Abstract
As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.
Highlights
Mobile robots are capable of moving around in their environment and carrying out intelligent activities autonomously, having extensive realistic applications, including rescue works
We evaluated the results of some commonly used simultaneous localization and mapping (SLAM) algorithms in both simulation and real-world environment, tested the path planning algorithms (A∗ algorithm and dynamic window algorithm (DWA) algorithm), and conducted a combined experiment of mapping and path planning regarding the RoboCup competition. ese experiments revealed the demerits of some algorithms and provided a benchmark for subsequent algorithm improvement
Path planning, depth camera, and Lidar data are processed by the industrial computer. e industrial computer and STM32 are connected via USB cable to exchange data and instructions. e depth camera is calibrated by using a printed black and white checkerboard. e OpenCV function called by the robot operating system (ROS) is used to extract the corner information from camera images, and internal and external parameters are obtained through calculations [6]. e industrial computer is fitted with Intel Core i5 processor, 4G memory, 128G access space, and the ubuntu16.04 system
Summary
Mobile robots are capable of moving around in their environment and carrying out intelligent activities autonomously, having extensive realistic applications, including rescue works. Yu et al [5] applied an improved A∗ path planning algorithm to unmanned underwater survey ships, enabling quick obstacle avoidance and return to the preset route. These studies did not take into account the impact of the rescue environment on the SLAM algorithm. Path planning, depth camera, and Lidar data are processed by the industrial computer. Path planning, depth camera, and Lidar data are processed by the industrial computer. e industrial computer and STM32 are connected via USB cable to exchange data and instructions. e depth camera is calibrated by using a printed black and white checkerboard. e OpenCV function called by the robot operating system (ROS) is used to extract the corner information from camera images, and internal and external parameters are obtained through calculations [6]. e industrial computer is fitted with Intel Core i5 processor, 4G memory, 128G access space, and the ubuntu16.04 system
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