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DKP (Deterministic Kinodynamic Planning) is a bottom-up trajectory planner for robots with flatnessproperties. DKP builds an exploration tree of which the branches are spline trajectories. DKP employs an A * -like algorithm to select which branch of the tree to grow. The selected trajectories are then grown in a propagation process which respects the kinematic constraints, such as linear/angular speed limits or obstacle avoidance. In addition, DKP produces trajectories that are immediately executable by the robot. Various experiments are provided to show the ability of DKP to effectively handle complex environments with one or more robots. I. INTRODUCTIONComputing a trajectory that takes into account both kinematic and dynamic constraints is known as kinodynamic planning [5], and is proven to be PSPACE-hard [22].Decoupled approaches separate kinodynamic planning in two successive problems; first, compute a path taking into account a part of the problem constraints (classically obstacles) and, then, smooth this path with the remaining constraints to make the solution admissible by the robot. The efficiency of decoupled approaches, such as variants of Elastic Bands [23], is explained by the fact that they are generally customized for specific kinodynamic problems [15]. They also provide bounds on the computation time, allowing on-line planning, which explains their wide usage. However, decoupled approaches present difficulties to solve complicated problems, with many degrees of freedom. Moreover, they suffer from incompleteness issues: since the initial path is not guaranteed to be feasible by the robot, the path smoothing phase may fail to satisfy all kinodynamic constraints or fail to find a solution even if one exists.To solve these difficulties [18], we can distinguish two categories of hybrid approaches which incorporate a local motion planner (selection process) within a global path planner (propagation process) to ensure the respect of constraints. The first category contains heavily customized approaches: both local and global motion planner are then designed to generate complex local maneuvers [11] and/or improve the quality of the global path to be tracked. They successfully deal with very specific problems, such as [6] which integrates perception sensors in the local planner; or the multi resolution approaches like AD * [18].

International audienceAmbient Intelligence (AmI) provides a vision of the information society where heterogeneous hardware entities are disseminated in the environment and used by intelligent agents to provide ubiquitous applications. To ease the integration of new entities in the system, the application and the underlying hardware infrastructure have to be decorrelated. The aim of our research work is to propose mechanisms for the deployment, automatic configuration and monitoring of applications on an heterogeneous hardware infrastructure. In this paper, we model ambient systems to fulfill this purpose. We propose a graph-based mathematical model for ambient systems. This model allows to use a projection algorithm, extending an existing graph matching algorithm, for the deployment and the automatic configuration of applications on an heterogeneous hardware infrastructure

Time-aware agents are agents capable of reasoning about their tasks duration and deadlines, and, more generally, to manage the temporal aspects of the execution of their tasks. We first focus on the case of agents in charge of long duration computations, sustaining that it is not acceptable for an autonomous agent to remain unaware of its environment for too long. We then consider deadline meetings when several time-aware agents share the same CPU. To achieve these goals, we recognize the importance of the artifact concept [16]. We introduce computational artifacts for long duration tasks and a coordination artifact for managing the CPU agenda and acting as an intermediary when agents negotiate CPU power. Control of computational artifacts is done thanks to a set of operating instructions dynamically computed by the coordination artifact.

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