Efficient Path Planing for Articulated Vehicles in Cluttered Environments

Abstract

Motion planning and control for articulated logistic vehicles such as tugger trains is a challenging problem in service robotics. The case of tugger trains presents particular difficulties due to the kinematic complexity of these multiarticulated vehicles. Sampling-based motion planners offer a motion planning solution that can take into account the kinematics and dynamics of the vehicle. However, their planning times scale poorly for high dimensional systems, such as these articulated vehicles moving in a big map. To improve the efficiency of the sampling-based motion planners, some approaches combine these methods with discrete search techniques. The goal is to direct the sampling phase with heuristics provided by a faster, precociously ran, discrete search planner. However, sometimes these heuristics can mislead the search towards unfeasible solutions, because the discrete search planners do not take into account the kinematic and dynamic restrictions of the vehicle. In this paper we present a solution adapted for articulated logistic vehicles that uses a kinodynamic discrete planning to bias the sampling-based algorithm. The whole system has been applied in two different towing tractors (a tricycle and a quadricycle) with two different trailers (simple trailer and synchronized shaft trailer).

A Novel Collision-Free Homotopy Path Planning for Planar Robotic Arms

“…In the last few decades, several algorithm have been used to determine collision-free paths, and some of the most common are as follows: (a) sampling-based planning algorithms such as rapidly random trees (RRT) [ 16 ], probabilistic roadmap (PRM) [ 1 , 17 ], and the variants of each of them [ 16 ]; (b) graph-based algorithms such as visibility graph [ 18 ] and A* [ 19 ]; (c) heuristic-based algorithms such as ant colony [ 20 ] and genetic-based [ 3 ]; (d) deterministic-based methods, which include artificial potential fields (APFs) [ 21 ] and the homotopy-based path-planning method (HPPM) [ 22 , 23 , 24 ]. These algorithms and methods have been applied in mobile terrestrial robots, UAVs, car-like vehicles, and robotic manipulators [ 1 , 16 , 17 , 20 , 23 , 25 , 26 , 27 , 28 ]. However, these algorithms and methods still have several drawbacks such as falling into local minima, high computational cost, or long times to obtain a solution path, and some of these do not guarantee a solution path.…”

Trajectory Planner CDT-RRT* for Car-Like Mobile Robots toward Narrow and Cluttered Environments

“…A rewiring procedure of RRT* guaranteed asymptotic convergence to optimal solutions with a proper nearest range limit. Various planners based on RRT* were proposed in [ 27 , 28 ]. An RRT*-based planner was proposed for the high-speed maneuvering of autonomous vehicles in [ 23 ].…”