Hybrid Motion Research Articles

The Effect of Combining 2D and 3D Moving Particles on Hybrid Motion Graphics

Objectives: This study aimed to evaluate the level of complexity involved in integrating 2D and 3D techniques and evaluating the utility of this approach for designers in generating innovative motion designs,. It also aims to examine the design methodology employed in the creation of 2D and 3D motion graphics. Methods: The study used the descriptive- analytical approach. It also used the quantitative approach based on understanding the effect of combining 2D and 3D moving particles in designing new and innovative designs and determining the relevant terminology and concepts to create 2D and 3D moving graphics. A questionnaire was also distributed to 200 professional and specialized motion graphic designers to test the possibility of combining contemporary designs together. Results: The study’s quantitative results showed a positive and significant reality for employing the combination of the 2D and 3D elements in motion graphics with a high likelihood of implementation by the designer. The study results also showed a high average in producing cutting-edge styles via combining 2D and 3D elements in motion graphics. Conclusion: The study highlights the importance of conducting an accurate evaluation of the various variables to achieve the best results during the process of designing hybrid motion graphics which combine 2D and 3D particles. The study also states that the success of the combination process depends on the effectiveness of the visual style of the hybrid graphic designs and the effects, structure and motion in creating integrated images that effectively transmit the emotional story behind those designs.

Enhanced Power Extraction via Hybrid Pitching Motion in an Oscillating Wing Energy Harvester with Leading Flap

This study applied a hybrid pitching motion for an oscillating wing with a leading flap aimed at enhancing energy extraction efficiency. In the first half of the cycle, the hybrid pitching motion begins with a non-sinusoidal pitching motion for 0.0 ≤ t/T ≤ 0.25, transitioning to a sinusoidal pitching motion for 0.25 < t/T ≤ 0.50. The latter half of the motion mirrors the first one but moves toward the reverse direction. Hybrid motions combine the benefits of non-sinusoidal and sinusoidal pitching motions, enhancing the optimization of pitch angle variation. The findings show that hybrid motions for the wing fitted with an attached leading flap outperform both the single plate and the wing with an attached flap using sinusoidal pitching motion. The simulation was conducted with flap lengths ranging from 30% to 45% of the chord length and examined maximum pitching angles of the wing and the attached leading flap between 80° to 95° and 25° to 60°, respectively. By setting the pitch angles of the wing and leading flap to 85° and 45°, respectively, with the wing comprising 65% of the total length and the leading flap 35%, the proposed hybrid pitching motion with the leading flap generates a maximum power output of 1.276 that surpasses that of a sinusoidal pitching motion of 0.963 on an oscillating flat plate by 32.50%. This combination of hybrid pitching motion and a wing flap configuration is effective in improving the performance.

Representation Learning and Reinforcement Learning for Dynamic Complex Motion Planning System.

Indoor motion planning challenges researchers because of the high density and unpredictability of moving obstacles. Classical algorithms work well in the case of static obstacles but suffer from collisions in the case of dense and dynamic obstacles. Recent reinforcement learning (RL) algorithms provide safe solutions for multiagent robotic motion planning systems. However, these algorithms face challenges in convergence: slow convergence speed and suboptimal converged result. Inspired by RL and representation learning, we introduced the ALN-DSAC: a hybrid motion planning algorithm where attention-based long short-term memory (LSTM) and novel data replay combine with discrete soft actor-critic (SAC). First, we implemented a discrete SAC algorithm, which is the SAC in the setting of discrete action space. Second, we optimized existing distance-based LSTM encoding by attention-based encoding to improve the data quality. Third, we introduced a novel data replay method by combining the online learning and offline learning to improve the efficacy of data replay. The convergence of our ALN-DSAC outperforms that of the trainable state of the arts. Evaluations demonstrate that our algorithm achieves nearly 100% success with less time to reach the goal in motion planning tasks when compared to the state of the arts. The test code is available at https://github.com/CHUENGMINCHOU/ALN-DSAC.

