This paper investigates the motion planning problem for industrial robots with complex constraints. An optimal and efficient hierarchical motion planner is proposed to obtain high-quality ...trajectories with low computational effort. First, the motion planning problem is formulated as an optimal control problem incorporating robot kinematics, obstacle-avoidance, and dynamics constraints. Thereafter, a hierarchical framework is constructed within the Markov decision process, consisting of two planners. In the high-level planner, a reinforcement learning-based policy is employed to generate virtual targets for the robot to navigate around obstacles, which can avoid collision detection. Then, in the low-level planner, a global orthogonal collocation method is used to generate time-energy optimal trajectories, considering dynamics, path, and boundary constraints such as joint position, velocity, and torque. Finally, simulation results on a 6-DOF (degree of freedom) and a 7-DOF industrial robot validate that the proposed method can produce high-quality trajectories with improved success rates and computation times compared to existing works.
Just as psychologists studying humans have found that the infant brain does not develop fully without social interaction, progress towards socially valuable AI will be stunted unless we put the ...problem of cooperation at the centre of our research. Most of the value from self-driving vehicles will come not from driving on empty roads, but from vehicles coordinating smoothly with the flow of pedestrians, cyclists and cars driven by humans. ...cooperative intelligence is not an alternative to autonomous intelligence, but goes beyond it. AI-AI cooperation Multi-agent Al research has seen most success in two-player zero-sum settings, from the superhuman performance of IBM's chess-playing computer Deep Blue to the powerful demonstration of deep reinforcement learning by the program AlphaGo. Collaborative industrial robots work on factory floors alongside labourers6, care robots help human health workers and personal Al assistants (such as Amazon's Alexa, Apple's Siri and Google Assistant) help us with scheduling, albeit in an elementary way.
The absolute positioning accuracy of an industrial robot arm is vital for advancing manufacturing-related applications like automatic assembly, which can be improved via the data-driven approaches to ...robot arm calibration. Existing data-driven calibrators have illustrated their efficiency in addressing the issue of robot arm calibration. However, they mostly are single learning models that can be easily affected by the insufficient representation of the solution space, therefore, suffering from the calibration accuracy loss. To address this issue, this study proposes a calibrator fuzzy ensemble (CFE) with twofold ideas: 1) implementing eight data-driven calibrators relying on different sophisticated machine learning algorithms for an industrial robot arm, which guarantees the accuracy of individual base models and 2) innovatively developing a fuzzy ensemble of the obtained eight diversified calibrators to obtain impressively high calibration accuracy for an industrial robot arm. Extensive experiments on an ABB IRB120 industrial robot implemented with MATLAB demonstrate that compared with state-of-the-art calibrators, CFE decreases the maximum error at 8.59%. Hence, it has great potential for real applications.
Robotic handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This ...makes them particularly fit for e-commerce operations. This paper reviews new categories of automated and robotic handling systems, such as shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and operations research modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Because of unique system features (such as autonomous control, flexible layout, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotic warehouse systems will form the next category of warehouses. All vital warehouse design, planning, and control logic, such as methods to design layout, storage and order-picking system selection, storage slotting, order batching, picker routing, and picker to order assignment, will have to be revisited for new robotized warehouses.
•Automation technology-driven innovation is growing in industry 4.0.•The role of robots in unconventional innovation is proposed.•We explore robots as an unconventional source of innovation.•Robots ...drive firm sustainable innovation.•Mediating effects of robots on innovation are empirically analyzed.
Automation technology-driven innovation, as a key component of the Fourth Industrial Revolution, has significantly increased in recent years with the rapid development of artificial intelligence and robots. This paper aims at investigating robots as an unconventional source of innovation and a driver of desirable outcomes such as sustainability. We study unconventional innovation with the adoption of robots through the lens of user innovation theory and carry out a series of empirical analyses by compiling a unique dataset containing firm characteristics, patent applications, and robot stock in Chinese manufacturing. The regression results show a positive and significant relationship between robots and firm innovation. We also find that robots drive firm sustainable innovation measured as patents that are related to environmental innovation. Lastly, we empirically analyze several mediating mechanisms of robots as the unconventional source of innovation through the factor-augmenting technology effect, substitution effect, and absorptive capacity effect.
Origami-inspired fabrication presents an attractive platform for miniaturizing machines: thinner layers of folding material lead to smaller devices, provided that key functional aspects, such as ...conductivity, stiffness, and flexibility, are persevered. Here, we show origami fabrication at its ultimate limit by using 2D atomic membranes as a folding material. As a prototype, we bond graphene sheets to nanometer-thick layers of glass to make ultrathin bimorph actuators that bend to micrometer radii of curvature in response to small strain differentials. These strains are two orders of magnitude lower than the fracture threshold for the device, thus maintaining conductivity across the structure. By patterning 2-μm-thick rigid panels on top of bimorphs, we localize bending to the unpatterned regions to produce folds. Although the graphene bimorphs are only nanometers thick, they can lift these panels, the weight equivalent of a 500-nm-thick silicon chip. Using panels and bimorphs, we can scale down existing origami patterns to produce a wide range of machines. These machines change shape in fractions of a second when crossing a tunable pH threshold, showing that they sense their environments, respond, and perform useful functions on time and length scales comparable with microscale biological organisms. With the incorporation of electronic, photonic, and chemical payloads, these basic elements will become a powerful platform for robotics at the micrometer scale.
