Biomimetic Intelligence and Robotics最新文献

Swift perception of interaction forces is a crucial skill required for legged robots to ensure safe human–robot interaction and dynamic contact management. Proprioceptive-based interactive force is widely applied due to its outstanding cross-platform versatility. In this paper, we present a novel interactive force observer, which possesses superior dynamic tracking performance. We propose a dynamic cutoff frequency configuration method to replace the conventional fixed cutoff frequency setting in the traditional momentum-based observer (MBO). This method achieves a balance between rapid tracking and noise suppression. Moreover, to mitigate the phase lag introduced by the low-pass filtering, we cascaded a Newton Predictor (NP) after MBO, which features simple computation and adaptability. The precision analysis of this method has been presented. We conducted extensive experiments on the point-foot biped robot BRAVER to validate the performance of the proposed algorithm in both simulation and physical prototype.

Continuum robots, which are characterized by high length-to-diameter ratios and flexible structures, show great potential for various applications in confined and irregular environments. Due to the combination of motion modes, the existence of multiple solutions, and the presence of complex obstacle constraints, motion planning for these robots is highly challenging. To tackle the challenges of online and flexible operation for continuum robots, we propose a flexible head-following motion planning method that is suitable for scalable and bendable continuum robots. Firstly, we establish a piecewise constant curvature (PCC) kinematic model for scalable and bendable continuum robots. The article proposes an adaptive auxiliary points model and a method for updating key nodes in head-following motion to enhance the precise tracking capability for paths with different curvatures. Additionally, the article integrates the strategy for adjusting the posture of local joints of the robot into the head-following motion planning method, which is beneficial for achieving safe obstacle avoidance in local areas. The article concludes by presenting the results of multiple sets of motion simulation experiments and prototype experiments. The study demonstrates that the algorithm presented in this paper effectively navigates and adjusts posture to avoid obstacles, meeting the real-time demands of online operations. The average time for a single-step solution is $4.41\times 10^{-5}$ s, and the average tracking accuracy for circular paths is 7.8928 mm.

In the field of pipeline inner wall inspection, the snake robot demonstrates significant advantages over other inspection methods. While a simple traveling wave or meandering motion will suffice for inspecting the inner wall of small-diameter pipes, comprehensively and meticulously inspecting the inner wall of large-diameter pipes requires the snake robot to adopt a helical gait that closely adheres to the inner wall. Our review of existing literature indicates that most research and development on the helical gait of snake robots has focused on the outer surface of cylinders, with very few studies dedicated to developing a helical gait specifically for the inspection of the inner wall of pipes. Therefore, in this study, we propose a helical gait that is suitable for the inner wall of pipes and meets the requirements of gas pipeline engineering. The helical gait is designed using the backbone curve method. First, we create a mathematical model for a circular helix curve with constant curvature and torsion, ensuring it is applicable to a snake robot prototype in a laboratory environment. Subsequently, we calculate the joint angles required for two conical spiral curves with variable curvature and torsion, establish a new model, and define the physical significance of the specific parameters. To ensure the feasibility of the proposed gait, we conduct experiments involving meandering and traveling wave motions to verify the communication and control between the host computer and the snake robot. Building upon this foundation, we further validate the mathematical model of the complex helical motion gait through simulation experiments. Our findings provide a theoretical basis for realizing helical movement with a real snake robot.

Customized 3D-printed structural parts are widely used in surgical robotics. To satisfy the mechanical properties and kinematic functions of these structural parts, a topology optimization technique is adopted to obtain the optimal structural layout while meeting the constraints and objectives. However, topology optimization currently faces some practical challenges that must be addressed, such as ensuring that structures do not have significant defects when converted to additive manufacturing models. To address this problem, we designed a 3D hierarchical fully convolutional network (FCN) to predict the precise position of the defective structures. Based on the prediction results, an effective repair strategy is adopted to repair the defective structure. A series of experiments is conducted to demonstrate the effectiveness of our approach. Compared to the 2D fully convolutional network and the rule-based detection method, our approach can accurately capture most defect structures and achieve 89.88% precision and 95.59% recall. Furthermore, we investigate the impact of different ways to increase the receptive field of our model, as well as the trade-off between different defect-repairing strategies. The results of the experiment demonstrate that the hierarchical structure, which increases the receptive field, can substantially improve the defect detection performance. To the best of our knowledge, this paper is the first to investigate 3D defect prediction and repair for topology optimization in conjunction with deep learning algorithms, providing practical tools and new perspectives for the subsequent development of topology optimization techniques.

Acoustic propulsion system presents a novel underwater propulsion approach in small scale swimmer. This study introduces a submerged surface acoustic wave (SAW) propulsion system based on the SiO₂/Al/LiNbO ₃ structure. At 19.25 MHz, the SAW propulsion system is proposed and investigated by the propulsion force calculation, PIV measurements and propulsion measurements. 3.3 mN propulsion force is measured at 27.6 V${}_{pp}$. To evaluate the miniature swimmer, the SAW propulsion systems with multiple frequencies are studied. At 2.2 W, the submerged SAW propulsion system at 38.45 MHz demonstrates 0.83 mN/mm${}^2$ propulsion characteristics. At 96.13 MHz and 24 V${}_{pp}$, the movements of miniature swimmer with a fully submerged SAW propulsion system are recorded and analyzed to a maximum of 177 mm/s. Because of miniaturization, high power density, and simple structure, the SAW propulsion system can be expected for some microrobot applications, such as underwater drone, pipeline robot and intravascular robot.

