Design of Ackerman Mobile Robot System Based on ROS and Lidar

Dong, Mingze; Chen, Donghong; Ye, Nan; Chen, Xiai

doi:10.1088/1742-6596/1838/1/012073

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The mainstream research of UMV systems focuses on the combined use of multiple sensors to exploit their complementary advantages and disadvantages. Potdar et al [18] proposed a method to accomplish localization, obstacle detection, and path planning in an Ackerman steering robot [19,20] using a single camera and LiDAR sensor; LiDAR data are transmitted over a wireless network to a computer using the TCP/IP protocol, and obstacle data from the robot are augmented with potential fields and combined with positional data from overhead cameras to construct cost maps for use in navigational algorithms. Four different search algorithms [21][22][23] were implemented for testing.…”

Section: Introductionmentioning

confidence: 99%

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

Shih

Lin

Jhang

2023

Sensors

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With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion.

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Section: Introductionmentioning

confidence: 99%

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

Shih

Lin

Jhang

2023

Sensors

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“…In literature [3], the A* and DWA (dynamic window approach) algorithms are used for path planning, but there are still problems with reliance on odometry and incomplete path planning. In literature [4], the Hector algorithm is used for mapping, and the Dijkstra algorithm is used for path planning. Although the odometer can be eliminated, it puts higher requirements on the laser radar.…”

Section: Introductionmentioning

confidence: 99%

Simulation of autonomous inspection robot application based on ROS

Zang,

Xiao,

Song

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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Simultaneous Localization and Map construction (SLAM) is a hot research topic in mobile robotics and is a crucial technology for developing inspection robots. This paper focuses on mapping the environment by analyzing the Gmapping and Karto Slam algorithms' mapping effects and constructing a simulation environment map using the Karto Slam algorithm. To address the obstacle avoidance problem that cannot be handled by only using the Dijkstra algorithm for global path planning, an improved DWA algorithm is introduced for local path planning to optimize the real-time obstacle avoidance function of the robot. Based on the construction of a robot simulation platform, this paper studies the previous simulation of an autonomous inspection robot before actual experiments. Through simulation experiments of mapping and path planning, this study provides support for the feasibility of robot field experiments, reducing the cycle and difficulty of on-site debugging.

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Path planning of indoor mobile robot based on improved A* algorithm incorporating RRT and JPS

Xiao

Huang

2023

AIP Advances

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Indoor mobile robots are widely used in modern industry. Traditional motion control methods for robots suffer from discontinuous path curvature, low planning efficiency, and insufficient verification of theoretical algorithms. Therefore, a motion control system for an intelligent indoor robot was designed. By optimizing the radar map detecting and positioning, path planning, and chassis motion control, the performance of the system has been improved. First, a map of the warehouse environment is established, and the number of resampling particles interval is set for the Gmapping building process to improve the efficiency of map construction. Second, an improved A* algorithm is proposed, which converts the path solution with obstacles between two points into the path solution without obstacles between multiple points based on the Rapidly expanding Random Trees and Jump Point Search algorithms and further improves the pathfinding speed and efficiency of the A* algorithm by screening the necessary expansion nodes. The Dynamic Window Approach (DWA) algorithm based on the dynamic window is used to smooth the path, and the target velocity is reasonably assigned according to the kinematic model of the robot to ensure the smooth motion of the chassis. By establishing raster map models of different sizes, the traditional and improved A* pathfinding algorithms are compared and validated. The results illustrate that the improved pathfinding algorithm reduces the computing time by 67% and increases the pathfinding speed by 47% compared with the A* algorithm. Compared with the traditional method, the speed and effect are greatly improved, and the motion control system can meet the requirements of autonomous operation of mobile robots in indoor storage.

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Design of Ackerman Mobile Robot System Based on ROS and Lidar

Cited by 4 publications

References 6 publications

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

Simulation of autonomous inspection robot application based on ROS

Path planning of indoor mobile robot based on improved A* algorithm incorporating RRT and JPS

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