Effortless creation of safe robots from modules through self-programming and self-verification

Sustainable manufacturing through application of reconfigurable and intelligent systems in production processes: a system perspective

Sustainable production aims at creating products from processes that minimize environmental impact, energy consumption and natural resources. Customers and markets are ever more leaning towards digital, custom, and flexible solutions with lower environmental impact. Hence, Industry 4.0 (I4.0) solutions are increasingly including social and environmental sustainability aspects. We focus on the realization of an infrastructure integrating industrially relevant application modules by combining system reconfigurability and artificial intelligence, towards sustainable production. To meet the final goal of sustainable production, we address four challenges considering flexibility and sustainability in production in a holistic way: (1) developing infrastructural and methodological tools to support companies to explore the potential of I4.0 towards sustainable production; (2) managing the configurability and customization possibilities of products; (3) effectively handling the flexibility provided by a production system with rapid reconfiguration capabilities; (4) integrating hardware and software flexibility by using reconfigurable robotics and machine learning methods. By developing and connecting different application modules, we obtain a physical demonstrator which represents on the one hand an exemplary scenario of reconfigurable and flexible production system; on the other, it enables new research activities and insights with a see, touch & feel approach for industrial and research realities.

Vision-controlled jetting for composite systems and robots

Recreating complex structures and functions of natural organisms in a synthetic form is a long-standing goal for humanity1. The aim is to create actuated systems with high spatial resolutions and complex material arrangements that range from elastic to rigid. Traditional manufacturing processes struggle to fabricate such complex systems2. It remains an open challenge to fabricate functional systems automatically and quickly with a wide range of elastic properties, resolutions, and integrated actuation and sensing channels2,3. We propose an inkjet deposition process called vision-controlled jetting that can create complex systems and robots. Hereby, a scanning system captures the three-dimensional print geometry and enables a digital feedback loop, which eliminates the need for mechanical planarizers. This contactless process allows us to use continuously curing chemistries and, therefore, print a broader range of material families and elastic moduli. The advances in material properties are characterized by standardized tests comparing our printed materials to the state-of-the-art. We directly fabricated a wide range of complex high-resolution composite systems and robots: tendon-driven hands, pneumatically actuated walking manipulators, pumps that mimic a heart and metamaterial structures. Our approach provides an automated, scalable, high-throughput process to manufacture high-resolution, functional multimaterial systems.

Online Computation of Time-Optimization-Based, Smooth and Path-Consistent Stop Trajectories for Robots

Enforcing the cessation of motion is a common action in robotic systems to avoid the damage that the robot can exert on itself, its environment or, in shared environments, people. This procedure raises two main concerns, which are addressed in this paper. On the one hand, the stopping procedure should respect the collision free path computed by the motion planner. On the other hand, a sudden stop may produce large current peaks and challenge the limits of the motor’s control capabilities, as well as degrading the mechanical performance of the system, i.e., increased wear. To address these concerns, we propose a novel method to enforce a mechanically feasible, smooth and path-consistent stop of the robot based on a time-minimization algorithm. We present a numerical implementation of the method, as well as a numerical study of its complexity and convergence. Finally, an experimental comparison with an off-the-shelf stopping scheme is presented, showing the effectiveness of the proposed method.

Multifunctional Electronic Skins Enable Robots to Safely and Dexterously Interact with Human

Human-robot collaboration is playing more and more important roles in current deployments of robotic systems in our lives. Haptic perception and intelligent control are essential to ensure safety and efficiency of human-robot interaction. However, existing robotic sensory and control systems are deficient in terms of performance issues, complexity, and cost. Here, the authors report a multifunctional electronic skin (e-skin) incorporating multiple perceptions with intelligent robotic control, by which robots can safely and dexterously interact with humans. The e-skin with a simple and cost-effective sensory structure has multimodal perceptions of proximity, temperature, contact force, and contact position with broad measuring range, high sensitivity, and fast response. The e-skin is applied onto robots to accomplish obstacle avoidance, safe and dexterous human-robot interaction, smart teaching, and playing Tai-Chi, which demonstrate a broad range of applications for intelligent robots equipped with e-skins.

Crash test-based assessment of injury risks for adults and children when colliding with personal mobility devices and service robots

Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults who are inherently smaller and fragile. This study compared the risks faced by different pedestrian categories and determined risks through crash testing involving a service robot hitting an adult and a child dummy. Results of collisions at 3.1 m/s (11.1 km/h/6.9 mph) showed risks of serious head (14%), neck (20%), and chest (50%) injuries in children, and tibia fracture (33%) in adults. Furthermore, secondary impact analysis resulted in both populations at risk of severe head injuries, namely, from falling to the ground. Our data and simulations show mitigation strategies for reducing impact injury risks below 5% by either lowering the differential speed at impact below 1.5 m/s (5.4 km/h/3.3 mph) or through the usage of absorbent materials. The results presented herein may influence the design of controllers, sensing awareness, and assessment methods for robots and small vehicles standardization, as well as, policymaking and regulations for the speed, design, and usage of these devices in populated areas.

Application of an adapted FMEA framework for robot-inclusivity of built environments

Mobile robots are deployed in the built environment at increasing rates. However, lack of considerations for a robot-inclusive planning has led to physical spaces that would potentially pose hazards to robots, and contribute to an overall productivity decline for mobile service robots. This research proposes the use of an adapted Failure Mode and Effects Analysis (FMEA) as a structured tool to evaluate a building's level of robot-inclusivity and safety for service robot deployments. This Robot-Inclusive FMEA (RIFMEA) framework, is used to identify failures in the built environment that compromise the workflow of service robots, assess their effects and causes, and provide recommended actions to alleviate these problems. The method was supported with a case study of deploying telepresence robots in a university campus. The study concluded that common failures were related to poor furniture design, a lack of clearance and hazard indicators, and sub-optimal interior planning.

Assembly Configuration Representation and Kinematic Modeling for Modular Reconfigurable Robots Based on Graph Theory

A Modular Reconfigurable Robot (MRR) composed of standard joint and link modules has the advantages of rapid changeover of assembly configurations for a diversity of application requirements. To tackle the kinematics analysis issues for an MRR, it is essential to develop a generic method to mathematically represent all possible assembly configurations and automatically generate their kinematic models. In this paper, two types of robot modules, i.e., revolute joint modules and rigid link modules, are considered as the basic building blocks of an MRR. The topological structure of an MRR is represented by a graph, termed an Assembly Graph (AG), in which all robot modules, regardless of their types, are treated as vertices, while the connecting interfaces between any two robot modules are treated as edges. Based on graph theory, a modified adjacency matrix, termed an Assembly Adjacency Matrix (AAM), is proposed to represent the assembly configuration of an MRR, in which the three-dimensional assembly information is specified with connecting port vectors for the adjacent modules. A path matrix derived from the AAM of an MRR is employed to describe the connecting sequence of its constituting modules. The local frame representation of the Product-of-Exponentials (POE) formula is employed, which is configuration-independent. Therefore, for an MRR configuration represented with an AAM, its kinematic model will be automatically generated according to its path matrix. The proposed assembly configuration representation and kinematic modeling method are validated through a dual-branch MRR configuration.

GuLiM: A Hybrid Motion Mapping Technique for Teleoperation of Medical Assistive Robot in Combating the COVID-19 Pandemic

Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.