Generating efficient locomotion in granular media is important, although it is difficult for robots. Inspired by the fact that sand vipers usually have saw-like scales, in this study, we design a soft undulation robot with tangential anisotropic friction to enhance the undulation performance of soft robots in granular media. A mathematical model was derived and numerical simulations were conducted accordingly to investigate the effectiveness of tangential friction anisotropy for undulation gait generation in granular media. In particular, we introduce a pseudo-rigid-body dynamics model consisting of links and joints while simulating the pneumatic actuation method to more closely approximate the response of soft robots. Moreover, a soft snake-like robot was fabricated, and its forward and reverse undulations were compared in two sets of controlled experiments. The consistency between the experimental results and the numerical simulations confirms that tangential anisotropic friction induces a propulsive effect in undulation, thereby increasing the robot’s locomotion speed. This discovery provides new insights into the design of undulation robots in granular environments.

This study presents an innovative approach in soft robotics, focusing on an inchworm-inspired robot designed for enhanced transport capabilities. We explore the impact of various parameters on the robot’s performance, including the number of activated sections, object size and material, supplied air pressure, and command execution rate. Through a series of controlled experiments, we demonstrate that the robot can achieve a maximum transportation speed of 8.54 mm/s and handle loads exceeding 100 g, significantly outperforming existing models in both speed and load capacity. Our findings provide valuable insights into the optimization of soft robotic design for improved efficiency and adaptability in transport tasks. This research not only contributes to the advancement of soft robotics but also opens new avenues for practical applications in areas requiring precise and efficient object manipulation. The study underscores the potential of biomimetic designs in robotics and sets a new benchmark for future developments in the field.

Soft robotics is a breakthrough technology to support human–robot interactions. The soft structure of a soft robot can increase safety during human and robot interactions. One of the promising soft actuators for soft robotics is dielectric elastomer actuators (DEAs). DEAs can operate silently and have an excellent energy density. The simple structure of DEAs leads to the easy fabrication of soft actuators. The simplicity combined with silent operation and high energy density make DEAs interesting for soft robotics researchers. DEAs actuation follows the Maxwell-pressure principle. The pressure produced in the DEAs actuation depends much on the voltage applied. Common DEAs requires high voltage to gain an actuation. Since the power consumption of DEAs is in the milli-Watt range, the current needed to operate the DEAs can be neglected. Several commercially available DC-DC converters can convert the volt range to the kV range. In order to get a voltage in the 2–3 kV range, the reliable DC-DC converter can be pricy for each device. This problem hinders the education of soft actuators, especially for a newcomer laboratory that works in soft electric actuators. This paper introduces an entirely do-it-yourself (DIY) Ultrahigh voltage amplifier (UHV-Amp) for education in soft robotics. UHV-Amp can amplify 12 V to at a maximum of 4 kV DC. As a demonstration, we used this UHV-Amp to test a single layer of powdered-based DEAs. The strategy to build this educational type UHV-Amp was utilizing a Cockcroft-Walton circuit structure to amplify the voltage range to the kV range. In its current state, the UHV-Amp has the potential to achieve approximately 4 kV. We created a simple platform to control the UHV-Amp from a personal computer. In near future, we expect this easy control of the UHV-Amp can contribute to the education of soft electric actuators.

Sampling-based path planning is widely used in robotics, particularly in high-dimensional state spaces. In the path planning process, collision detection is the most time-consuming operation. Therefore, we propose a learning-based path planning method that reduces the number of collision checks. We develop an efficient neural network model based on graph neural networks. The model outputs weights for each neighbor based on the obstacle, searched path, and random geometric graph, which are used to guide the planner in avoiding obstacles. We evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world experiments. The results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.

The remarkable skill of changing its grasp status and relocating its fingers to perform continuous in-hand manipulation is essential for a multifingered anthropomorphic hand. A commonly utilized method of manipulation involves a series of basic movements executed by a high-level controller. However, it remains unclear how these primitives evolve into sophisticated finger gaits during manipulation. Here, we propose an adaptive finger gait-based manipulation method that offers real-time regulation by dynamically changing the primitive interval to ensure the force/moment balance of the object. Successful manipulation relies on contact events that act as triggers for real-time online replanning of multifinger manipulation. We identify four basic motion primitives of finger gaits and create a heuristic finger gait that enables the continuous object rotation of a round cup. Our experimental results verify the effectiveness of the proposed method. Despite the constant breaking and reengaging of contact between the fingers and the object during manipulation, the robotic hand can reliably manipulate the object without failure. Even when the object is subjected to interfering forces, the proposed method demonstrates robustness in managing interference. This work has great potential for application to the dexterous operation of anthropomorphic multifingered hands.