A hybrid task-constrained motion planning for collaborative robots in intelligent remanufacturing

Industrial manipulators have extensively collaborated with human operators to execute tasks, e.g., disassembly of end-of-use products, in intelligent remanufacturing. A safety task execution requires real-time path planning for the manipulator’s end-effector to autonomously avoid human operators. This is even more challenging when the end-effector needs to follow a planned path while avoiding the collision between the manipulator body and human operators, which is usually computationally expensive and limits real-time application. This paper proposes an efficient hybrid motion planning algorithm that consists of A∗ algorithm and an online manipulator reconfiguration mechanism (OMRM) to tackle such challenges in task and configuration spaces, respectively. A∗ algorithm is first leveraged to plan the shortest collision-free path of the end-effector in task space. When the manipulator body is risky to the human operator, our OMRM then selects an alternative joint configuration with minimum reconfiguration effort from a database to assist the manipulator to follow the planned path and avoid the human operator simultaneously. The database of manipulator reconfiguration establishes the relationship between the task and configuration space offline using forward kinematics, and is able to provide multiple reconfiguration candidates for a desired end-effector’s position. The proposed new hybrid algorithm plans safe manipulator motion during the whole task execution. Extensive numerical and experimental studies, as well as comparison studies between the proposed one and the state-of-the-art ones, have been conducted to validate the proposed motion planning algorithm.

Hybrid passive vibration control of lightweight manipulators

Passive vibration control methods in the literature are known to be highly sensitive to dynamic system parameters and are generally applied one by one. Therefore, their effectiveness in suppressing vibration control is limited. In this work, hybrid passive control (HPC) by integrating the Posicast control (PC) and the motion parameters-based control (MPC) is developed to suppress the during-motion vibrations (DMV) and residual vibrations (RV) of a single-link lightweight manipulator with a payload. PC is designed as a three-step cycle using the first natural frequency and damping ratio of the manipulator. MPC is designed by determining the time parameters of the motion profiles based on the first natural frequency of the manipulator. HPC simulations are performed on MATLAB, creating a Simscape model of the manipulator. Then, the proposed control method is tested in experiments. The prepared hybrid motion inputs actuate the servomotor while the displacements are measured, using a laser sensor at the tip of the manipulator. DMV and RV responses and their RMS values reveal that suppressing the vibration amplitudes efficiently. MPC is mostly effective in eliminating RV, whereas PC is sensitive to DMV as well. HPC combines these methods by eliminating their disadvantages and highlighting their advantages. Moreover, it is also successful in cases where the deceleration time of the trapezoidal input signal is single times half of the natural period, unlike MPC.

Numerical simulation of stabilisation of floating wind with submerged hydrofoil

This research focuses on the optimal design and method of attaching a submerged hydrofoil to an offshore platform to enhance stabilisation. The flapping hydrofoil, exhibiting a hybrid motion combining heave and pitch, is engineered to convert incoming wave energy. It generates a distinctive wake that effectively counteracts incoming waves, thereby reducing wave impact. In this study, a NACA0030-type hydrofoil was strategically positioned between two columns of the platform model. Comprehensive analyses were conducted to evaluate the free-floating platform’s response to regular waves, with a focus on the attached hydrofoil. The results indicate that the hydrofoil significantly reduces the surge motion and drifting speed of the platform, affirming its effectiveness in enhancing stabilisation.

Self-Powered Hybrid Motion and Health Sensing System Based on Triboelectric Nanogenerators.

Triboelectric nanogenerator (TENG) represents an effective approach for the conversion of mechanical energy into electrical energy and has been explored to combine multiple technologies in past years. Self-powered sensors are not only free from the constraints of mechanical energy in the environment but also capable of efficiently harvesting ambient energy to sustain continuous operation. In this review, the remarkable development of TENG-based human body sensing achieved in recent years is presented, with a specific focus on human health sensing solutions, such as body motion and physiological signal detection. The movements originating from different parts of the body, such as body, touch, sound, and eyes, are systematically classified, and a thorough review of sensor structures and materials is conducted. Physiological signal sensors are categorized into non-implantable and implantable biomedical sensors for discussion. Suggestions for future applications of TENG-based biomedical sensors are also indicated, highlighting the associated challenges.

