Recent Advances in Motion Control of Parallel Robots for High-Speed Industrial Applications
Serial robotic manipulators consist of a set of sequentially connected links, forming an open kinematic chain. These robots are mainly characterized by their large workspace and their high dexterity. However, despite these advantages, in order to perform tasks requiring high speeds/accelerations and/or high precision; such robots are not always recommended because of their lack of stiffness and accuracy. Indeed, parallel kinematic manipulators (PKMs) are more suitable for such tasks. The main idea of their mechanical structure consists in using at least two kinematic chains linking the fixed base to the travelling plate, where each of these chains contains at least one actuator. This may allow a good distribution of the load between the chains. PKMs have important advantages with respect to their serial counterparts in terms of stiffness, speed, accuracy and payload. However, these robots are characterized by their high nonlinear dynamics, kinematic redundancy, uncertainties, actuation redundancy, singularities, etc. Besides, when interested in high-speed robotized repetitive tasks, such as food packaging and waste sorting applications, the key idea lies in looking for short cycle times. This means obviously to look for short motion and short stabilization times while guaranteeing the robustness and performance with respect to disturbances and changes/uncertainties in the operational conditions. Consequently, if we are interested in control of such robots, all these issues should be taken into account, which makes it a bit challenging task.
This talk will give an overview of some proposed advanced control solutions for high-speed industrial applications of PKMs in food packaging, waste sorting, and machining tasks. The proposed solutions are mainly borrowed from nonlinear robust and adaptive control techniques and have been validated through real-time experiments on different PKM prototypes.
Smart City Applications with Pervasive AI
Abstract: Internet of Things (IoT) systems have expanded the role of Artificial intelligence (AI) in many applications. On the other hand, AI has witnessed a substantial usage in different IoT applications and services, spanning the smart city systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensed data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams to predict future insights and revolutionize the decision-taking process, inaugurates pervasive AI systems as a worthy paradigm to achieve better predictions which can lead to a better quality-of-life. The confluence of pervasive computing and artificial intelligence (Pervasive AI) expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy concerns, scalability, and latency requirements. In this context, a wise cooperation and resource scheduling should be envisaged for a smart city using IoT devices (e.g., smartphones, smart healthcare, and smart vehicles) and infrastructure (e.g., edge nodes and base stations) to avoid communication and computation overheads and ensure maximum performance and efficient accuracy.
In this talk, a quick review of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems will be given. Specifically, a description of pervasive computing, its architecture, and its intersection with artificial intelligence is presented. Then, we review the background, applications, and performance metrics of AI, particularly Federated Learning (FL), running in a ubiquitous system. Next, we present some communication-efficient techniques of distributed inference, training and learning tasks across a plethora of IoT devices, edge devices and cloud servers. Finally, we discuss future directions in this area and provide some research challenges.