Biomimetic Intelligence and Robotics最新文献

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.

The versatile motion capability of snake robots offers themselves robust adaptability in varieties of challenging environments where traditional robots may be incapacitated. This study reports a novel flexible snake robot featuring a rigid–flexible coupling structure and multiple motion gaits. To better understand the robot’s behavior, a bending model for the soft actuator is established. Furthermore, a dynamic model is developed to map the relationship between the input air pressure and joint torque, which is the model base for controlling the robot effectively. Based on the wave motion generated by the joint coupling direction function in different planes, multiple motion gait planning methods of the snake-like robot are proposed. In order to evaluate the adaptability and maneuverability of the developed snake robot, extensive experiments were conducted in complex environments. The results demonstrate the robot’s effectiveness in navigating through intricate settings, underscoring its potential for applications in various fields.

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.

Human–robot object handover is one of the most primitive and crucial capabilities in human–robot collaboration. It is of great significance to promote robots to truly enter human production and life scenarios and serve human in numerous tasks. Remarkable progressions in the field of human–robot object handover have been made by researchers. This article reviews the recent literature on human–robot object handover. To this end, we summarize the results from multiple dimensions, from the role played by the robot (receiver or giver), to the end-effector of the robot (parallel-jaw gripper or multi-finger hand), to the robot abilities (grasp strategy or motion planning). We also implement a human–robot object handover system for anthropomorphic hand to verify human–robot object handover pipeline. This review aims to provide researchers and developers with a guideline for designing human–robot object handover methods.

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.

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.

With the rise in the aging population, an increase in the number of semidisabled elderly individuals has been noted, leading to notable challenges in medical and healthcare, exacerbated by a shortage of nursing staff. This study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog environment. The investigation focuses on semantic segmentation of human features using deep learning methodologies. Initially, 3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human models. Subsequently, a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human regions. Finally, the network is refined by integrating the spatial feature extraction module and the channel attention module based on PointNet. The results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes, achieving an overall accuracy of 95.7%. This represents a 4.5% improvement compared with the PointNet network and a 2.5% enhancement over mean intersection over union. In conclusion, this study substantially augments the human feature segmentation capabilities, facilitating effective collaboration with bath scrubbing robots for caregiving tasks, thereby possessing significant engineering application value.