Unmanned aerial vehicle path planning with hybrid motion algorithm for obstacle avoidance

Unmanned aerial vehicles (UAVs) are gaining prominence in autonomously navigating diverse terrains, requiring the capability to establish collision-free trajectories and adapt them on-the-fly to changing environments. This study's central contribution lies in devising an optimized motion planning framework tailored for UAVs operating amidst dynamic scenarios. This framework comprises two integral components: an optimized motion planner and a dynamic scenario generator. To enhance trajectory optimization, the optimized motion planner enhances the Rapidly-exploring Random Tree (RRTX) method with a Covariant Hamiltonian Optimization for Motion Planning (CHOMP) algorithm-based optimizer. Addressing the challenges posed by dynamic environments characterized by abrupt appearance, disappearance, or shifting of constraints, the motion planner adeptly identifies environmental changes and computes collision-free paths during UAV navigation. The dynamic scenario generator integrates a UAV simulator and barrier information, effectively emulating UAV obstacles and intended flight patterns within a Unity-based simulation environment. The simulator employed is Flight Mare, a versatile quadrotor simulator that employs Unity's graphics engine and a physics engine for dynamic simulations. Through comprehensive simulations, the proposed approach is validated, demonstrating its efficacy in enabling UAVs to autonomously navigate dynamic environments while avoiding obstacles successfully.

Multiagent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots.

In this article, a novel hybrid multirobot motion planner that can be applied under no explicit communication and local observable conditions is presented. The planner is model-free and can realize the end-to-end mapping of multirobot state and observation information to final smooth and continuous trajectories. The planner is a front-end and back-end separated architecture. The design of the front-end collaborative waypoints searching module is based on the multiagent soft actor-critic (MASAC) algorithm under the centralized training with decentralized execution (CTDE) diagram. The design of the back-end trajectory optimization module is based on the minimal snap method with safety zone constraints. This module can output the final dynamic-feasible and executable trajectories. Finally, multigroup experimental results verify the effectiveness of the proposed motion planner.

Safe Trajectory Generation for Wheel-Leg Hybrid Mechanism Using Discrete Mechanics and Optimal Control

Abstract The wheel-legged robot inherits the merit of both the wheeled robot and the legged robot, which can not only adapt to the complex terrain but also maintain the driving efficiency on the flat road. This article presents an optimization-based approach that leverage ideas from computational geometric mechanics to generate safe and high-quality wheel-leg hybrid motions among obstacles. The formulation of the proposed motion optimization problem incorporates the Lagrange–d’Alembert principle as the robot’s dynamic constraints and an efficient closed-form formulation of collision-free constraints. By discretizing the variational mechanics principle directly, rather than its corresponding forced Euler–Lagrange equation, the continuous trajectory optimization problem is transformed into a nonlinear programming (NLP) problem. Numerical simulations and several real-world experiments are conducted on a wheel-legged robot to demonstrate the effectiveness of the proposed trajectory generation approach.

Kinematic modeling and hybrid motion planning for wheeled-legged rovers to traverse challenging terrains

AbstractThis paper presents a kinematics modeling and hybrid motion planning framework for wheeled-legged rovers. It is a unified solution for wheeled-legged rovers to traverse multiple challenging terrains using hybrid locomotion. A kinematic model is first established to describe the rover’s motions. Then, a hybrid motion planning framework is proposed to determine the rover’s gait patterns and parameterize the legs’ and the body’s trajectories. Furthermore, an optimization algorithm based on B-spline is utilized to minimize the motors’ energy dissipation and generate smooth trajectories. The wheeled and legged hybridization allows the rover for faster locomotion while maintaining high stability. Besides, it also improves the rover’s ability to overcome obstacles. Prototype experiments are carried out in more complex environments to verify the rover’s flexibility and maneuverability to traverse irregular terrains. The proposed algorithm reduces the swing amplitude by 83.3% compared to purely legged locomotion.

