A New Path Evaluation Method for Path Planning With Localizability

Abstract

As a fundamental capability of mobile robots, path planning highly relies on the accurate localization of the robot. However, limited consideration for the localizability (which describes the capability of acquiring accurate localization) has been made in path planning. This brings a high risk of choosing a path that is optimal but results in the robot easily getting lost. There exist two key challenges to address this problem: 1) How to evaluate the localizability of a path and its impact on path planning. 2) How to balance the localizability of a path and the standard path planning criteria (e.g., shortest travel distance, obstacle-free path, etc.. To overcome the two challenges a new path evaluation method is required. So we first analyzed the uncertainty that comes from dead-reckoning and map matching. Then the localizability was estimated by the fusion of the uncertainty coming from both of them. Based on that, the impact of the localizability on the path planning task has been evaluated by an evaluation function. By combining the localizability evaluation function with traditional criteria (e.g., shortest length, obstacle-free path, etc.), a new path evaluation function for path planning is established. Both simulation and experimental studies show that the new path evaluation function can offer a balance between the localizability and the traditional criteria for path planning.

Analisis Perbandingan Algoritma Breadth First Search (BFS) dan Algoritma A*

“…Tentu dalam melakukan path planning harus memiliki keamanan sehingga tidak menabrak kendaraan lain [6]. Pada proses pencarian jalur harus dapat menghindari setiap obstacle yang ada [7] [8]. Menghindari obstacle akan membuat hasil pembuatan jalur tidak menabrak dengan obstacle.…”

Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot Using a Three-Parameter Error Model

“…There are other kinds of criteria for the path. Gao et al [ 13 ] proposed a localizability evaluation method to find the path with optimal localizability. Zhang et al [ 14 ] proposed criteria for considering the path length and angle and applied the path planning method in the greenhouse.…”

Optimization for medical logistics robot based on model of traveling salesman problems and vehicle routing problems

“…5 To path planning of robots or manipulators, most of the studies focus on efficient approaches to search the shortest length, such as Huang and Savkin used a shortest viable path planning algorithm in the unicycle robot, 6 least time, such as Jin et al considered a path planning by dual motors, the influence of velocity affects the path selected between any two pickings, 7,8 or least energy, such as Norouzi studied an analytical strategy to generate stable paths for manipulator arms of the reconfigurable mobile robot. 9 Besides the localization problem of the robot affects the path selected, such as Gao et al studied localization problem of the robot, and a new evaluation function for path planning has been proposed, 10 memory saving, such as Song et al proposed an improved cuckoo search algorithm based on compact and parallel techniques for three-dimensional path planning problems, 11 path smoothness, such as Nazarahari et al proposed an innovative artificial potential field algorithm and an enhanced genetic algorithm to find a set of feasible initial paths and is guaranteed to find a feasible path improved. 12 But the best scheme from start to end, generally, without other factors is unconsidered in the path such as environment, artificial element, and so on.…”