Abstract
For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features’ positions and the robot’s posture. Simulation results demonstrate the validity and accuracy of the proposed method.
Highlights
Different fields like factories, hospitals and houses require mobile robots to navigate autonomously and to perform tasks by themselves
In our previous research [9], we have proposed a double guarantee kidnapping detection (DGKD) in Simultaneous localization and mapping (SLAM)
Probabilistic double guarantee kidnapping detection In this paper, we assume that the robot works in
Summary
Hospitals and houses require mobile robots to navigate autonomously and to perform tasks by themselves. (2016) 3:20 kidnapping, the mobile robot should rebuild information to locate itself In this case, the efficiency of SLAM would be significantly reduced, especially in a large-scale environment. Comparing with the other studies, two new processes are added to execute the evaluation while SLAM is working in DGKD procedure They have the double guarantees to judge whether kidnapping has happened and which type of kidnapping it is. When the mobile robot is working in the large-scale environment without loop closure, the Observe
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