The combination of assistive technology and robotic tools can help determine the application area. Apart from this, it offers many advantages for the elderly. The idea is to help the elderly to carry out their routine tasks. Some good examples of the application of this technology include motorized wheelchair navigation and autonomous vehicles. In this article, we are going to discover how SLAM algorithms can be used in robotics to facilitate navigation in an unfamiliar environment. Keep reading to know more.

Simultaneous mapping and location implementation is carried out to facilitate environmental learning. This is done with the help of a mobile robot, but navigation is done by electromyography signals.

In this case, part of the system depends on the decisions of the user. In other words, the Muscle Computer Interface, also known as MCI, is responsible for the navigation of mobile robots.

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

Methods

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

In addition, the electromyographic signals that control the robot’s movements can be tailored to the patient’s disabilities. For mobile robot navigation, MCI provides 5 commands: Exit, Start, Stop, Turn Left, and Turn Right.

To control the mobile robot, a kinematic controller is implemented. In addition, an effective behavioral strategy is used to avoid collision with moving agents and the environment.

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

Results

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

Based on previous experiments, SLAM resulted in an environment that was constantly rebuilt. At the end of the experiment, a map was obtained and saved to the muscle computer interface. Therefore, the process is quite efficient and can be used to enjoy excellent results.

Conclusions

Simply put, the slam integration 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 road navigation without any user interference. Like a motorized wheelchair, the mobile robot features a similar kinematic pattern. Therefore, this is a great advantage that it can allow autonomous wheelchair navigation.