Lab Report
1 — Abstract:
In this task I combined my previous learning of the basics of simulator and the basic scripting with sensors (Proximity) for making the robot do a random wandering and wall following.
2 — Introduction:
Robotic mapping has been a highly active research area in robotics and AI for at least two decades. Robotic mapping addresses the problem of acquiring spatial models of physical environments through mobile robots. The mapping problem is generally regarded as one of the most important problems in the pursuit of building truly autonomous mobile robots. In this task I have used CoppeliaSim simulator with python scripting to control a robot on a map was designed in CoppeliaSim by making a perception with the sensor then making a decision depending on the results to randomly wandering the map then following the wall whenever the robot finds it.
3 — Problem statement:
In CoppeliaSim simulator map, the robot can’t find the wall unless it is near to it and in the simulator the robot can’t be taught how far to travel or how much to turn to find the wall.
4 — Analysis result:
In this lab I faced two problems,
Wandering.Wall following.For the first problem wandering: From plotting the data of the sensors I found that the max range of proximity sensor is 1 m so I solved this by making a random wandering by travel a random distance using random library then turn and travel a straight distance again during the travel I keep read the 3 sensor, whenever I got a valid value from any sensor that indicate a detection of the wall I go to task2 the wall following.
For the second problem wall following: I followed the model based control
By getting the data from the sensor (Perception) then model these data to give a number indicate about the real distance between the sensor and the wall (Modelling) to input this value to the planning and task execution model
IF:
Else if
Else:
5 — Conclusion:
Robotics mapping could be achieved using the model based control, using the perception at the beginning and getting a signal which we can transform to a reading using modelling which give a glimpse about the surroundings and inputting this value to the planning and task execution model which consider the robot mind which the robot act based on the output of it.
6 — Reference: