Path Planning and Control of Mobile Robot in Road Environments Using Sensor Fusion and Active Force Control

Ali, Mohammed A. H.; Mailah, Musa

doi:10.1109/tvt.2019.2893878

Cited by 48 publications

(45 citation statements)

References 38 publications

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“…Ali et al [261] developed an approach for a three-wheeled mobile robot in an online navigation of road following and roundabout environments. They developed a complete planner in which the sensor fusion was used to remove noise and uncertainties from the sensors.…”

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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“…This paper focuses on the road roundabout environments navigation, since it needs a further development and consideration as stated in Section 1 ; however, the other types of junctions such as T/Y and cross intersections have been already studied well in the literature [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Related Workmentioning

confidence: 99%

“…Several works for navigation on the road intersections (e.g., T and Y branches and cross areas) and road following, have been reported [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] through building a suitable local map of terrains using sensors fusion technique. In fact, a handful of the above-mentioned reported works have dealt with a roundabout environment where the surroundings and rules of the road are significantly different from other kinds of junctions or intersections.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Road Roundabout Detection and Navigation System for Smart Vehicles and Cities Using Laser Simulator–Fuzzy Logic Algorithms and Sensor Fusion

Ali

Mailah

Jabbar

et al. 2020

Sensors

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A real-time roundabout detection and navigation system for smart vehicles and cities using laser simulator–fuzzy logic algorithms and sensor fusion in a road environment is presented in this paper. A wheeled mobile robot (WMR) is supposed to navigate autonomously on the road in real-time and reach a predefined goal while discovering and detecting the road roundabout. A complete modeling and path planning of the road’s roundabout intersection was derived to enable the WMR to navigate autonomously in indoor and outdoor terrains. A new algorithm, called Laser Simulator, has been introduced to detect various entities in a road roundabout setting, which is later integrated with fuzzy logic algorithm for making the right decision about the existence of the roundabout. The sensor fusion process involving the use of a Wi-Fi camera, laser range finder, and odometry was implemented to generate the robot’s path planning and localization within the road environment. The local maps were built using the extracted data from the camera and laser range finder to estimate the road parameters such as road width, side curbs, and roundabout center, all in two-dimensional space. The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time.

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“…Path planning technology of mobile robot is one of the core contents of intelligent mobile robot research [1]. Path planning of mobile robot refers to that activity where the robot can perceive the environment and independently plan a route to reach the target based on the information obtained by the sensor.…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Path Planning of Wheeled Robot Based on Deep Reinforcement Learning on the Slope Ground

Wang

Song

et al. 2020

Journal of Robotics

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The existing dynamic path planning algorithm cannot properly solve the problem of the path planning of wheeled robot on the slope ground with dynamic moving obstacles. To solve the problem of slow convergence rate in the training phase of DDQN, the dynamic path planning algorithm based on Tree-Double Deep Q Network (TDDQN) is proposed. The algorithm discards detected incomplete and over-detected paths by optimizing the tree structure, and combines the DDQN method with the tree structure method. Firstly, DDQN algorithm is used to select the best action in the current state after performing fewer actions, so as to obtain the candidate path that meets the conditions. And then, according to the obtained state, the above process is repeatedly executed to form multiple paths of the tree structure. Finally, the non-maximum suppression method is used to select the best path from the plurality of eligible candidate paths. ROS simulation and experiment verify that the wheeled robot can reach the target effectively on the slope ground with moving obstacles. The results show that compared with DDQN algorithm, TDDQN has the advantages of fast convergence and low loss function.

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Path Planning and Control of Mobile Robot in Road Environments Using Sensor Fusion and Active Force Control

Cited by 48 publications

References 38 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

Autonomous Road Roundabout Detection and Navigation System for Smart Vehicles and Cities Using Laser Simulator–Fuzzy Logic Algorithms and Sensor Fusion

Research on Dynamic Path Planning of Wheeled Robot Based on Deep Reinforcement Learning on the Slope Ground

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