A smart walker based on a hybrid motion model and machine learning method

Due to the degeneration of musculoskeletal structure and strength, the elderly population is facing significant mobility challenges. Walkers are widely used for mobility-impaired people. In this research, a smart walker has been developed by using a hybrid motion model (HMM) and a machine learning model based on the vertical interaction force to match the velocity between the walker and its user, which can reduce the horizontal interaction force thus to help lower the operational effort. It is equipped with only force sensors on the handlebars as a human-robot interface. The human motion is modeled by using the HMM in which an inverted pendulum model is used in the single support phase and a constant velocity model is used in the double support phase. A novel gait phase detector is built based on a machine learning method to correlate the force information and the walking gait phases. Experiments with three subjects are carried out to verify the effectiveness of the gait phase detection method and the hybrid motion model. The results demonstrate that the walker can accurately identify gait phases with more than 90 % accuracy and the horizontal interaction force is reduced to around 50 % of that under the constant velocity model and power-off condition when using the HMM during walking.

RPP-Net: Rigid Constrained Point Cloud Prediction Network

Forecasting the future environment is an essential and fundamental capability of autonomous driving systems. In contrast to the widely studied video prediction, only a few literatures have explored LiDAR point cloud prediction. To achieve future point cloud generation, most existing methods are based on free-form 3D scene flow prediction. However, these simple scene flow prediction-based methods may cause distortions since the motions of 3D scenes can be seen as a combination of rigid (static background) and flow (dynamic foreground) motions. To address this issue, we propose a simple but effective rigid constrained point cloud prediction network named RPP-Net. The key component of the proposed method is the hybrid motion decoder, which generates motion masks and combines flow motions and rigid motions to produce hybrid motions without additional annotations and sensors. Besides, to reduce running time, we provide a hierarchical cell, which uses a feature encoder to extract deep features and an RPP-RNN module to capture temporal correlations across frames. To evaluate the effectiveness of our RPP-Net, we have conducted extensive experiments on both KITTI dataset and Argoverse dataset and the results show that our RPP-Net significantly outperforms existing methods and achieves a new state-of-the-art.

A Hybrid Motion Planning Algorithm for Multi-Mobile Robot Formation Planning

This paper addresses the problem of relative position-based formation planning for a leader–follower multi-robot setup, where the robots adjust the formation parameters, such as size and three-dimensional orientation, to avoid collisions and progress toward their goal. Specifically, we develop a virtual sub-target-based obstacle avoidance method, which involves a transitional virtual sub-target that guides the robots to avoid obstacles according to obstacle information, target, and boundary. Moreover, we develop a changing formation strategy to determine the necessity to avoid collisions and a priority-based model to determine which robots move, thus dynamically adjusting the relative distance between the followers and the leader. The backstepping-based sliding motion controller guarantees that the trajectory and velocity tracking errors converge to zero. The proposed robot navigation method can be employed in various environments and types of obstacles, allowing for a formation change. Furthermore, it is efficient and scalable under various numbers of robots. The approach is experimentally verified using three real mobile robots and up to five mobile robots in simulated scenarios.

Hybrid motion control for multiple mobile robot systems

Hybrid motion control for multiple mobile robot systems

Modular Soft Robotic Actuators from Flexible Perforated Sheets

Soft robotic actuators can be designed to achieve complex and tailored motions while simultaneously leveraging their compliance to interact with complex and often delicate environments. Mechanical metamaterials reveal a route to customizable deformations, force exertion, and mechanical energy efficiency attainable by careful arrangement of local geometric features. Herein, modular soft robotic actuators are developed from soft elastomers and flexible thermoplastic sheets of various unit cell designs. The efforts are focused on center‐symmetric perforated sheets, which are formed into flexible cylindrical skins that surround the soft inflatable actuators. The results demonstrate the influence of perforation geometry on the spatial stiffness of the reinforcement structure and the proposed actuators’ response through several investigations. It is demonstrated that the free‐boundary displacement, maximal force exertion, and mechanical energy efficiency of extensile actuators are dependent on a change of deformation mode in the mesostructure. The spatial stiffness concept is extended to develop soft robotic actuators that can bend, twist, and perform hybrid motions, such as simultaneous bending and twisting. Multisegment soft robotic arms are also developed from the aforementioned actuators. Investigations in this study provide a step toward the development of highly customizable and programmable soft robotic actuators for various applications.

