Biomimetics最新文献

Grasp planning is crucial for robots to perform precision grasping tasks, where determining the grasp points significantly impacts the performance of the robotic hand. Currently, the majority of grasp planning methods based on analytic approaches solve the problem by transforming it into a nonlinear constrained planning problem. This method often requires performing convex hull computations, which tend to have high computational complexity. This paper proposes a new algorithm for calculating multi-finger force-closure grasps of three-dimensional objects based on humanoid multi-fingered hands. Firstly, sufficient conditions for the multi-finger force-closure grasps of three-dimensional objects are derived from a point contact model with friction. These three-dimensional force-closure conditions are then transformed into two-dimensional plane conditions, leading to a simple algorithm for multi-finger force-closure determination. This method is purely based on geometric analysis, resulting in low computational demands and enabling the rapid assessment of force-closure grasps, which are beneficial for real-time applications. Finally, the algorithm is validated through two case studies, demonstrating its feasibility and effectiveness.

Type 1 diabetes mellitus (T1DM) is a chronic condition primarily managed with insulin replacement, leading to significant treatment costs. Complications include vasculopathy, cardiovascular diseases, nephropathy, neuropathy, and reticulopathy. Pancreatic islet transplantation is an option but its success does not depend solely on adequate vascularization. The main limitations to clinical islet transplantation are the scarcity of human pancreas, the need for immunosuppression, and the inadequacy of the islet isolation process. Despite extensive research, T1DM remains a major global health issue. In 2015, diabetes affected approximately 415 million people, with projected expenditures of USD 1.7 trillion by 2030. Pancreas transplantation faces challenges due to limited organ availability and complex vascularization. T1DM is caused by the autoimmune destruction of insulin-producing pancreatic cells. Advances in biomaterials, particularly the extracellular matrix (ECM), show promise in tissue reconstruction and transplantation, offering structural and regulatory functions critical for cell migration, differentiation, and adhesion. Tissue engineering aims to create bioartificial pancreases integrating insulin-producing cells and suitable frameworks. This involves decellularization and recellularization techniques to develop biological scaffolds. The challenges include replicating the pancreas's intricate architecture and maintaining cell viability and functionality. Emerging technologies, such as 3D printing and advanced biomaterials, have shown potential in constructing bioartificial organs. ECM components, including collagens and glycoproteins, play essential roles in cell adhesion, migration, and differentiation. Clinical applications focus on developing functional scaffolds for transplantation, with ongoing research addressing immunological responses and long-term efficacy. Pancreatic bioengineering represents a promising avenue for T1DM treatment, requiring further research to ensure successful implementation.

A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.

Soft robots have often been proposed for medical applications, creating human-friendly machines, and dedicated subject operation, and the pneumatic actuator is a representative example of such a robot. Plants, with their hingeless architecture, can take advantage of morphology to achieve a predetermined deformation. To improve the modes of motion, two pneumatic actuators that mimic the principles of the plants (the birds-of-paradise plant and the waterwheel plant) were designed, simulated, and tested using physical models in this study. The most common deformation pattern of the pneumatic actuator, bending deformation, was utilized and hingeless structures based on the plants were fabricated for a more complex motion of the lobes. Here, an ABP (actuator inspired by the birds-of-paradise plant) could bend its midrib downward to open the lobes, but an AWP (actuator inspired by the waterwheel plant) could bend its midrib upward to open the two lobes. In both the computational and physical models, the associated movements of the midrib and lobes could be observed and measured. As it lacks multiple parts that have to be assembled using joints, the actuator would be simpler to fabricate, have a variety of deformation modes, and be applicable in more fields.

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective optimization issues in recent years. Their study has garnered a lot of attention since multi-objective optimization problems have a hard high-dimensional goal space. The black-winged kite optimization algorithm still suffers from the imbalance between global search and local development capabilities, and it is prone to local optimization even though it combines Cauchy mutation to enhance the algorithm's optimization ability. The heuristic optimization algorithm of the black-winged kite fused with osprey (OCBKA), which initializes the population by logistic chaotic mapping and fuses the osprey optimization algorithm to improve the search performance of the algorithm, is proposed as a means of enhancing the search ability of the black-winged kite algorithm (BKA). By using numerical comparisons between the CEC2005 and CEC2021 benchmark functions, along with other swarm intelligence optimization methods and the solutions to three engineering optimization problems, the upgraded strategy's efficacy is confirmed. Based on numerical experiment findings, the revised OCBKA is very competitive because it can handle complicated engineering optimization problems with a high convergence accuracy and quick convergence time when compared to other comparable algorithms.

