Abstract
Problem statement: In this study, we presented a novel vision-based learning approach for autonomous robot navigation. Approach: In our method, we converted the captured image in a binary one, which after the partition is used as the input of the neural controller. Results: The neural control system, which maps the visual information to motor commands, is evolved online using real robots. Conclusion/Recommendations: We showed that evolved neural networks performed well in indoor human environments. Furthermore, we compared the performance of neural controllers with an algorithmic vision based control method.
Highlights
The general problem of designing a machine for real time navigation and obstacle avoidance in an arbitrary environment is ongoing
Robot navigation in human environments has been previously investigated from a number of different approaches
In order to evaluate the performance of evolved neural controllers, we developed an algorithmic navigation method where the right and left wheel angular velocities are calculated based on the average grey level of pixels in the left and right visual field
Summary
The general problem of designing a machine for real time navigation and obstacle avoidance in an arbitrary environment is ongoing. Due to the desired tasks and the environment in the advantages of GA-NN approach, Hui and Pratihar which the robot has been designed to operate, vision (2004) studied its performance for solving the was chosen as the primary sensor for navigation. It is navigation problems of a car-like robot through expected that in the environments with sufficient computer simulations. A PC/104 stack running the based navigation schemes, training is given to off-line Linux operating system provides the software interface and the performance of optimal motion planner is tested to record and process all the sensor information in real on a real robot. Tate Rob, a robotic platform developed in our by a joystick: laboratory
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