Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments

Combining symbolic and geometric reasoning in multi-agent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility that is intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.Comment: 12 pages, 6 figures, accepted for publication on IEEE Transactions on Cybernetics in March 202

Call preparation documents: Guide for Applicants, Model Contract, Proposal Template, Call text, and publication plan for the call

Project HORSE, co-funded from the European Union’s Horizon 2020 research and innovation programme under agreement No 680734, foresees as an eligible activity the provision of financial support to third parties, as means to achieve its own objectives. The HORSE framework is driven by and validated with end-users - manufacturing companies- in two steps: in the first, the framework was jointly developed together with the selected end-users (Pilot Experiments); in the second, its suitability and transferability to further applications will be validated with new end-users recruited via this Open Call

Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration

A good estimation of the actions' cost is key in task planning for human-robot collaboration. The duration of an action depends on agents' capabilities and the correlation between actions performed simultaneously by the human and the robot. This paper proposes an approach to learning actions' costs and coupling between actions executed concurrently by humans and robots. We leverage the information from past executions to learn the average duration of each action and a synergy coefficient representing the effect of an action performed by the human on the duration of the action performed by the robot (and vice versa). We implement the proposed method in a simulated scenario where both agents can access the same area simultaneously. Safety measures require the robot to slow down when the human is close, denoting a bad synergy of tasks operating in the same area. We show that our approach can learn such bad couplings so that a task planner can leverage this information to find better plans.Comment: Accepted at IEEE Int. Conf. on Emerging Technology and Factory Automation, 202

Robots in Industry. Past,present and future of a growing collaboration with humans

Robots have been part of automation systems for a very long time, and in public perception, they are often synonymous with automation and industrial revolution perse. Fueled by Industry 4.0 and Internet of Things (IoT) concepts as well as by new software technologies, the field of robotics in industry is currently undergoing a revolution on its own. This article gives an overview of the evolution of robotics from its beginnings to recent trends like collaborative robotics, autonomous robots, and human- robot interaction. Particular attention is devoted to the deep changes of the last decades, from the traditional industrial scenario based on isolated robotic cells up to the most recent coworking and collaborative robots. The role of robotics in the Industry 4.0 framework is analyzed, and the relationships with industrial communications and software technologies are also discussed. Some future directions for robotics are envisaged, focusing on the contributions coming from new materials, sensors, actuators, and technologies. Open issues are highlighted as well as the main barriers that currently limit the deployment of industrial robots in the small and medium enterprise (SME) world

Human centric collaborative workplace: the human robot interaction system perspective

The implementation of smart technologies and physical collaboration with robots in manufacturing can provide competitive advantages in production, performance and quality, as well as improve working conditions for operators. Due to the rapid advancement of smart technologies and robot capabilities, operators face complex task processes, decline in competences due to robots overtaking tasks, and reduced learning opportunities, as the range of tasks that they are asked to perform is narrower. The Industry 5.0 framework introduced, among others, the human-centric workplace, promoting operators wellbeing and use of smart technologies and robots to support them. This new human centric framework enables operators to learn new skills and improve their competencies. However, the need to understand the effects of the workplace changes remain, especially in the case of human robot collaboration, due to the dynamic nature of human robot interaction. A literature review was performed, initially, to map the effects of workplace changes on operators and their capabilities. Operators need to perform tasks in a complex environment in collaboration with robots, receive information from sensors or other means (e.g. through augmented reality glasses) and decide whether to act upon them. Meanwhile, operators need to maintain their productivity and performance. This affects cognitive load and fatigue, which increases safety risks and probability of human-system error. A model for error probability was formulated and tested in collaborative scenarios, which regards the operators as natural systems in the workplace environment, taking into account their condition based on four macro states; behavioural, mental, physical and psychosocial. A scoping review was then performed to investigate the robot design features effects on operators in the human robot interaction system. Here, the outcomes of robot design features effects on operators were mapped and potential guidelines for design purposes were identified. The results of the scoping review showed that, apart from cognitive load, operators perception on robots reliability and their safety, along with comfort can influence team cohesion and quality in the human robot interaction system. From the findings of the reviews, an experimental study was designed with the support of the industrial partner. The main hypothesis was that cognitive load, due to collaboration, is correlated with quality of product, process and human work. In this experimental study, participants had to perform two tasks; a collaborative assembly and a secondary manual assembly. Perceived task complexity and cognitive load were measured through questionnaires, and quality was measured through errors participants made during the experiment. Evaluation results showed that while collaboration had positive influence in performing the tasks, cognitive load increased and the temporal factor was the main reason behind the issues participants faced, as it slowed task management and decision making of participants. Potential solutions were identified that can be applied to industrial settings, such as involving participants/operators in the task and workplace design phase, sufficient training with their robot co-worker to learn the task procedures and implement direct communication methods between operator and robot for efficient collaboration

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers