Blended Control, U-CenTB2, with a user-driven directionality principle, wherein temporary goals are set in alignment with intended user commands rather than with a fixed global goal. Risk evaluation is conducted through a dual-speed strategy, addressing a critical gap in traditional trajectory estimation methods that may not detect incremental movements at slow speeds, potentially causing collisions in blind spots. This preemptive risk assessment prioritizes maintaining desired commands while dynamically avoiding collisions. The performance of the algorithm is evaluated by conducting batch simulations via Monte Carlo method. The results demonstrate the system's efficacy in preventing collisions and adhering to user inputs, thereby offering a significant contribution to teleoperation of assistive mobile robots.

Virtual Environments (VEs) are used extensively for training systems because of their ability to provide interactive experiences. Effective use of such environments requires the ability to generate consistent scenarios, which involve one or more realistic yet repeatable interactions between the trainee and the virtual humans in the training environment. This paper presents a framework that facilitates the development of rich interactive scenarios between one or more virtual humans and a trainee. The framework utilizes a scenario authoring system with runtime elements that ensure the trainee encounters realistic and stochastic, yet repeatable, well-orchestrated and precisely choreographed situations to meet specific training goals. Core elements of the framework include the use of probabilistic state machines for controlling the behavior of virtual elements along with triggers which ensure pre-determined outcomes even when utilizing stochastic behavioral models for the virtual humans.

An approach for visually specifying parallel/distributed software using Petri nets (PNs) extend with transition enabling functions (TEFs) is investigated. The approach is demonstrated to be useful in the specification of decision-making activities that control distributed computing systems. PNs are employed because of their highly visual nature that can give insight into the nature of the controller of such a system and because of their analytical properties. In order to increase the expressive power of PNs, the extension of TEFs is used. The main focus is the specification and analysis of parallel/distributed software and systems. A key element of this approach is a set of rules derived to automatically transform such an extended net into a basic PN. Once the rules have been applied to transform the specification, analytical methods can be used to investigate characteristic properties of the system and validate correct operation.>

A novel approach for visually specifying parallel/distributed software using extended Petri-nets (PNS) is investigated. The approach is demonstrated to be useful in the specification of decision-making activities that control distributed computing systems. PNs are employed because of their highly visual nature that can give insight into the nature of the controller of such a system and because of their well-known analytical properties. In order to increase the expressive power of PNs, the extension of transition enabling functions is used. The main focus is a set of automatic transformations derived to transform such an extended net into a basic PN. Then, well-known analytical methods can be used to investigate characteristic properties of the system and validate correct operation. A graphical programming tool (XPAT) has been built to automate the visual programming process as well as the transformations.>