A multivalued logic approach to integrating planning and control
Artificial Intelligence - Special volume on planning and scheduling
Context-mediated behavior for intelligent agents
International Journal of Human-Computer Studies - Special issue: using context in applications
Robot Motion Planning
Integrated Plan-Based Control of Autonomous Robots in Human Environments
IEEE Intelligent Systems
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Adaptive execution in complex dynamic worlds
Adaptive execution in complex dynamic worlds
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Clarification dialogues in human-augmented mapping
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Planning Algorithms
Multi-objective exploration and search for autonomous rescue robots: Research Articles
Journal of Field Robotics
Journal of Field Robotics - Special Issue on Teamwork in Field Robotics
25 years of applications of logic programming in Italy
A 25-year perspective on logic programming
Intelligent supervision for robust plan execution
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Design and implementation of architecture for multi-robot cooperation in the context of WSN
Proceedings of the 10th ACM symposium on Performance evaluation of wireless ad hoc, sensor, & ubiquitous networks
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The need for improving the robustness, as well as the ability to adapt to different operational conditions, is a key requirement for a wider deployment of robots in many application domains. In this paper, we present an approach to the design of robotic systems, that is based on the explicit representation of knowledge about context. The goal of the approach is to improve the system's performance, by dynamically tailoring the functionalities of the robot to the specific features of the situation at hand. While the idea of using contextual knowledge is not new, the proposed approach generalizes previous work, and its advantages are discussed through a case study including several experiments. In particular, we identify many attempts to use contextual knowledge in several basic functionalities of a mobile robot such as: behavior, navigation, exploration, localization, mapping and perception. We then show how re-designing our mobile platform with a common representation of contextual knowledge, leads to interesting improvements in many of the above mentioned components, thus achieving greater flexibility and robustness in the face of different situations. Moreover, a clear separation of contextual knowledge leads to a design methodology, which supports the design of small specialized system components instead of complex self-contained subsystems.