64 research outputs found
Bang-Bang Boosting of RRTs
This paper explores the use of time-optimal controls to improve the
performance of sampling-based kinodynamic planners. A computationally efficient
steering method is introduced that produces time-optimal trajectories between
any states for a vector of double integrators. This method is applied in three
ways: 1) to generate RRT edges that quickly solve the two-point boundary-value
problems, 2) to produce an RRT (quasi)metric for more accurate Voronoi bias,
and 3) to time-optimize a given collision-free trajectory. Experiments are
performed for state spaces with up to 2000 dimensions, resulting in improved
computed trajectories and orders of magnitude computation time improvements
over using ordinary metrics and constant controls
Sensor based planning and nonsmooth analysis
This paper describes some initial steps towards sensor based path planning in an unknown static environment. The method is a based on a sensor-based incremental construction of a one-dimensional retract of the free space. In this paper we introduce a retract termed the generalized Voronoi graph, and also analyze the roadmap of Canny and Lin's opportunistic path planner (1990, 1993). The bulk of this paper is devoted to the application of nonsmooth analysis to the Euclidean distance function. We show that the distance function is in fact nonsmooth at the points which are required to construct the plan. This analysis leads directly to the incorporation of simple and realistic sensor models into the planning scheme
Sensor based planning and nonsmooth analysis
This paper describes some initial steps towards sensor based path planning in an unknown static environment. The method is a based on a sensor-based incremental construction of a one-dimensional retract of the free space. In this paper we introduce a retract termed the generalized Voronoi graph, and also analyze the roadmap of Canny and Lin's opportunistic path planner (1990, 1993). The bulk of this paper is devoted to the application of nonsmooth analysis to the Euclidean distance function. We show that the distance function is in fact nonsmooth at the points which are required to construct the plan. This analysis leads directly to the incorporation of simple and realistic sensor models into the planning scheme
ADAPTIVE PROBABILISTIC ROADMAP CONSTRUCTION WITH MULTI-HEURISTIC LOCAL PLANNING
The motion planning problem means the computation of a collision-free motion for a movable object among obstacles from the given initial placement to the given end placement. Efficient motion planning methods have many applications in many fields, such as robotics, computer aided design, and pharmacology. The problem is known to be PSPACE-hard. Because of the computational complexity, practical applications often use heuristic or incomplete algorithms. Probabilistic roadmap is a probabilistically complete motion planning method that has been an object of intensive study over the past years. The method is known to be susceptible to the problem of “narrow passages”: Finding a motion that passes a narrow, winding tunnel can be very expensive. This thesis presents a probabilistic roadmap method that addresses the narrow passage problem with a local planner based on heuristic search. The algorithm is suitable for planning motions for rigid bodies and articulated robots including multirobot systems with many degrees-of-freedom. Variants of the algorithm are describe
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