Robotics, Volume 13, Issue 5 (May 2024) – 7 articles

Although the use of articulated robots and AGVs is common in many industrial sectors such as automotive or aeronautics, the use of mobile manipulators is not widespread nowadays. Even so, the majority of applications separate the navigation and manipulation tasks, avoiding simultaneous movements of the platform and arm. The capability to use mobile manipulators to perform operations on moving objects would open the door to new applications such as the riveting or screwing of parts transported by conveyor belts or AGVs. This paper presents a novel position-based visual servoing (PBVS) architecture for mobile manipulators for precise industrial operations on moving parts. The proposed architecture includes a state machine to guide the process through the different phases of the task to ensure its correct execution. The approach has been validated in an industrial environment for screw-fastening operations, obtaining promising results and metrics. Full article

The integration of Virtual Reality with radiology is the focus of this study. A narrative review has been proposed to delve into emerging themes within the integration of Virtual Reality in radiology by scrutinizing reviews gathered from PubMed and Scopus. The proposed approach was based on a standard narrative checklist and a qualification process. The selection process identified 20 review studies. Integration of Virtual Reality (VR) in radiology offers potential transformative opportunities also integrated with other emerging technologies. In medical education, VR and AR, using 3D images from radiology, can enhance learning, emphasizing the need for standardized integration. In radiology, VR combined with Artificial Intelligence (AI) and Augmented Reality (AR) shows promising prospectives to give a complimentary contribution to diagnosis, treatment planning, and education. Challenges in clinical integration and User Interface design must be addressed. Innovations in medical education, like 3D modeling and AI, has the potential to enable personalized learning, but face standardization challenges. While robotics play a minor role, advancements and potential perspectives are observed in neurosurgery and endovascular systems. Ongoing research and standardization efforts are crucial for maximizing the potential of these integrative technologies in healthcare. In conclusion, the synthesis of these findings underscores the opportunities for advancements in digital radiology and healthcare through the integration of VR. However, challenges exist, and continuous research, coupled with technological refinements, is imperative to unlock the full potential of these integrative approaches in the dynamic and evolving field of medical imaging. Full article

In the rapidly evolving domain of conversational agents, the integration of Large Language Models (LLMs) into Chatbot Development Platforms (CDPs) is a significant innovation. This study compares the efficacy of employing generic and fine-tuned GPT-3.5-turbo models for designing dialog flows, focusing on the intent and entity recognition crucial for dynamic conversational interactions. Two distinct approaches are introduced: a generic GPT-based system (G-GPT) leveraging the pre-trained model with complex prompts for intent and entity detection, and a fine-tuned GPT-based system (FT-GPT) employing customized models for enhanced specificity and efficiency. The evaluation encompassed the systems’ ability to accurately classify intents and recognize named entities, contrasting their adaptability, operational efficiency, and customization capabilities. The results revealed that, while the G-GPT system offers ease of deployment and versatility across various contexts, the FT-GPT system demonstrates superior precision, efficiency, and customization, although it requires initial training and dataset preparation. This research highlights the versatility of LLMs in enriching conversational features for talking assistants, from social robots to interactive chatbots. By tailoring these advanced models, the fluidity and responsiveness of conversational agents can be enhanced, making them more adaptable and effective in a variety of settings, from customer service to interactive learning environments. Full article

Using the N400 component of event-related brain potentials, a neurophysiological marker associated with processing incongruity, we examined brain responses to sentences spoken by a robot that had no arms or legs. Statements concerning physically impossible actions (e.g., knitting) elicit significant N400 responses, reflecting that participants perceived these statements as incongruent with the robot’s physical condition. However, this effect was attenuated for participants who indicated that the robot could have hidden limbs, indicating that expectations modify the way an agent’s utterances are interpreted. When it came to statements relating to emotional capabilities a distinct pattern was found. Although participants acknowledged that the robot could have emotions, there were significant N400 responses to statements about the robot’s emotional experiences (e.g., feeling happy). This effect was not modified by participants’ beliefs, suggesting a cognitive challenge of accepting robots as capable of experiencing emotions. Our findings thus point to a boundary in human acceptance of artificial social agents: while physical attributes may be negotiable based on expectations, emotional expressions are more difficult to establish as credible. By elucidating the cognitive mechanisms at play, our study informs the design of social robots that are capable of more effective communication to better support social connectivity and human well-being. Full article

Unanimous action to achieve specific goals is crucial for the success of a robotic swarm. This requires clearly defined roles and precise communication between the robots of a swarm. An optimized task allocation algorithm defines the mechanism and logistics of decision-making that enable the robotic swarm to achieve such common goals. With more nodes, the traffic of messages that are required to communicate inside the swarm relatively increases to maintain decentralization. Increased traffic eliminates real-time capabilities, which is an essential aspect of a swarm system. The aim of this research is to reduce execution time while retaining efficient power consumption rates. In this research, two novel decentralized swarm communication algorithms are proposed, namely Clustered Dynamic Task Allocation–Centralized Loop (CDTA-CL) and Clustered Dynamic Task Allocation–Dual Loop (CDTA-DL), both inspired by the Clustered Dynamic Task Allocation (CDTA) algorithm. Moreover, a simulation tool was developed to simulate different swarm-clustered communication algorithms in order to calculate the total communication time and consumed power. The results of testing the proposed CDTA-DL and CDTA-CL against the CDTA attest that the proposed algorithm consumes substantially less time. Both CDTA-DL and CDTA-CL have achieved a significant speedup of 75.976% and 54.4% over CDTA, respectively. Full article

This study focuses on addressing the problem of motion planning within workspaces cluttered with obstacles while considering temporal and input constraints. These specifications can encapsulate intricate high-level objectives involving both temporal and spatial constraints. The existing literature lacks the ability to fulfill time specifications while simultaneously managing input-saturation constraints. The proposed approach introduces a hybrid three-component control algorithm designed to learn the safe execution of a high-level specification expressed as a timed temporal logic formula across predefined regions of interest in the workspace. The first component encompasses a motion controller enabling secure navigation within the minimum allowable time interval dictated by input constraints, facilitating the abstraction of the robot’s motion as a timed transition system between regions of interest. The second component utilizes formal verification and convex optimization techniques to derive an optimal high-level timed plan over the mentioned transition system, ensuring adherence to the agent’s specification. However, the necessary navigation times and associated costs among regions are initially unknown. Consequently, the algorithm’s third component iteratively adjusts the transition system and computes new plans as the agent navigates, acquiring updated information about required time intervals and associated navigation costs. The effectiveness of the proposed scheme is demonstrated through both simulation and experimental studies. Full article