Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles

Marin-Plaza, Pablo; Hussein, Ahmed; Martín, David; Escalera, Arturo de la

doi:10.1155/2018/6392697

Cited by 130 publications

(74 citation statements)

References 20 publications

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“…Information about the environment will be known a priori and can consist of maps, cells, grid, and so on. A complete path is generated from source to target, before the vehicle starts moving [228]. Some of the global planners are Voronoi [229] by Bhattacharya et al, Silhouette [230] by Canny et al, Dijkstra [231] by Skiena et al, A * by Dechter et al [232], Neural Network based by Yang et al [233], and so on.…”

Section: Path Planningmentioning

confidence: 99%

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Kolar

Benavidez

Jamshidi

2020

Sensors

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This paper focuses on data fusion, which is fundamental to one of the most important modules in any autonomous system: perception. Over the past decade, there has been a surge in the usage of smart/autonomous mobility systems. Such systems can be used in various areas of life like safe mobility for the disabled, senior citizens, and so on and are dependent on accurate sensor information in order to function optimally. This information may be from a single sensor or a suite of sensors with the same or different modalities. We review various types of sensors, their data, and the need for fusion of the data with each other to output the best data for the task at hand, which in this case is autonomous navigation. In order to obtain such accurate data, we need to have optimal technology to read the sensor data, process the data, eliminate or at least reduce the noise and then use the data for the required tasks. We present a survey of the current data processing techniques that implement data fusion using different sensors like LiDAR that use light scan technology, stereo/depth cameras, Red Green Blue monocular (RGB) and Time-of-flight (TOF) cameras that use optical technology and review the efficiency of using fused data from multiple sensors rather than a single sensor in autonomous navigation tasks like mapping, obstacle detection, and avoidance or localization. This survey will provide sensor information to researchers who intend to accomplish the task of motion control of a robot and detail the use of LiDAR and cameras to accomplish robot navigation.

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Section: Path Planningmentioning

confidence: 99%

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Kolar

Benavidez

Jamshidi

2020

Sensors

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“…Thus, this paper focuses more on analyzing the tracking accuracy of the mobile robot with the different local planners available in ROS. The global planner is configured with the default parameters as explained in [4], [6], [14].…”

Section: Global Plannermentioning

confidence: 99%

“…The local planner tracks the global path and performs feedback control considering the actual position and movement of the mobile robot within the environment. There are several types of availalble local planners, including the base local planner [4], [6], [15], elastic band (EBand) [7], [8], and timed elastic band (TEB) [9]. usually used for continuous data streams such as sensor data and robot status.…”

Section: Local Plannermentioning

confidence: 99%

“…Trajectories that fails to meet any of the metrics are discarded. The trajectory with the highest score is selected and is published as the velocity commands for the mobile robot [6]. These steps are repeated until the mobile robot reaches the target position.…”

Section: Ros Navigation Stackmentioning

confidence: 99%

“…ROS provides a navigation package that contains global and local path planning algorithms [4]. The local path planners included in ROS are defined as base [6], elastic band (EBand) [7], [8], and timed elastic band (TEB) [9]. Simultaneous localization and mapping (SLAM) is made easier with ROS [10]- [12].To utilize the navigation package, distance sensors is attached to a mobile robot such as a laser rangefinder (LRF) to detect the obstacles within the environment and measure the distance between the detected obstacle and the robot to perform an avoidance scheme.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of ROS on Embedded System for a Mobile Robot

Kim¹,

Delgado²,

Choi³

2018

JAMRIS

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This paper presents a comparative study of Robot Operating System (ROS) packages for mobile robot navigation on an embedded system. ROS provides various libraries and tools in developing complex robot software. We discuss the process of porting ROS to an open embedded platform, which serves as the main controller for a mobile robot. In the case of driving the robot, ROS provides local path planners such as the base, elastic band, and timed elastic band. The local planners are compared and analyzed in terms of accuracy in tracking the global path conducted on a robot model using Gazebo, 3D simulation tool provided by ROS. We also discussed the difference in performance of deploying ROS packages on a personal computer and on the embedded environment. Experiments were performed by controlling two different mobile robots with results showing that tracking error is highly dependent on the goal tolerance. This study will serve as a promising metric in improving the performance of mobile robots using ROS navigation packages.

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Path planning for multiple agents in an unknown environment using soft actor critic and curriculum learning

Sun

Yan

Qin

2022

Computer Animation & Virtual

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Path planning can guarantee that agents reach their goals without colliding with obstacles and other agents in an optimal way and it is a very important component in the research of crowd simulation. In this article, we propose a novel path planning approach for multiple agents which combines soft actor critic (SAC) algorithm and curriculum learning to solve the problems of single policy, slow convergence of the policy in an unknown environment with sparse rewards. The path planning task is set as lessons from easy to difficult, and the neural network of the SAC algorithm is arranged to learn in sequence, and finally the neural network can be fully competent for the path planning task. We also stack the state information to address the problems caused by limited observation for policy learning, and design a comprehensive reward function to make agents reach their goals successfully and avoid collisions with static obstacles and other agents. The experimental results demonstrate that our approach can plan smooth and natural paths for multiple agents, and furthermore, our model has a certain generalization ability and a better adaptability to the changes in a dynamic environment.

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Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles

Cited by 130 publications

References 20 publications

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Comparative Study of ROS on Embedded System for a Mobile Robot

Path planning for multiple agents in an unknown environment using soft actor critic and curriculum learning

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