Consideration of obstacle danger level in path planning using A* and fast-marching optimisation: comparative study

Authors:
P. Melchior;B. Orsoni;O. Lavialle;A. Poty;A. Oustaloup
Affiliations:
LAP-UMR 5131 CNRS, Université Bordeaux 1-ENSEIRB, 351 cours de la Libération, Talence, cedex 33405, France;LAP-UMR 5131 CNRS, Université Bordeaux 1-ENSEIRB, 351 cours de la Libération, Talence, cedex 33405, France;LAP-UMR 5131 CNRS, Université Bordeaux 1-ENSEIRB, 351 cours de la Libération, Talence, cedex 33405, France;LAP-UMR 5131 CNRS, Université Bordeaux 1-ENSEIRB, 351 cours de la Libération, Talence, cedex 33405, France;LAP-UMR 5131 CNRS, Université Bordeaux 1-ENSEIRB, 351 cours de la Libération, Talence, cedex 33405, France
Venue:
Signal Processing - Special issue: Fractional signal processing and applications
Year:
2003

Citing 9
Cited 8

Real-time obstacle avoidance for manipulators and mobile robots

International Journal of Robotics Research
Shortest paths in the plane with convex polygonal obstacles

Information Processing Letters
Obstacle growing in a nonpolygonal world

Information Processing Letters
Robot motion planning: a distributed representation approach

International Journal of Robotics Research
The CRONE path planning

Mathematics and Computers in Simulation - Special issue: Robotics
Fast Marching Methods

SIAM Review
Robot Motion Planning

Robot Motion Planning
Retraction: A new approach to motion-planning

STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Problem-Solving Methods in Artificial Intelligence

Problem-Solving Methods in Artificial Intelligence

Fractional order electromagnetics

Signal Processing - Fractional calculus applications in signals and systems
Exploration of a cluttered environment using Voronoi Transform and Fast Marching

Robotics and Autonomous Systems
Exploration of 2D and 3D Environments using Voronoi Transform and Fast Marching Method

Journal of Intelligent and Robotic Systems
Chaotic bee swarm optimization algorithm for path planning of mobile robots

EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Evolutionary trajectory planner for multiple UAVs in realistic scenarios

IEEE Transactions on Robotics
Heuristically driven front propagation for geodesic paths extraction

VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Swarm-based path creation in dynamic environments for search and rescue

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
The IFC-based path planning for 3D indoor spaces

Advanced Engineering Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Obstacle danger level is taken into consideration in path planning using fractional potential maps. This paper describes the two optimisation methods tested: the A* algorithm and the Fast-Marching technique. The efficiency of the two approaches is illustrated and compared through a vehicle path planning application in a fixed obstacle environment. A* is a heuristically ordered research algorithm and is complete and admissible. Fast-Marching provides a convex map without local minima and permits real-time evaluation of optimal trajectories. A vehicle path planning application is considered in a fixed obstacle environment. A specific danger level is given to each obstacle. The obtained continuous curve trajectories are compared.