Exploring the Potential of Virtual Reality in Table Tennis Training through Hybrid Motion Capture

Exploring the Potential of Virtual Reality in Table Tennis Training through Hybrid Motion Capture

Bioinspired Soft Wrist Based on Multicable Jamming With Hybrid Motion and Stiffness Control for Dexterous Manipulation

Dexterous manipulation is important for modern logistics automation. Industrial robots remain inferior to human labor in this area, especially in handling unknown objects in unstructured scenarios. It is observed that the human wrist contributes significantly to the dexterous manipulation capability by agilely changing the hand orientation to approach the target object and adjusting wrist stiffness to adapt to dynamic interaction. Inspired by the human wrist, we propose a novel soft wrist to achieve hybrid motion/stiffness control in a compact and lightweight structure. The dexterous motion and stiffness adjustment are simultaneously enabled by a novel multicable jamming mechanism, which is achieved by a precalibration of the relationship between stiffness with robot length and bending angle to adapt to both compliant and forceful tasks. Dedicated experiments were performed to validate its motion dexterity and variable stiffness property. A soft grasping system was developed to demonstrate the manipulation capability of the soft wrist by tasking it with automatic packaging, which is challenging for robots. Overall, the proposed soft wrist demonstrates a promising solution to enhance robotic manipulation capability to a human comparable level.

Circadian aspects of mobility-related behavior in patients with dementia: An exploratory analysis in acute geriatric psychiatry.

Analyzing patients' mobility-related behavior may improve the assessment of motor behavior in dementia, however, few studies addressed circadian aspects of mobility. This cross-sectional explorative study analyzed the timing of peak mobility-related behavior, the prevalence of mobility-related sundowning and nocturnal mobility-related behavior and associated clinical characteristics in acute geriatric psychiatry. Mobility-related behavior of 73 patients (M: 81years) was measured over 48h using lower-back worn hybrid motion sensors. We derived the start of the 30-min period with peak gait activity (highest number of steps) for each day and the number of nocturnal steps taken from 10PM to 7AM. Professional caregiver ratings of the patients' motor behavior were conducted within the Neuropsychiatric Inventory (NPI). The mean start time of peak gait activity was 2:37PM, but large variations in timing were found (range: 3:25AM-9:30PM). Twenty-five patients (34%) were identified as "sundowners". Nocturnal mobility-related behavior was measured in 35 patients (53%), whereas professional caregivers assessed night-time disorders in only 19 patients (26%). Clinical characteristics of "sundowners" were not significantly different from other patients, except for lower doses of antipsychotics as compared to non-sundowners (M:1.6mg/day; p=0.015). The number of nocturnal steps was significantly associated to corresponding NPI ratings (Spearman's rho=0.4; p<0.001). Analyzing the timing of peak gait activity and nocturnal step-count seem to provide clinical applicable information on the circadian aspects of mobility-related behavior in acute geriatric psychiatry. Even though the clinical validity needs to be evaluated, objective information on the individual circadian aspects of mobility-related behavior could help to personalize treatment with benefits for patients and caregivers.

A worm-inspired robot based on origami structures driven by the magnetic field

With the rapid development of origami technology, worm-inspired origami robots have attracted tremendous interest owing to their colourful locomotion behaviours, such as creeping, rolling, climbing and obstacle crossing. In the present study, we aim to engineer a worm-inspired robot based on knitting process with paper, which could realize complicated functions associated large deformation and exquisite locomotion patterns. At first we fabricate the backbone of the robot by using the paper-knitting technique. The experiment shows that the backbone of the robot can endure significant deformation during the tension, compression and bending process, and this feature ensures it can achieve the desired targets of motion. Next, the magnetic forces and torques under the actuation of permanent magnets are analysed, which are just the driving forces of the robot. We then consider three formats of motion on the robot, i.e. the inchworm motion, the Omega motion, and the hybrid motion. Typical examples for the robot fulfil desired tasks are given, including sweeping obstacles, climbing the wall and delivering cargoes. Detailed theoretical analyses and numerical simulations are performed to illustrate these experimental phenomena. The results show that the developed origami robot is equipped with such characteristics as lightweight and great flexibility, which is sufficiently robust in various environments. These promising performances shed new light on design and fabrication of bio-inspired robots with good intelligence.