In this paper, a novel and complete navigation system is proposed for mobile ground vehicles in park environment. LiDAR map representation maintenance, dynamic objects detection removal, hierarchal path planning model-free local are developed the system. The formulated three layers. global layer, given point cloud of environment, traverse area detected its skeleton graph extracted to represent ...

This paper presents an experimental study of prospects for using adaptable local search techniques in probabilistic roadmap based motion planning. The classical PRM approach uses a single fast and simple local planner to build a network representation of the configuration space. Advanced PRM planners utilize heuristic sampling techniques and combine multiple local planners. The planner describe...

A tractor-trailer robot consists of a carlike tractor towing a passive trailer. Due to its highly nonholonomic nature, the kinematics of this type of robot are complicated and di cult to compute. We present exact closed-form solutions for the kinematic parameters of a tractortrailer robot and use them to construct an exact and e cient motion planner in the absence of obstacles. This local plann...

We adopt the framework of Younes, Musliner, & Simmons for planning with concurrency in continuous-time stochastic domains. Our contribution is a set of concrete techniques for policy generation, failure analysis, and repair. These techniques have been implemented in TEMPASTIC, a novel temporal probabilistic planner, and we demonstrate the performance of the planner on two variations of a transp...

This paper presents a flexible approach using a utilitybased planner to choose between different extraction methods in a question-answering (QA) system, enabling multiple run-time strategies. We model the QA process as a set of probabilistic actions, and use the planner to select an action sequence maximizing the expected value of information. The planner exploits redundancy and diversity by dy...

In this article, we describe a possibilistic/probabilistic conditional planner called PTLplan. Being inspired by Bacchus and Kabanza’s TLplan, PTLplan is a progressive planner that uses strategic knowledge encoded in a temporal logic to reduce its search space. Actions effects and sensing can be context dependent and uncertain, and the information the planning agent has at each point in time is...

We present a stochastic planner based on Markov Decision Processes (MDPs) that participates to the probablistic planning track of the 2004 International Planning Competition. The planner transforms the PDDL problems into factored MDPs that are then solved with a structured policy iteration algorithm. A probabilistic reachability analysis is performed, approximating the MDP solution over the rea...

We present a planner for the Combat Power Management (CPM) problem. In response to multiple simultaneous or sequential threats, the planner generates a set of local plans, one for each target considered apart, and then merges them by searching the space of global plans. The proposed plan merging solution serves also as an iterative plan repair process that resolves negative interferences (subad...

Probabilistic road-map (PRM) planners have shown great promise in attacking motion planning problems with many degrees of freedom that were previously infeasible. Yet when such a planner fails to find a path, it is not clear that no path exists, or that the planner simply did not sample adequately or intelligently the free part of the configuration space. We propose to attack the motion plannin...

in this paper, a new online robot motion planner is developed for systematically exploring unknown environâ¬ments by intelligent mobile robots in real-time applications. the algorithm takes advantage of sensory data to find an obstacle-free start-to-goal path. it does so by online calculation of the generalized voronoi graph (gvg) of the free space, and utilizing a combination of depth-first an...