In this paper, we propose an architecture that uses a tender protocol, the Contract Net Protocol (CNP), to let human operators express their consent about the allocation of goals to Unmanned Aerial Vehicles (UAVs) in a constrained environment. The CNP has several good points: it has an appropriate level of automation, it is simple, it spares the bandwidth. But if bids are evaluated solely on the base of a numerical payoff, the CNP cannot fully convey human preference in complex situations. Thus, we extended and implemented the CNP within our framework, Aerial, to enable a more subjective human feedback. We detail how we build the bids and how we filter them to not flood the user. We also explain how we enable a dependable commitment in a dynamic context when the award time is not accurately foreseen.

In a maritime area supervision context, we seek providing a human operator with dynamic information on the behaviors of the monitored entities. Linking raw measurements, coming from sensors, with the abstract descriptions of those behaviors is a tough challenge. This problem is usually addressed with a two-stepped treatment: filtering the multidimensional, heterogeneous and imprecise measurements into symbolic events and then using efficient plan recognition techniques on those events. This allows, among other things, the possibility of describing high level symbolic plan steps without being overwhelmed by low level sensor specificities. However, the first step is information destructive and generates additional ambiguity in the recognition process. Furthermore, splitting the behavior recognition task leads to unnecessary computations and makes the building of the plan library tougher. Thus, we propose to tackle this problem without dividing the solution into two processes. We present a hierarchical model, inspired by the formal language theory, allowing us to describe behaviors in a continuous way, and build a bridge over the semantic gap between measurements and intents. Thanks to a set of algorithms using this model, we are able, from observations, to deduce the possible future developments of the monitored area while providing the appropriate explanations.

Numerous approaches have been proposed for path planning in dynamic current fields, for a fixed departure time. However, in many applications, the departure time is not necessarily known in advance, but can vary in a time window. In this context, the choice of a good departure time is a critical issue. That is why we introduce in this paper a new approach, called symbolic wavefront expansion, determining both the path and the departure time minimizing the travel time of the vehicle. The key idea of this approach is to propagate and compose functions instead of numerical values, with appropriate operators.

The wavefront expansion is commonly used for path planning tasks and appreciated for its efficiency. However, the existing extensions able to handle currents are subject to incorrectness and incompleteness issues when these currents become strong. That is, they may return physically infeasible paths or no path at all, even if a feasible path exists. This behavior endangers the robot, especially in a dynamic replanning context. That is why we propose a new extension called sliding wavefront expansion. This algorithm, combining an appropriate cost function and continuous optimization techniques, guarantees the existence of a path with an arbitrary precision.

The concept of wavefront expansion was introduced in order to build a collision-free path between two points among obstacles, minimizing a criterion. In this paper, we propose a new concept, called wavefront collision, in order to do the same for all possible paths between n waypoints. It consists in expanding simultaneously one wavefront by waypoint, until they collide. Using this concept, all paths can be computed thanks to one multiple wavefront expansion instead of n - 1, hence dividing the computational effort by about 3.5.