Efficient Motion Research Articles

Making motors work - potential applications in biocomputing and synthetic biology.

Biomolecular motors exhibit outstanding functions, including efficient motion and force generation, as well as autonomous operation. In this Essay, I discuss how biomolecular motors can be engineered to be used in artificial systems and what future applications such systems might have.

Bistatic Radar Cooperative Imaging Based on Complementary Random Waveform

The cooperative imaging of the bistatic radar is an important research topic for missile-borne radar detection. The existing missile-borne radar detection system is mainly based on the target plot information separately extracted by each radar for data level fusion, without considering the gain brought by the cooperative processing of the radar target echo signal. In this paper, a random frequency-hopping waveform is designed for the bistatic radar to achieve efficient motion compensation. A coherent processing algorithm for bistatic echo signals is designed to achieve band fusion and improve the signal quality and range resolution of the radar. Simulation and high-frequency electromagnetic calculation data were used to verify the effectiveness of the proposed method.

Coverage Motion Planning Based on 3D Model’s Curved Shape for Home Cleaning Robot

Research on the automated control of robots for wiping curved surfaces includes studies on cleaning contaminated areas and controlling the end-effector posture to apply a constant force along curved surfaces. However, such robots should also clean difficult-to-find contaminants such as dust and dirt. One of previous studies has considered motion generation as a generalized traveling salesman problem. In particular, a point cloud is acquired from an RGBD camera, and a large graph is created to represent the surface based on the point cloud. The system then finds an appropriate end-effector posture for each node and sets up multiple coordinate systems. Consequently, an efficient cleaning motion can be generated; however, path generation using the previous study is a highly time-consuming process. Therefore, in this study, a 3D model is used to generate cleaning actions more efficiently. A point cloud is then generated from the mesh data of the 3D model, based on which the surface is represented as a simple graph that can be solved as a traveling salesman problem, thereby reducing the computational time. The optimal end-effector posture is determined based on the shape of the surface and is set as the coordinate system for each node. Finally, experiments are conducted to compare the cleaning results with the previous study results, thereby verifying that results can be obtained in less than one-tenth the computational time required for the previous study results.

An efficient RRT-based motion planning algorithm for autonomous underwater vehicles under cylindrical sampling constraints

An efficient RRT-based motion planning algorithm for autonomous underwater vehicles under cylindrical sampling constraints

Design of Parallel Variable Stiffness Actuators

Direct-drive motors (DDMs) have been increasingly used for robot actuation because they provide high-fidelity torque control, but they typically have low torque density. Gearing can be used to increase the torque density of motors, but gearing decreases the power density of the actuator. Parallel elastic actuators (PEAs), composed of a spring attached in parallel to a motor, can increase both the torque and power density of the actuator without jeopardizing torque control fidelity. PEAs can also generate efficient oscillatory motion by applying the torque of the motor through resonant oscillations. However, conventional fixed stiffness springs used in PEAs only enable efficient oscillatory motion at a fixed resonant frequency defined by the stiffness of the spring. In this article, we present a parallel variable stiffness actuator (PVSA) consisting of a DDM connected in parallel to a variable stiffness spring. PVSAs retain the torque control bandwidth of DDMs and PEAs and can be used to amplify the torque and power of the motor over a range of oscillation frequencies. We present a compact design of a PVSA where a direct-drive motor, a high energy density composite spring, and a variable stiffness mechanism are arranged in a conventional cylindrical geometry, similar to a motor-gearbox assembly. We foresee the use of PVSAs in mobile robots and wearable devices, where energy efficient oscillatory motion at different frequencies, along with high torque and power density is indispensable.

Distributing Collaborative Multi-Robot Planning With Gaussian Belief Propagation

Precise coordinated planning over a forward time window enables safe and highly efficient motion when many robots must work together in tight spaces, but this would normally require centralised control of all devices which is difficult to scale. We demonstrate GBP Planning, a new purely distributed technique based on Gaussian Belief Propagation for multi-robot planning problems, formulated by a generic factor graph defining dynamics and collision constraints over a forward time window. In simulations, we show that our method allows high performance collaborative planning where robots are able to cross each other in busy, intricate scenarios. They maintain shorter, quicker and smoother trajectories than alternative distributed planning techniques even in cases of communication failure. We encourage the reader to view the accompanying video demonstration.

Clinical Evaluation of Scout Accelerated Motion Estimation and Reduction Technique for 3D MR Imaging in the Inpatient and Emergency Department Settings.

