AI Summary & Key Takeaways
AI InsightThis distinguishes the proposed system from similar prior studies.
📌 3 Key Takeaways
Abstract
In this paper, a remotely controllable, differentially driven wheeled mobile robot was developed in order to build 2D maps of unknown indoor environments; this system would eliminate the need to pre-explore such environments. Main aim of the study is to develop a system with high accuracy by using minimum number of sensors and a processor with low cost especially for comparatively small indoor areas. The distance traveled was calculated using the wheel odometry method. Obstacles surrounding the robot, the distance traveled, and the robot’s orientation were obtained using an ultrasonic distance sensor, optical encoder, and a 3D orientation sensor (also known as an Attitude and Heading Reference System –AHRS), respectively. In addition, the characteristics of the system hardware components were empirically explored, and the errors resulting from the sensors were evaluated. The non-linearity percentage error arising from the encoder was defined and then compensated for. The hysteresis behavior of the ultrasonic distance sensors was also empirically tested. All of the tasks were conducted by using a low-cost FPGA (Field Programmable Gate Arrays) board. This study used the graphical development platform, National Instruments (NI) LabVIEW, and it’s FPGA Module which is used for programming of embedded systems instead of the text-based HDLs (Hardware Description Languages). This distinguishes the proposed system from similar prior studies.