SLAM Algorithm and Robotics Assistance

The combo of assistance technology and robotic tools can help determine the area of applications. Apart from this, it offers a lot of advantages for the elderly. The idea is to help older people perform their routine tasks. Some of the good examples of the application of this technology include motorized wheelchair navigation and autonomous vehicles. In this article, we are going to find out how SLAM algorithms can be used in robotics for easy navigation in an unfamiliar environment. Read on to find out more.

Implementation

The implementation of simultaneous localization and mapping is performed to facilitate environmental learning. This is done through the help of a mobile robot, but the navigation is done through electromyography signals.

In this case, part of the system is dependent on user decisions. In other words, the Muscle Computer Interface, aka MCI, is responsible for mobile robot navigation.

Let’s know take a look at some common methods used in this system. We will also learn about results of these methods.

Methods of SLAM Algorithm

A SLAM algorithm based on a sequential Extended Kalman Filter (EKF) is a common method. The features of the system correspond to the corners and lines of the environment. A universal metric map is obtained out of the architecture.

Besides, the electromyographic signals that control the movements of the robot can be adapted to the disabilities of the patient. For mobile robot navigation, MCI provides 5 commands: Exit, start, stop, turn to the left and turn to the right.

For controlling the mobile robot, a kinematic controller is implemented. Besides, an effective behavior strategy is used to prevent collision with the moving agents and the environment.

The beauty of these methods is that they can be used in order to enjoy great results and prevent possible complications in the process. New research studies are being conducted to find out how these methods can be used in order to get even better results.

Results OF Systems

The system is tested with the help of volunteers. The experiments can be performed in a low dynamic environment that is closed. The volunteers can be given around half an hour to navigate the environment and get a better understanding of how to tap into the power of MCI.

According to previous experiments, the SLAM resulted in an environment that was consistently reconstructed. At the end of the experiment, a map was obtained and was saved in the muscle computer interface. So, the process is quite efficient and can be used to enjoy great results.

Conclusions

Long story short, the integration of slam with MCI has been quite successful so far. Apart from this, the communication between the two has been quite consistent and successful. The metric map created by the robot can facilitate autonomous navigation down the road without any user interference. Just like a motorized wheelchair, the mobile robot features a similar kinematic model. Therefore, this is a great advantage that can allow wheelchair autonomous navigation.