Environment Mapping Using Multiple Robot

Lee, Pei Kee (2017) Environment Mapping Using Multiple Robot. Project Report. UTeM, Melaka. (Submitted)

Text Environment Mapping Using Multiple Robot - Lee Pei Kee - 24 pages.pdf - Submitted Version Download (225kB)

Abstract

Navigation is needed for any tasks that are related to environment mapping. Navigation can be defined as the combination of three fundamental abilities which are self-localization, path planning and map-building. Localization, mapping, obstacle avoidance, path planning, and motion control are the basic capacities of the mobile robot. Simultaneous localization and mapping (SLAM) is one of the techniques for navigation to enable the robot to move autonomously and observes its surrounding in an unknown surrounding. Nowadays, the rescue robots are able to achieve many tasks, however they are mainly depending on the remote control and lack of the ability to explore the site. It is clearly demonstrated the need of rescue robots to respond to the collapse environment that cause by disaster. Rescue environment is considered a typical unstructured environment which no information about the surrounding. The objective of this study is to implement SLAM algorithm for environment mapping by using multiple mobile robot. The performance of the system will be compared in term of time taken and accuracy with single robot and multiple robot by using Extend Kalman Filter based SLAM. The experiments are conducted by using Pioneer P3dx robot that equipped with ultrasonic sensors and laser ranger with fusion strategy. The mobile robot will observe the surrounding environment by using ultrasonic sensors and laser range finder to detect the obstacles. Extend Kalman filter for robot localization is predicting the new state and correcting with the new observation measurement. The result of the experiment shows that multiple robot able to complete the mapping process faster than a single robot which improved by 17.6%. Besides, the accuracy of all estimated landmarks is improved after increase the number of the mobile robot. The accuracy of the estimated landmark is improved by 14.16%.

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