Approaches for heuristically biasing RRT growth
نویسندگان
چکیده
This paper presents several modifications to the basic rapidly-exploring random tree (RRT) search algorithm. The fundamental idea is to utilize a heuristic quality function to guide the search. Results from a relevant simulation experiment illustrate the benefit and drawbacks of the developed algorithms. The paper concludes with several promising directions for future research.
منابع مشابه
Large-scale path planning for Underwater Gliders in ocean currents
Underwater gliders are a class of AUVs designed for high endurance over long distances, but their reduced velocity makes them more susceptible to ocean currents during deployment. Thus, feasible paths need to be generated through the ocean current field. This paper proposes a method for determining energy-optimal paths that account for the influence of ocean currents. The proposed technique is ...
متن کاملAbstraction-Guided Sampling for Motion Planning
ion-guided Sampling for Motion Planning University of New Hampshire Department of Computer Science Technical Report 12-01 Scott Kiesel, Ethan Burns and Wheeler Ruml May 1, 2012 Abstraction-guided Sampling for Motion Planningion-guided Sampling for Motion Planning Scott Kiesel and Ethan Burns and Wheeler Ruml Abstract Motion planning in continuous space is a fundamental robotics problem that has...
متن کاملMotion Planning for Bounding on Rough Terrain with the LittleDog Robot
In this paper we develop an RRT-based motion planner that achieved bounding in simulation with the LittleDog robot over extremely rough terrain. LittleDog is a quadruped robot that has 12 actuators, and a 36-dimensional state space; the task of bounding involves differential contstraints due to underactuation and motor limits, which makes motion planning extremely challenging. Rapidly-exploring...
متن کاملSample-based motion planning in high-dimensional and differentially-constrained systems
State of the art sample-based path planning algorithms, such as the Rapidly-exploring Random Tree (RRT), have proven to be effective in path planning for systems subject to complex kinematic and geometric constraints. The performance of these algorithms, however, degrade as the dimension of the system increases. Furthermore, sample-based planners rely on distance metrics which do not work well ...
متن کاملA Simple Experiment on the Effect of Biasing Sampling Distributions for Planning Reaching Motions with RRTs
This paper reports experiments performed with a simple sampling cache mechanism used to bias the configuration sampling of a RRT-based motion planner. The Planner is used for synthesizing reaching motions for a humanlike character performing object relocation tasks in a virtual environment. Experiments are reported showing that the method can significantly reduce the computational time required...
متن کامل