Artificial Life
Digital pheromone mechanisms for coordination of unmanned vehicles
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Heuristic shortest path algorithms for transportation applications: state of the art
Computers and Operations Research
A stigmergic approach for dynamic routing of active products in FMS
Computers in Industry
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
In-field and inter-field path planning for agricultural transport units
Computers and Industrial Engineering
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Over the last decade, authorities have begun inquiring about the use of safe, comfortable, ecological vehicles for operation in an urban context as an alternative to private cars. Several on-demand transport projects have emerged with new automated vehicles known as cybercars or Personal Rapid Transit (PRT). Our state-of-the-art survey of the literature about automated On-Demand Transport (ODT) control solutions highlighted the desirability of a decentralized approach, although centralized approaches do have some advantages. In order to benefit from the advantages of both centralized/hierarchical and decentralized/heterarchical control approaches, we propose a new concept of control: open-control. In this paper, the context is intelligent transportation, where vehicles (e.g., PRTs) can be seen as autonomous decisional entities that are part of a transport system. In this context, the open-control concept is used to support two solutions to PRT routing with uncertainty and perturbations. This open-control concept, developed in our lab, exhibits the traditional explicit control, as well as an innovative type of control called implicit control, which allows system entities to be influenced via an Optimization Mechanism (OM). After introducing the open-control paradigm, we illustrate two applications of the implicit control of a PRT fleet, one based on a stigmergic method and the second based on an embedded version of the Dijkstra's algorithm. We present a real implementation of the second approach applied to an experimental PRT network. We describe our experimental platform for PRT control and report our first experimental results. These experiments clearly show the reactivity of the control faced with unpredictable events, such as path perturbation or dynamic insertion of PRT in the network.