Collective Singleton-Based Consistency for Qualitative Constraint Networks

Partial singleton closure under weak composition, or partial singleton (weak) path-consistency for short, is essential for approximating satisfiability of qualitative constraints networks. Briefly put, partial singleton path-consistency ensures that each base relation of each of the constraints of a qualitative constraint network can define a singleton relation in the corresponding partial closure of that network under weak composition, or in its corresponding partially (weak) path-consistent subnetwork for short. In particular, partial singleton path-consistency has been shown to play a crucial role in tackling the minimal labeling problem of a qualitative constraint network, which is the problem of finding the strongest implied constraints of that network. In this paper, we propose a stronger local consistency that couples partial singleton path-consistency with the idea of collectively deleting certain unfeasible base relations by exploiting singleton checks. We then propose an efficient algorithm for enforcing this consistency that, given a qualitative constraint network, performs fewer constraint checks than the respective algorithm for enforcing partial singleton path-consistency in that network. We formally prove certain properties of our new local consistency, and motivate its usefulness through demonstrative examples and a preliminary experimental evaluation with qualitative constraint networks of Interval Algebra

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

Agent and object aware tracking and mapping methods for mobile manipulators

The age of the intelligent machine is upon us. They exist in our factories, our warehouses, our military, our hospitals, on our roads, and on the moon. Most of these things we call robots. When placed in a controlled or known environment such as an automotive factory or a distribution warehouse they perform their given roles with exceptional efficiency, achieving far more than is within reach of a humble human being. Despite the remarkable success of intelligent machines in such domains, they have yet to make a full-hearted deployment into our homes. The missing link between the robots we have now and the robots that are soon to come to our houses is perception. Perception as we mean it here refers to a level of understanding beyond the collection and aggregation of sensory data. Much of the available sensory information is noisy and unreliable, our homes contain many reflective surfaces, repeating textures on large flat surfaces, and many disruptive moving elements, including humans. These environments change over time, with objects frequently moving within and between rooms. This idea of change in an environment is fundamental to robotic applications, as in most cases we expect them to be effectors of such change. We can identify two particular challenges1 that must be solved for robots to make the jump to less structured environments - how to manage noise and disruptive elements in observational data, and how to understand the world as a set of changeable elements (objects) which move over time within a wider environment. In this thesis we look at one possible approach to solving each of these problems. For the first challenge we use proprioception aboard a robot with an articulated arm to handle difficult and unreliable visual data caused both by the robot and the environment. We use sensor data aboard the robot to improve the pose tracking of a visual system when the robot moves rapidly, with high jerk, or when observing a scene with little visual variation. For the second challenge, we build a model of the world on the level of rigid objects, and relocalise them both as they change location between different sequences and as they move. We use semantics, image keypoints, and 3D geometry to register and align objects between sequences, showing how their position has moved between disparate observations.Open Acces