TY - JOUR
AU - Lino, Jorge
AB - PurposeThe purpose of this paper is to characterise the TWIN-RRT* algorithm which solves a motion planning problem in which an agent has multiple possible targets where none of them is compulsory and retrieves feasible, “low cost”, asymptotically optimal and probabilistically complete paths. The TWIN-RRT* algorithm solves path planning problems for both holonomic and non-holonomic robots with or without kinematic constraints in a 2D environment.Design/methodology/approachIt was designed to work equally well with higher degree of freedom agents in different applications. It provides a practical implementation of feasible and fast planning, namely where a closed loop is required. Initial and final configurations are allowed to be exactly the same.FindingsThe TWIN-RRT* algorithm computes an efficient path for a single agent towards multiple targets where none of them is mandatory. It inherits the low computational cost, probabilistic completeness and asymptotical optimality from RRT*.Research limitations/implicationsIt uses efficiency as cost function, which can be adjusted to the requirements of any given application. TWIN-RRT also shows compliance with kinematic constraints.Practical implicationsThe practical application where this work has been used consists of an autonomous mobile robot that picks up golf balls in a driving range. The multiple targets are the golf balls and the optimum path is a requirement to reduce the time and energy to refill as quickly as possible the balls dispensing machine.Originality/valueThe new random sampling algorithm – TWIN-RRT* – is able to generate feasible efficient paths towards multiple targets retrieving closed-loop paths starting and finishing at the same configuration.
TI - Path planning towards non-compulsory multiple targets using TWIN-RRT*
JF - Industrial Robot: An International Journal
DO - 10.1108/IR-02-2016-0069
DA - 2016-06-20
UR - https://www.deepdyve.com/lp/emerald-publishing/path-planning-towards-non-compulsory-multiple-targets-using-twin-rrt-7q5NcTuHXN
SP - 370
EP - 379
VL - 43
IS - 4
DP - DeepDyve
ER -