Agile application-aware adaptation for mobility
Proceedings of the sixteenth ACM symposium on Operating systems principles
Artificial intelligence and mobile robots
Energy-aware adaptation for mobile applications
Proceedings of the seventeenth ACM symposium on Operating systems principles
Project Aura: Toward Distraction-Free Pervasive Computing
IEEE Pervasive Computing
A Middleware Infrastructure for Active Spaces
IEEE Pervasive Computing
Practical RDF
Mobile adaptive tasks guided by resource contracts
MPAC '04 Proceedings of the 2nd workshop on Middleware for pervasive and ad-hoc computing
Iterative Adaptation for Mobile Clients Using Existing APIs
IEEE Transactions on Parallel and Distributed Systems
SHAGE: a framework for self-managed robot software
Proceedings of the 2006 international workshop on Self-adaptation and self-managing systems
Task-driven automated component deployment for ambient intelligence environments
Pervasive and Mobile Computing
Puppeteer: Component-based adaptation for mobile computing
USITS'01 Proceedings of the 3rd conference on USENIX Symposium on Internet Technologies and Systems - Volume 3
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Intelligent service robots provide various services to users by understanding the context and goals of a user task. In order to provide more reliable services, intelligent service robots need to consider various factors, such as their surrounding environments, users' changing needs, and constrained resources. To handle these factors, most of the intelligent service robots are controlled by a task-based control system, which generates a task plan that represents a sequence of actions, and executes those actions by invoking the corresponding functions. However, the traditional task-based control systems lack the consideration of resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). Moreover, system-specific concerns such as the relationships among functional modules are not considered during the task generation phase. Without considering both the resource conditions and interdependencies among software modules as a whole, it will be difficult to efficiently manage the functionalities that are essential to provide core services to users. In this paper, we propose a mechanism for intelligent service robots to efficiently use their resources on-demand by separating system-specific information from task generation. We have defined a sub-architecture that corresponds to each action of a task plan, and provides a way of using the limited resources by minimizing redundant software components and maintaining essential components for the current action. To support the optimization of resource consumption, we have developed a two-phase optimization process, which is composed of the topological and temporal optimization steps. We have conducted an experiment with these mechanisms for an infotainment robot, and simulated the optimization process. Results show that our approach contributed to increase the utilization rate by 20% of the robot resources. Copyright © 2011 John Wiley & Sons, Ltd.