Supervised training of neural networks is time consuming, and the scenarios required for obtaining a representative dataset must be carefully considered for each task. Applying an unsupervised training approach can greatly simplify this data collection aspect. This paper explores options for the unsupervised training of a convolutional neural network for the navigation of a mobile robot and compares its benefits with respect to a supervised training approach. A simulated training environment was created, in which the robot, through random motion, gathered the required data needed for training. Two approaches to training were investigated: either selectively choosing the training data from the random set acquired or considering modifying the network output to favor improved navigation. Both methods proved successful at obtaining an optimum value of 80% efficiency of directional travel whilst maintaining a collision avoidance performance of 97.7%. The results proved our approach was comparable in performance with respect to supervised training approaches, whilst it demonstrated superiority in terms of training-data collection.
Path planning in cluttered environments constitutes a critical challenge for mobile robotics. Although optimal solutions can be obtained by classical methods such as A*, they have the disadvantage of being computationally expensive in complex environments. In this paper, we propo…
In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carri…
Recent advancements in artificial intelligence have enabled reinforcement learning (RL) agents to exceed human-level performance in various gaming tasks. However, despite the state-of-the-art performance demonstrated by model-free RL algorithms, they suffer from high sample compl…
Autonomous legged navigation in unstructured environments is still an open problem which requires the ability of an intelligent agent to detect and react to potential obstacles found in its area. These obstacles may range from vehicles, pedestrians, or immovable objects in a stru…
In brain–machine interface (BMI) systems, the performance of trained Convolutional Neural Networks (CNNs) is significantly influenced by the quality of the training data. Another issue is the training time of CNNs. This paper introduces a novel approach by combining transfer lear…
Controlling a laparoscopic camera during robotic surgery represents a multifaceted challenge, demanding considerable physical and cognitive exertion from operators. While manual control presents the advantage of enabling optimal viewing angles, it is offset by its taxing nature.…