The nutcracker optimizer algorithm (NOA) is a metaheuristic method proposed in recent years. This algorithm simulates the behavior of nutcrackers searching and storing food in nature to solve the optimization problem. However, the traditional NOA struggles to balance global exploration and local exploitation effectively, making it prone to getting trapped in local optima when solving complex problems. To address these shortcomings, this study proposes a reinforcement learning-based bi-population nutcracker optimizer algorithm called RLNOA. In the RLNOA, a bi-population mechanism is introduced to better balance global and local optimization capabilities. At the beginning of each iteration, the raw population is divided into an exploration sub-population and an exploitation sub-population based on the fitness value of each individual. The exploration sub-population is composed of individuals with poor fitness values. An improved foraging strategy based on random opposition-based learning is designed as the update method for the exploration sub-population to enhance diversity. Meanwhile, Q-learning serves as an adaptive selector for exploitation strategies, enabling optimal adjustment of the exploitation sub-population's behavior across various problems. The performance of the RLNOA is evaluated using the CEC-2014, CEC-2017, and CEC-2020 benchmark function sets, and it is compared against nine state-of-the-art metaheuristic algorithms. Experimental results demonstrate the superior performance of the proposed algorithm.

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierarchical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace were delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm was employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trained foot-points to facilitate robot movement along predefined paths. Experimental results demonstrated the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieved a 100% success rate, enhanced average overall scores by 6.27%, reduced average stride lengths by 39.04%, and attained a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.

In response to the sudden violation of pedestrians crossing the road, intelligent vehicles take into account factors such as the road conditions in the accident zone, traffic rules, and surrounding vehicles' driving status to make emergency evasive decisions. Thus, the collision simulation models for pedestrians and three types of vehicles, i.e., sedans, Sport Utility Vehicles (SUVs), and Multi-Purpose Vehicle (MPVs), are built to investigate the impact of vehicle types, vehicle steering angles, collision speeds, collision positions, and pedestrian orientations on head injuries of pedestrians. The results indicate that the Head Injury Criterion (HIC) value of the head increases with the increase in collision speed. Regarding the steering angles, when a vehicle's steering direction aligns with a pedestrian's position, the pedestrian remains on top of the vehicle's hood for a longer period and moves together with the vehicle after the collision. This effectively reduces head injuries to pedestrians. However, when the vehicle's steering direction is opposite to the pedestrian's position, the pedestrian directly collides with the ground, resulting in higher head injuries. Among them, MPVs cause the most severe injuries, followed by SUVs, and sedans have the least impact. Overall, intelligent vehicles have great potential to reduce head injuries of pedestrians in the event of sudden pedestrian-vehicle collisions by combining with Automatic Emergency Steering (AES) measures. In the future, efforts need to be made to establish an optimized steering strategy and optimize the handling of situations where steering is ineffective or even harmful.

This study systematically investigates the effects of the coaxial nozzle's inner nozzle diameter on the strength and gelation of filaments produced via extrusion-based 3D printing with in situ ionic crosslinking. In this system, bioink (sodium alginate solution) was extruded through the outer nozzle, and the ionic crosslinking solution (calcium chloride solution) was extruded through the inner nozzle. The outer nozzle diameter was fixed at 2.16 mm, and the inner nozzle diameter was varied among 1.19, 0.84, and 0.584 mm. The results indicate that, as the inner nozzle diameter decreased, filament strength decreased, and filament gelation became poorer. These findings highlight the importance of optimizing inner nozzle diameter for improved filament strength and gelation in extrusion-based 3D printing with in situ ionic crosslinking.

In practical applications, integrating three-dimensional models of bionic devices with simulation systems can predict their behavior and performance under various operating conditions, providing a basis for subsequent engineering optimization and improvements. This study proposes a framework for characterizing three-dimensional models of objects, focusing on extracting 3D structures and generating high-quality 3D models. The core concept involves obtaining the density output of the model from multiple images to enable adaptive boundary surface detection. The framework employs a hierarchical octree structure to partition the 3D space based on surface and geometric complexity. This approach includes recursive encoding and decoding of the octree structure and surface geometry, ultimately leading to the reconstruction of the 3D model. The framework has been validated through a series of experiments, yielding positive results.