Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning

This paper presents an efficient learning-based method to solve the inverse kinematic (IK) problem on soft robots with highly non-linear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware. The effectiveness of our approach has been verified on pneumatic-driven soft robots for path following and interactive positioning

Nanostructure and microstructure fabrication:from desired properties to suitable processes

\u3cp\u3eWhen designing a new nanostructure or microstructure, one can follow a processing-based manufacturing pathway, in which the structure properties are defined based on the processing capabilities of the fabrication method at hand. Alternatively, a performance-based pathway can be followed, where the envisioned performance is first defined, and then suitable fabrication methods are sought. To support the latter pathway, fabrication methods are here reviewed based on the geometric and material complexity, resolution, total size, geometric and material diversity, and throughput they can achieve, independently from processing capabilities. Ten groups of fabrication methods are identified and compared in terms of these seven moderators. The highest resolution is obtained with electron beam lithography, with feature sizes below 5 nm. The highest geometric complexity is attained with vat photopolymerization. For high throughput, parallel methods, such as photolithography (≈10\u3csup\u3e1\u3c/sup\u3e m\u3csup\u3e2\u3c/sup\u3e h\u3csup\u3e−1\u3c/sup\u3e), are needed. This review offers a decision-making tool for identifying which method to use for fabricating a structure with predefined properties.\u3c/p\u3

Environmental sizing of smartphone batteries

\u3cp\u3eSmartphone use has increased at a phenomenal pace worldwide. In 2011 more smartphones have been sold than desktop pc's, notebooks, netbooks and tablets together. The total worldwide smartphone sales reached 472 million units in 2011, and 149 million of them were sold in the fourth quarter of 2011. The smartphone is, like almost every other mobile device, powered by batteries, limited in size and therefore capacity, which makes energy management paramount. While global demand and use of mobile devices continuously expands, the energy density of smartphone batteries has grown at an insignificant rate, but the use period still decreases because of high loads and big screens. In this paper we have studied the power breakdown of five smartphones on sale in 2011. We have defined three different user profiles for 'heavy', 'moderate' and 'light' users and we can state that theoretically it is sensible to re-size the battery based on the user profile. While keeping the user period acceptable we can decrease the battery capacity for moderate and light users with 25%, reducing the worldwide energy needed to product smartphone batteries with 2.1 to 3.4PJ per year. In practice the aging of the battery will result in a decreasing battery capacity over its life. When taking this into account most batteries comply with the moderate users and only a resizing strategy for the light users is sensible. This will account for only 20% of all users and can result in a worldwide decrease of energy needed for producing the smartphone batteries with 0.5 to 0.9PJ.\u3c/p\u3

Computational Design for Digitally Fabricated 3D Inductive Power Transfer Coils

The geometric shapes and the relative position of coils influence the performance of a three-dimensional (3D) inductive power transfer system. In this paper, we propose a coil design method for specifying the positions and the 3D shapes of a pair of coils to transmit the desired power. Given region of interests (ROIs) for designing the transmitter and the receiver coils on two surfaces, the transmitter coil is generated around the center of its ROI. The center of the receiver coil is estimated as a random seed position in the corresponding 3D surface. At this position, we use the heatmap method with electromagnetic constraints to iteratively extend the coil until the desired power can be transferred via the set of coils. In each step, the shape of the extension, i.e., a new turn of the receiver coil, is found as a spiral curve based on the convex hulls of the 2D projected adjacent turns along their normal direction. Then, the optimal position of the receiver coil isfound by maximizing the efficiency of the system. In the next step, the position and the shape of the transmitter coil are optimized based on the fixed receiver coil using the same method. This optimization process iterates until an optimum is reached. Simulations and experiments with digitally fabricated prototypes were conducted and the effectiveness of the proposed 3D coil design method was verified. [DOI: 10.1115/1.4053500]Accepted Author ManuscriptMechatronic DesignEmerging Material

Computer Supported Collaborative Work in Ultra Personalized Products and Services

How does individualized production look like? This paper proposes a genericvision of a smart factory which is able to produce so-called ultra personalized products(UPPS) in a batch size of one. This means that each customer is able to configure his or her personal product perfectly adjusted to the needs on base of personal data like bodyscans. As we are convinced that there will always be humans involved in planning and conducting manufacturing processes, we want to highlight the human-to-human interaction and the computer supported collaborative work, which is required in order to set up such a production line.Mechatronic DesignInternet of ThingsSystem EngineeringEmerging Material

Planning Jerk-Optimized Trajectory With Discrete Time Constraints for Redundant Robots

We present a method for effectively planning the motion trajectory of robots in manufacturing tasks, the tool paths of which are usually complex and have a large number of discrete time constraints as waypoints. Kinematic redundancy also exists in these robotic systems. The jerk of motion is optimized in our trajectory planning method at the meanwhile of fabrication process to improve the quality of fabrication. Our method is based on a sampling strategy and consists of two major parts. After determining an initial path by graph search, a greedy algorithm is adopted to optimize a path by locally applying adaptive filers in the regions with large jerks. The filtered result is obtained by numerical optimization. In order to achieve efficient computation, an adaptive sampling method is developed for learning a collision-indication function that is represented as a support-vector machine. Applications in robot-Assisted 3-D printing are given in this article to demonstrate the functionality of our approach. Note to Practitioners-In robot-Assisted manufacturing applications, robotic arms are employed to realize the motion of workpieces (or machining tools) specified as a sequence of waypoints with the positions of tool tip and the tool orientations constrained. The required degree of freedom (DOF) is often less than the robotic hardware system (e.g., a robotic arm has six-DOF). Specifically, rotations of the workpiece around the axis of a tool can be arbitrary (see Fig. 1 for an example). By using this redundancy, i.e., there are many possible poses of a robotic arm to realize a given waypoint, the trajectory of robots can be optimized to consider the performance of motion in velocity, acceleration, and jerk in the joint space. In addition, when fabricating complex models, each tool path can have a large amount of waypoints. It is crucial for a motion planning algorithm to compute a smooth and collision-free trajectory of robot to improve the fabrication quality. The time taken by the planning algorithm should not significantly lengthen the total manufacturing time; ideally, it would remain hidden as computing motions for a layer can be done while the previous layer is printing. The method presented in this article provides an efficient framework to tackle this problem. The framework has been well tested on our robot-Assisted additive manufacturing system to demonstrate its effectiveness and can be generally applied to other robot-Assisted manufacturing systems.</p