Platform Relative Sensor Abstractions across Mobile Robots using Computer Vision and Sensor Integration

Uniform sensor management and abstraction across different robot platforms is a difficult task due to the sheer diversity of sensing devices. However, because these sensors can be grouped into categories that in essence provide the same information, we can capture their similarities and create abstractions. An example would be distance data measured by an assortment of range sensors, or alternatively extracted from a camera using image processing. This paper describes how using software components it is possible to uniformly construct high-level abstractions of sensor information across various robots in a way to support the portability of common code that uses these abstractions (e.g. obstacle avoidance, wall following). We demonstrate our abstractions on a number of robots using different configurations of range sensors and cameras

Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

Mobile robot visual navigation based on fuzzy logic and optical flow approaches

This paper presents the design of mobile robot visual navigation system in indoor environment based on fuzzy logic controllers (FLC) and optical flow (OF) approach. The proposed control system contains two Takagi–Sugeno fuzzy logic controllers for obstacle avoidance and goal seeking based on video acquisition and image processing algorithm. The first steering controller uses OF values calculated by Horn–Schunck algorithm to detect and estimate the positions of the obstacles. To extract information about the environment, the image is divided into two parts. The second FLC is used to guide the robot to the direction of the final destination. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.Peer ReviewedPostprint (author's final draft

Obstacle Avoidance using Fuzzy Logic Controller on Wheeled Soccer Robot

The purpose of this study is to apply Fuzzy Logic Controller on a wheeled soccer robot to avoid the collision with other robots in the field. The robot equipped by an omnidirectional camera as a vision sensor, a mini-PC for the image processing device, a microcontroller to handle I/O system, and three wheel's omnidirectional mover system. Omni-camera produces four input-values, namely: X coordinate ball position, Y coordinate ball position, distance and angle from obstacle to the point of interest in the camera frame. These inputs processed by a mini-PC and then forward to a microcontroller to calculate the output using Fuzzy Logic Controller. The output variables are the movement rate of the robot in the X, and Y coordinate.  These outputs will be used by the kinematics controller to manage the speed of three Omni-wheels driven by 24 volts DC motors. The experiment shows a good result with the percentage of the success of the robot catching the ball is around 70% and 80% in avoiding the obstacle. In time performance, the soccer robot with Fuzzy Logic Controller is superior by 4.67 seconds compared to the robot without this method

Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0