Local path planning for mobile robots based on intermediate objectives

Ma, Yingchong; Zheng, Gang; Perruquetti, Wilfrid; Qiu, Zhaopeng

doi:10.1017/s0263574714000186

Cited by 11 publications

(8 citation statements)

References 34 publications

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“…Faktor-faktor penting pada navigasi berbasis peta adalah pemetaan, lokalisasi, path planning, dan trajectory tracking. Pemetaan adalah proses pembentukan representasi lingkungan tempat robot berada, lokalisasi adalah teknik untuk menentukan posisi robot pada lingkungan, path planning adalah proses identifikasi dan pemilihan jalur robot menuju posisi tujuan, dan trajectory tracking adalah teknik agar robot mengikuti rute yang dihasilkan [2].…”

Section: Pendahuluanunclassified

“…Metode scan matching pada Hector Mapping berbasis pada pendekatan Gauss-Newton. Perpindahan posisi robot dihitung dengan Persamaan (1) dan nilai H dihitung dengan Persamaan (2). Variabel ∆ adalah perpindahan posisi robot dalam kordinat Kartesian (x, y, dan θ), r adalah jumlah titik pemindaian LIDAR, Si( ) adalah koordinat-koordinat hasil pemindaian LIDAR berdasarkan posisi robot ξ (x, y, dan θ), dan M (Si(ξ)) adalah probabilitas adanya halangan pada Si(ξ).…”

Section: Hector Mappingunclassified

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Navigasi Indoor Berbasis Peta pada Robot Beroda dengan Platform Robot Operating System

Putra

Muliady

Setiadikarunia

2020

J. Iim. Tek. Elektro

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Wheeled robots are widely used in many industrial fields. The wheeled robot needs to have implemented an autonomous navigation system to improve work efficiency. In this research, a map-based indoor navigation system is implemented on wheeled robot with Robotics Operating System (ROS) platform using Hector Mapping algorithm. The algorithm Multisensor Data Fusion using Extended Kalman Filter (EKF) which fuses Wheel Odometry data with IMU sensor data for localization, Field Dynamic A-Star algorithm for path planning, and ON-OFF controller for trajectory tracking. Field Dynamic A-Star algorithm is chosen because it solves general path planning algorithm's main issue that limits robot's orientation movement for every 45 o (suboptimal and subnatural path). The robot has ODROID-XU4 as controller to perform map-based indoor navigation, Arduino Mega 2560 to drive motors, RPLIDAR A2 LASER rangefinder for mapping, and VEX Integrated Encoder with Sparkfun Razor 9DoF IMU for localization. The navigation system is successfully implemented on wheeled robot with ROS platform. Robot has successfully mapped indoor environment with 0.174 meter error rate, and has successfully done localization with average error rate of 0.05m on x coordinate, 0.028m on y coordinate, and 1.506 o on orientation angle. Path planner is proved capable of generating path that is not limited every 45 o orientation. Path planner yields 62.5% success rate in generating traversable path and the robot yields 75% success rate in following the path. Robot yields average error rate of 0.046m in moving towards target's x coordinate, 0.072m in moving towards target's y coordinate, and 5.163 o in turning towards target's orientation angle.

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Section: Pendahuluanunclassified

Section: Hector Mappingunclassified

Navigasi Indoor Berbasis Peta pada Robot Beroda dengan Platform Robot Operating System

Putra

Muliady

Setiadikarunia

2020

J. Iim. Tek. Elektro

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“…The layering concept, which has been widely used in planning tasks, 6,7 aims to reduce this complexity by dividing the complete path planning task into several simple or small subtasks. Some sparse waypoints 8 or even a direct reference global path 9 is a good alternative to a precise global map. Figure 1 shows the two-layer planning framework, global planner, and local planner employed in this study.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of UGV Based on Bézier Curves

et al. 2019

Robotica

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An effective path planner is critical for autonomous traversal of unmanned ground vehicles (UGVs) in harsh environments. This paper describes a novel path planning method considering Bézier curves and a two-layer planning framework. In the two-layer framework, a road centerline (RCL) estimator located on the upper layer works as a global planner to obtain the local target for the bottom local planner. The RCL is estimated from a series of candidate Bézier curves based on a safety criterion. In the bottom layer, an optimal trajectory planner and a speed planner make up the local planner to obtain the desired steering turning angle and linear speed. The criteria for optimal trajectory selection are designed for comfortable driving. Road safety is considered in the speed planner for robust driving. Three sets of simulations are used to evaluate and quantify the relative performance of variations of our path planning algorithm. The proposed path planning method is implemented on a modified Polaris RZR 800 UGV, too. Two experiments based on this UGV are set up in the country road environment to demonstrate the viability of the proposed method.

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“…In the state of the art, a new potential field method was proposed for motion planning of a mobile robot to overcome the drawbacks in a dynamic environment [1]. The path planning problem was solved by representing the irregular contour of obstacles based on segments in order to avoid them when the robot commences its navigation [2]. In addition, the Dijkstra's algorithm was used to find the shortest path between the source and destination points with creating free collision paths [3].…”

Section: Introductionmentioning

confidence: 99%

Maze Maneuvering and Colored Object Tracking for Differential Drive Mobile Robot

Aldair¹,

Al-Mayyahi²

2019

IJEEE

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In maze maneuvering, it is needed for a mobile robot to feasibly plan the shortest path from its initial posture to the desired destination in a given environment. To achieve that, the mobile robot is combined with multiple distance sensors to assist the navigation while avoiding obstructing obstacles and following the shortest path toward the target. Additionally, a vision sensor is used to detect and track colored objects. A new algorithm is proposed based on different type of utilized sensors to aid the maneuvering of differential drive mobile robot in an unknown environment. In the proposed algorithm, the robot has the ability to traverse surrounding hindrances and seek for a particular object based on its color. Six infrared sensors are used to detect any located obstacles and one color detection sensor is used to locate the colored object. The Mobile Robotics Simulation Toolbox in Matlab is used to test the proposed algorithm. Three different scenarios are studied to prove the efficiency of the proposed algorithm. The simulation results demonstrate that the mobile robot has successfully accomplished the tracking and locating of a colored object without collision with hurdles.

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Local path planning for mobile robots based on intermediate objectives

Cited by 11 publications

References 34 publications

Navigasi Indoor Berbasis Peta pada Robot Beroda dengan Platform Robot Operating System

Navigasi Indoor Berbasis Peta pada Robot Beroda dengan Platform Robot Operating System

Path Planning of UGV Based on Bézier Curves

Maze Maneuvering and Colored Object Tracking for Differential Drive Mobile Robot

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