# Three-Dimensional Path-Following with Articulated 6 DoF Robot and ToF Sensors
## 5 Conclusions  
Thanks to the result of the presented research, it can be concluded that despite the use of only six low-cost ToF sensors (the price for one sensor breakout module with the sensor and integrated supporting electronics is approximately EUR 11, while one sensor itself costs approximately EUR 2), the low-cost microcontroller and Bluetooth (approximately EUR 15 for the MCU development board and EUR 12 for the Bluetooth module), and standard laptop computer, it was possible to meet the expected goals. This was achieved using simple technical tools and a simple algorithm, without the need for applying a complex method like, for example, AI algorithms, which need long-lasting and high computing power and time-consuming training. The presented solution is easily scalable. An improvement may be sought by increasing the number of sensors, e.g., to 10 instead of 6. This would improve the number of detection points, increase path detection accuracy, and reduce the occurrence of situations in which the algorithm loses the path or sharp bends in the path occur. The other proposal is to use LiDAR sensors (instead of ToF sensors), which should reduce the size of the detection tool and allow the reading of the position of the path to be much more accurate, not only at the intersections points of the detection zones of ToF sensors. The presented algorithm together with its implementation (including hardware elements) has a centimetre-level path-following accuracy. It is limited mostly by the detection tool spatial resolution. At the current stage of elaboration, the main goal of this research was to develop the methodology and test it in practice. Further development, as mentioned above, will be directed towards enhancing the path detection accuracy. When comparing the results to other solutions, it is worth noting that path detection and path-following methods for 2D paths, especially those based on vision systems supported by lasers and AI data processing, achieve even submillimetre-level accuracy. The methods used for 3D navigation and positioning or object and obstacle recognition, used, for example, for UAV or autonomous car control, have various accuracies, starting from metres down to millimetres depending on what is needed for the particular application. The most accurate methods used for object localization in a 3D space are precise structured light scanners. Commercially available scanners may achieve very high, micrometre-level accuracies, but are very expensive and need high computational power for data processing. In this context, the method proposed in this paper may appear inferior, but it is always a trade-off between the desired accuracy and system costs, including hardware and software. Thus, the described solution, even though it cannot match the extremally high accuracies of some of the most advanced solutions, may find applications, especially after detection resolution and accuracy are increased, which is possible within limited costs. Potential applications of the proposed path-following method are tasks performed in the robot's workspace that require moving along a previously unknown but physically defined path, for example, contactless scanning or monitoring of various long, narrow objects. After extending the method to mobile robots, specifically aerial or underwater, the proposed idea could be implemented in the tasks of monitoring power lines, pipelines, ropes, or chains, hanging in the air or placed underwater, especially if exact positioning or minimising the path-following error are not of the greatest importance, or the costs need to be kept to a minimum. After increasing path detection accuracy, this approach could also be applied, for example, in some less accuracy-demanding welding, glueing, or 3D printing jobs. The method could also be used as a supplementary add-on to other methods, for example, those relying on vision systems, especially in adverse environments or conditions such as underwater operations, dark areas, or in severe weather conditions.