Dynamic model has broad applications in motion planning, feedforward controller design, and disturbance observer design. Particularly, with the increasing application of model-based control in ...industrial robots, there has been a resurgence of research interest in accurate identification of dynamic models. However, on the one hand, most existing identification methods directly rely on least squares or weighted least squares (WLS), which suffer from outliers and could lead to physical infeasible solutions. On the other hand, nonlinearity of the friction model is seldom treated in a unified way with linear regression. Moreover, recent researches have shown that proper exciting trajectories are crucial to the identification accuracy, but few of previous works take measurement noise into consideration when optimizing the exciting trajectories. In this article, we propose an iterative approach which integrates WLS, iteratively reweighted least squares with linear matrix inequality constraints, and nonlinear friction models so that the above-mentioned issues can be properly solved altogether. Our research also reveals that performance can be improved by including priori knowledge of measurement noise in the optimization of exciting trajectories. The proposed approach is supported by experimental analysis of four different combinations within the framework on a 6-DoF industrial robot.
Touch sensing has a central role in robotic grasping and emerging human–machine interfaces for robot‐assisted prosthetics. Although advancements in soft conductive polymers have promoted the creation ...of diverse pressure sensors, these sensors are difficult to be employed as touch skins for robotics and prostheses due to their limited sensitivity, narrow pressure range, and complex structure and fabrication process. Here, a highly sensitive and robust soft touch skin is presented with ultracapacitive sensing that combines ionic hydrogels with commercially available conductive fabrics. Prototypical designs of the capacitive sensors through facile manufacturing methods are introduced and a high sensitivity up to 1.5 kPa−1 (≈44 times higher than conventional parallel‐plate capacitive counterparts), a broad pressure detection range of over four orders of magnitudes (≈35 Pa to 330 kPa), ultrahigh baseline of capacitance, fast response time (≈18 ms), and good repeatability are demonstrated. Ionogel skins composed of an array of cutaneous mechanoreceptors capable of monitoring various physiological signals and shape detection are further developed. The touch skin can be integrated within a soft bionic hand and provide an industrial robot and an amputee with robust tactile feedback when handling delicate objects, illustrating its potential applications in next‐generation human‐in‐the‐loop robotic systems with tactile sensing.
A highly sensitive and robust cutaneous ionogel mechanoreceptor is proposed through facile manufacturing methods. The hydrogel‐based hierarchical architecture harnesses the ultracapacitive mechanism to achieve enhanced pressure‐sensing functionality with high bandwidth and sensitivity. This scalable touch skin can provide tactile sensing for soft machines, physiological signal monitoring, industrial robots, and amputee prostheses.
Flexibility models based on the virtual joint approach (VJA) are essential for error compensation to improve the positioning accuracy of industrial robots across a range of payloads. However, current ...flexibility models are not accurate enough due to less consideration of deformation, or incorporate too many factors leading to difficulties in practical applications. This paper proposes a flexibility model based on the error sensitivity analysis to improve the positioning accuracy and stability of industrial robots. First, the effects of the six directions flexible deformation of the joint on the positioning error are analyzed by introducing the Sobol's method. It indicates that the rotational deformations around <inline-formula> <tex-math notation="LaTeX">X</tex-math> </inline-formula>, <inline-formula> <tex-math notation="LaTeX">Y</tex-math> </inline-formula> and <inline-formula> <tex-math notation="LaTeX">Z</tex-math> </inline-formula>-axes cause the majority of positioning errors, and only a tiny minority is originated from translational deformations along <inline-formula> <tex-math notation="LaTeX">X</tex-math> </inline-formula>, <inline-formula> <tex-math notation="LaTeX">Y</tex-math> </inline-formula>, and <inline-formula> <tex-math notation="LaTeX">Z</tex-math> </inline-formula>-axes. Then, a mapping equation between the flexible deformations around <inline-formula> <tex-math notation="LaTeX">X</tex-math> </inline-formula>, <inline-formula> <tex-math notation="LaTeX">Y</tex-math> </inline-formula> and <inline-formula> <tex-math notation="LaTeX">Z</tex-math> </inline-formula>-axes and the positioning error is derived based on this observation. Finally, a flexibility model for <inline-formula> <tex-math notation="LaTeX">N</tex-math> </inline-formula> degrees of freedom (DoF) industrial robots is established and an identification method is presented for flexibility coefficients. The verification experiments are performed on a 6-DoF robot, and an application example is provided for error compensation in robotic assembly tasks. The experimental results show that the proposed model has higher accuracy and stability but lower calculation cost than conventional models. Moreover, after compensation, the pose error is reduced to 0.1mm and 0.03<inline-formula> <tex-math notation="LaTeX">^{\circ}</tex-math> </inline-formula> meeting the assembly requirements in the application example. Note to Practitioners -The joint deformation under payload and link gravity is mainly responsible for the degraded positioning accuracy of industrial robots. Error compensation by flexibility models is an effective way to improve positioning accuracy. Traditional 6-DoF flexibility models are too complex to be used in industrial scenarios, while 1-DoF flexibility models are not accurate enough. This paper proposes an error sensitivity flexibility compensation that considers the main factors affecting the positioning error, while removing factors with less impact. Compared with traditional models, the proposed model combines both high accuracy and applicability. Through establishing the mathematical equation between the joint deformation and the positioning error, a new flexibility model is derived, and an easy-to-implement method is provided to identify flexibility coefficients. The proposed model can be used conveniently for high-precision compensation of errors, including applications with constant payloads, such as assembly, cutting and welding, as well as tasks with variable payloads, such as milling, drilling and de-burring. In addition, the model can also be used to optimize poses to reduce robot flexibility and enhance resistance to deformation in one workspace. Experiments indicate that high compensation accuracy can be obtained by applying the flexibility model to robot assembly tasks.
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