It has been presented an analysis, design and implementation of a system in which a mobile robot with basic behavior. The mobile robot system solves a maze and finds a predefined target which is located at the exit of the labyrinth. We also combine camera calibration, computer vision and path planning to construct this visual servo system for the mobile robot.
During the execution phase, we first used a camera located at the ceiling of our testing environment to detect the target position. Next, we collected laser range data, filtered the noise, analyzed the data obtained, and detected the maze. Finally, we used a fuzzy logic based control for our mobile robot, Amigo. The experiment results were really positive and very near our expectations for indoor environments. Our mobile robot was able to reach the targets, follow the maze shape, and avoid obstacles in an optimal way. So far, smoothness of the path, speed and dead-end cases will be considered in our future work.
For the trajectory tracking, the Motlagh’s approach has been enhanced and verified in this research for trajectory tracking. Even though there are small discrepancy between their simulated path and our real path, especially the robot trajectory around the corners, but our navigation system works well under the improvements of obstacle position sensing and target switching strategy. The empirical results demonstrate that the effect due to uncertainty in sensor reading is reduced significantly and the real-time obstacle avoidance capability is enhanced upon applying the modified algorithm in this paper.
The strategy of using virtual target has been improved to enable P3DX to move out of the dead zone (U-shaped and subspace) by following the wall, and turn back to original target as needed. In all situations, the FLC enables the robot to avoid the obstacles and guide the robot to the target while the target is instantaneously changing based on the target switching strategy. The effect of FLC and target switching strategy complement each other.
Not only P3DX, but any robot with sonar sensor and laser range finder could accomplish obstacle avoidance and target seeking using the approach proposed in this paper. In our experiments, the biggest error comes from the heading angle from the reading of the camera system on the ceiling. Therefore, the IMU (Inertial Measurement Unit) is suggested to be applied to get more accurate heading angle for future work.
For the visual servoing system on mobile robots, much of the instability of the vision perception algorithm comes from the environment variation such as illumination variation, shadow, and incident sunlight to the ground. The optimal dilation size of the obstacle is critical to the path-planning. The effect of over dilated obstacle is losing some feasible paths. While with under dilated obstacle, the result path might not be safe for the robot to pass.
We applies global mapping to implement path planning, the experiment shows that the robot
has the ability to plan a path and follow this trajectory. However, blind spots might exist in the map, for example targets hidden under other objects. Local mapping could be taken into consideration to further improve this issue. A dynamic system can be developed if we can reduce the complexity of path planning so that the increasing of obstacle numbers would not impact run-time performance.
The results of this study indicate that the forward and backward approach have significantly different trajectory character under same nominal control parameters and kinematics model. The experimental results demonstrated an optimal path of the wheeled mobile robot through the feedback control law to the goal with an average 10 cm error. However, the optimal parameter is obtained through experimental tries. The automatic parameter optimization algorithms should be implemented such as genetic algorithm. The real time path planning is also needed to obtain the dynamic parameters of k, k and k if there is the presence of obstacle. The real-time trajectory error estimation and velocity compensation can be further implemented to improve the system such as Kalman or Bastian filter. Lots of applications that need smooth movement towards the target, such as automatic parking system, search and rescue robot etc, can be further investigated based on our present approaches
Future work should be aimed at incorporating higher level scene knowledge to enable obstacle avoidance and other behavior characterization, as well as connecting multiple robots to enable autonomous navigation between arbitrary points.
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