Conceptual Design Evaluation of Mechatronic Systems

The definition of the conceptual design phase has been expressed in many different phrasings, but all of them lead to the same conclusion. The conceptual design phase is of the highest importance during the design process, due to the fact that many crucial decisions concerning the progress of the design need to be taken with very little to none information and knowledge about the design object. This implies to very high uncertainty about the effects that these decisions will have later on. During the conceptual design of a mechatronic system, the system to be designed is modeled, and several solutions (alternatives) to the design problem are generated and evaluated so that the most fitting one to the design specifications and requirements is chosen. The purpose of this chapter is to mention some of the most widely used methods of system modeling, mainly through hierarchical representations of their subsystems, and also to present a method for the generation and evaluation of the design alternatives

From Pillars to AI Technology-Based Forest Fire Protection Systems

The importance of forest environment in the perspective of the biodiversity as well as from the economic resources which forests enclose, is more than evident. Any threat posed to this critical component of the environment should be identified and attacked through the use of the most efficient available technological means. Early warning and immediate response to a fire event are critical in avoiding great environmental damages. Fire risk assessment, reliable detection and localization of fire as well as motion planning, constitute the most vital ingredients of a fire protection system. In this chapter, we review the evolution of the forest fire protection systems and emphasize on open issues and the improvements that can be achieved using artificial intelligence technology. We start our tour from the pillars which were for a long time period, the only possible method to oversee the forest fires. Then, we will proceed to the exploration of early AI systems and will end-up with nowadays systems that might receive multimodal data from satellites, optical and thermal sensors, smart phones and UAVs and use techniques that cover the spectrum from early signal processing algorithms to latest deep learning-based ones to achieving the ultimate goal

An Architecture for Safe Child–Robot Interactions in Autism Interventions

Autism Spectrum Disorder is a developmental disorder that affects children from a very young age and is characterized by persistent deficits in social, communicational, and behavioral abilities. Since there is no cure for autism, domain experts focus on aiding these children through specific intervention plans that are aimed towards the development of the deficient areas. Using socially assistive robots that interact in a social manner with children in autism interventions, efforts are being made towards alleviating the autistic behavior of children and enhancing their social behavior. However, implementing robots in autism interventions could lead to harmful situations concerning safety. In this paper, an architecture for safe child–robot interactions in autism interventions is proposed. First, a taxonomy of child–robot interactions in autism interventions is presented, explaining its complete framework. Next, the interaction is modelled according to this taxonomy where an interaction case is employed in order for the structure of the interaction to be defined. Based on that, the safety architecture is proposed that will be integrated into the robot’s controller. Focus is placed on detecting possible distracting elements that could influence the performance of the child, affecting their psychological or physical safety. Lastly, the interaction between child and robot is created in a simulated environment through dialogue inputs and outputs, and the code of the architecture is tested, where a virtual robot performs the appropriate actions

Experimental Verification of Optimized Anatomies on a Serial Metamorphic Manipulator

The inherit complexity of the determination of the optimal anatomy and structure to task requirements and specification for metamorphic manipulators poses a significant challenge to the end user, as such methods and tools to undertake such processes are required for the implementation of metamorphic robots to real-life applications in various fields. In this work, the methodology for an offline process for the determination of the optimal anatomy maximizing performance under different requirements is presented. Such requirements considered in this work include the kinematic, kinetostatic and dynamic performance of the manipulator during task execution. The proposed methodology is then applied to a 3 D.o.F. metamorphic manipulator for different tasks. The presented results clearly show that a single metamorphic structure is able to provide the end user with different anatomies, each better suited to task specifications

A Machine Learning Approach for Global Steering Control Moment Gyroscope Clusters

This paper addresses the problem of singularity avoidance for a 4-Control Moment Gyroscope (CMG) pyramid cluster, as used for the attitude control of a satellite using machine learning (ML) techniques. A data-set, generated using a heuristic algorithm, relates the initial gimbal configuration and the desired maneuver—inputs—to a number of null space motions the gimbals have to execute—output. Two ML techniques—Deep Neural Network (DNN) and Random Forest Classifier (RFC)—are utilized to predict the required null motion for trajectories that are not included in the training set. The principal advantage of this approach is the exploitation of global information gathered from the whole maneuver compared to conventional steering laws that consider only some local information, near the current gimbal configuration for optimization and are prone to local extrema. The data-set generation and the predictions of the ML systems can be made offline, so no further calculations are needed on board, providing the possibility to inspect the way the system responds to any commanded maneuver before its execution. The RFC technique demonstrates enhanced accuracy for the test data compared to the DNN, validating that it is possible to correctly predict the null motion even for maneuvers that are not included in the training data

A Machine Learning Approach for Global Steering Control Moment Gyroscope Clusters

This paper addresses the problem of singularity avoidance for a 4-Control Moment Gyroscope (CMG) pyramid cluster, as used for the attitude control of a satellite using machine learning (ML) techniques. A data-set, generated using a heuristic algorithm, relates the initial gimbal configuration and the desired maneuver—inputs—to a number of null space motions the gimbals have to execute—output. Two ML techniques—Deep Neural Network (DNN) and Random Forest Classifier (RFC)—are utilized to predict the required null motion for trajectories that are not included in the training set. The principal advantage of this approach is the exploitation of global information gathered from the whole maneuver compared to conventional steering laws that consider only some local information, near the current gimbal configuration for optimization and are prone to local extrema. The data-set generation and the predictions of the ML systems can be made offline, so no further calculations are needed on board, providing the possibility to inspect the way the system responds to any commanded maneuver before its execution. The RFC technique demonstrates enhanced accuracy for the test data compared to the DNN, validating that it is possible to correctly predict the null motion even for maneuvers that are not included in the training data

Design, Development, and a Pilot Study of a Low-Cost Robot for Child–Robot Interaction in Autism Interventions

Socially assistive robots are widely deployed in interventions with children on the autism spectrum, exploiting the benefits of this technology in social behavior intervention plans, while reducing their autistic behavior. Furthermore, innovations in modern technologies such as machine learning enhance these robots with great capabilities. Since the results of this implementation are promising, their total cost makes them unaffordable for some organizations while the needs are growing progressively. In this paper, a low-cost robot for autism interventions is proposed, benefiting from the advantages of machine learning and low-cost hardware. The mechanical design of the robot and the development of machine learning models are presented. The robot was evaluated by a small group of educators for children with ASD. The results of various model implementations, together with the design evaluation of the robot, are encouraging and indicate that this technology would be advantageous for deployment in child–robot interaction scenarios

Design, Development, and a Pilot Study of a Low-Cost Robot for Child–Robot Interaction in Autism Interventions

Socially assistive robots are widely deployed in interventions with children on the autism spectrum, exploiting the benefits of this technology in social behavior intervention plans, while reducing their autistic behavior. Furthermore, innovations in modern technologies such as machine learning enhance these robots with great capabilities. Since the results of this implementation are promising, their total cost makes them unaffordable for some organizations while the needs are growing progressively. In this paper, a low-cost robot for autism interventions is proposed, benefiting from the advantages of machine learning and low-cost hardware. The mechanical design of the robot and the development of machine learning models are presented. The robot was evaluated by a small group of educators for children with ASD. The results of various model implementations, together with the design evaluation of the robot, are encouraging and indicate that this technology would be advantageous for deployment in child–robot interaction scenarios