In this study, we investigated the effect of arm swinging with a flexible trunk on bipedal locomotion using a simulation model. The model imitates human arm swinging and flexible trunk twisting. The model showed that the center of pressure (CoP) oscillates sinusoidally in the left-right direction by asymmetrical arms swinging. We compared the stability of bipedal locomotion without arm swinging, with symmetrical arm swinging, and with asymmetrical arm swinging, by applying external force. The results showed that asymmetric arm swinging did not lead to fall over with a wider disturbance range and the CoP trajectory gradually became cyclic after the external force was applied.
Fisheye cameras' broad field of view makes them ideal for a variety of applications, such as robotic navigation, drone vision, and car surround-view systems. Fisheye eyeglasses' intrinsic nonlinear distortion, however, makes it difficult to accurately interpret images and analyze them later. This research proposes a Zynq System on Chip (SoC)-based accelerator to dynamically restore fisheye lens image distortion in real time. The proposed system improves the limitations of conventional field programmable gate array (FPGA) designs that require fixed parameters and leverages the Python Productivity for Zynq (PYNQ) framework to enable dynamic parameter modification without reprogramming. Experimental results show that the proposed system processes images with a resolution of 960x640 at a speed of 135.7 frames per second (fps), achieving more than 20 times faster performance compared to software-based solutions. Additionally, it can maintain real-time performance even at Full HD resolution. We also evaluated the accuracy by comparing the fisheye distortion correction of four interpolation techniques provided by OpenCV with the quality indicators Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). It is also flexible enough to allow the system to dynamically adapt to environmental changes and process images efficiently. This research provides a highly flexible, real-time fisheye distortion correction system for various industries such as robotics, automotive, and drones.
This paper presents the design and development of a novel, low-cost, fixed-base humanoid robot equipped with a torso, two arms, hands with five fingers, and a head. One of the biggest challenges in developing low-cost humanoid robots is identifying optimal ways to minimize manufacturing and production costs without compromising quality or performance. This requires creative engineering and design strategies and using cost-effective and readily available materials and components. The design prioritizes the use of fewer components, including a reduced number of sensors and actuators, to lower costs. Another approach involves manufacturing the robot's body parts using 3D printing technology, which reduces the costs associated with traditional manufacturing methods. As a result, it allows the robot to meet specific demands at any stage of the process economically. During the design process, careful consideration is given to selecting materials, kinematic structures, and mechanical components. Choosing suitable materials is crucial when designing a low-cost humanoid robot. Using inexpensive, strong, and lightweight materials is essential to keep the robot's cost down without compromising essential features. The robot's range of motion is dictated by its skeleton or kinematic structure. Subsequently, selecting mechanical parts, such as joints, actuators, and sensors, is critical in determining the robot's overall performance and agility. In the development process, building the physical body of a 6-DoF humanoid robot, integrating sensors for perception, and designing control and decision-making software are all necessary steps. When these components come together, they enable the creation of a robot capable of human-like movement and interaction. The ability of a humanoid robot to identify and interact with humans has significant real-world applications, especially in security roles where the robot can recognize individuals in public spaces. The humanoid robot is designed with 6 degrees of freedom (DoF) for its main body and an additional 5 DoF for each hand. In this design, the robot is equipped with an AI camera for facial recognition, enabling it to detect and identify human faces.
Investigated how bacteria self-propel in low Reynolds number liquid environments and modeled the swimming state. Through dimensional analysis, a centimeter-sized bionic robot, driven by a rubber band simulating a flagellar motor, was designed and fabricated. Kinematic experiments were conducted to test the swimming speed of the robot in liquids of different viscosities and to analyze the relationship between the swimming speed and the flagellar rotation speed. The experimental results show that the bio-inspired robot can mimic the continuous swimming motion of bacteria driven by elastic forces with a maximum speed of 25 mm/s in water. These results provide further insights into the study of micro bionic robots.
In contemporary manufacturing, efficient operation of robotic cells is critical and Job Shop Scheduling (JSS) faces challenges such as machine failures and shifting priorities. Deep Reinforcement Learning (DRL) addresses these challenges by enabling adaptive scheduling. DRL empowers robotic cells to dynamically optimize schedules which can increase resilience drastically. Through simulations and real-world use, DRL-based approaches outperform traditional methods, improving adaptability and efficiency. Integrating DRL into scheduling processes enables better navigation of uncertainties, enhancing productivity and competitiveness in modern manufacturing. This contribution focuses on these problems and provides a DRL approach for robotic cells to dynamically schedule production orders when facing random tool breakdowns. Using the epsilon-greedy policy, it allows the agent to explore the environment and choose different actions to generate rewards. Apart from reacting to random machine failures, the provided model enables collaborative robotic processes to further shorten the makespan and increase the utilization of industrial robots paired with machine tools.