A scout accelerated motion estimation and reduction (SAMER) framework has been developed for efficient retrospective motion correction. The goal of this study was to perform an initial evaluation of SAMER in a series of clinical brain MR imaging examinations. Ninety-seven patients who underwent MR imaging in the inpatient and emergency department settings were included in the study. SAMER motion correction was retrospectively applied to an accelerated T1-weighted MPRAGE sequence that was included in brain MR imaging examinations performed with and without contrast. Two blinded neuroradiologists graded images with and without SAMER motion correction on a 5-tier motion severity scale (none = 1, minimal = 2, mild = 3, moderate = 4, severe = 5). The median SAMER reconstruction time was 1 minute 47 seconds. SAMER motion correction significantly improved overall motion grades across all examinations (P < .005). Motion artifacts were reduced in 28% of cases, unchanged in 64% of cases, and increased in 8% of cases. SAMER improved motion grades in 100% of moderate motion cases and 75% of severe motion cases. Sixty-nine percent of nondiagnostic motion cases (grades 4 and 5) were considered diagnostic after SAMER motion correction. For cases with minimal or no motion, SAMER had negligible impact on the overall motion grade. For cases with mild, moderate, and severe motion, SAMER improved the motion grade by an average of 0.3 (SD, 0.5), 1.1 (SD, 0.3), and 1.1 (SD, 0.8) grades, respectively. SAMER improved the diagnostic image quality of clinical brain MR imaging examinations with motion artifacts. The improvement was most pronounced for cases with moderate or severe motion.

Ant weight-lifting algorithm for motion estimation

Every video coding standard includes and requires motion estimation and compensation. The full search algorithm, which provides the best motion estimation, has a very high computation cost. Researchers have developed several algorithms to reduce the cost of computation. However, most of these algorithms become trapped in local minima during the search. Population-based evolutionary algorithms are widely used to develop a computationally efficient and cost-effective motion estimation strategy. The most recent effort used the Jaya algorithm to develop a motion estimation process that outperformed the state-of-the-art test zone search algorithm. In this study, a motion estimation algorithm based on the ant weight-lifting approach is proposed. Previously, the ant weight-lifting algorithm was used to solve a variety of problems, such as image segmentation, signal compression, and so on. The ant weight-lifting algorithm's computation cost was reduced by adopting a fitness estimation method that uses nearest-neighbor interpolation and an early termination strategy. Compared to Jaya algorithm-based motion estimation, the proposed algorithm executes up to 3% more quickly and exhibits up to 1.2 dB less distortion.

HDM-RRT: A Fast HD-Map-Guided Motion Planning Algorithm for Autonomous Driving in the Campus Environment

On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a motion planning algorithm for autonomous vehicles on campus, called HD-Map-guided rapidly-exploring random tree (HDM-RRT). In our algorithm, A collision risk map (CR-Map) that quantifies the collision risk coefficient on the road is combined with the Gaussian distribution for sampling to improve the efficiency of algorithm. Then, the node optimization strategy of the algorithm is deeply optimized through the prior information of the CR-Map to improve the convergence rate and solve the problem of poor stability in campus environments. Three experiments were designed to verify the efficiency and stability of our approach. The results show that the sampling efficiency of our algorithm is four times higher than that of the Gaussian distribution method. The average convergence rate of the proposed algorithm outperforms the RRT* algorithm and DT-RRT* algorithm. In terms of algorithm efficiency, the average computation time of the proposed algorithm is only 15.98 ms, which is much better than that of the three compared algorithms.

Efficient motion capture data recovery via relationship-aggregated graph network and temporal pattern reasoning.

Human motion capture (mocap) data is of crucial importance to the realistic character animation, and the missing optical marker problem caused by marker falling off or occlusions often limit its performance in real-world applications. Although great progress has been made in mocap data recovery, it is still a challenging task primarily due to the articulated complexity and long-term dependencies in movements. To tackle these concerns, this paper proposes an efficient mocap data recovery approach by using Relationship-aggregated Graph Network and Temporal Pattern Reasoning (RGN-TPR). The RGN is comprised of two tailored graph encoders, local graph encoder (LGE) and global graph encoder (GGE). By dividing the human skeletal structure into several parts, LGE encodes the high-level semantic node features and their semantic relationships in each local part, while the GGE aggregates the structural relationships between different parts for whole skeletal data representation. Further, TPR utilizes self-attention mechanism to exploit the intra-frame interactions, and employs temporal transformer to capture long-term dependencies, whereby the discriminative spatio-temporal features can be reasonably obtained for efficient motion recovery. Extensive experiments tested on public datasets qualitatively and quantitatively verify the superiorities of the proposed learning framework for mocap data recovery, and show its improved performance with the state-of-the-arts.

A Pose-Only Solution to Visual Reconstruction and Navigation.

Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment. Large-scale scenarios and computational robustness are great challenges facing the research community to achieve this goal. This paper raises a pose-only imaging geometry representation and algorithms that might help solve these challenges. The pose-only representation, equivalent to the classical multiple-view geometry, is discovered to be linearly related to camera global translations, which allows for efficient and robust camera motion estimation. As a result, the spatial feature coordinates can be analytically reconstructed and do not require nonlinear optimization. Comprehensive experiments demonstrate that the computational efficiency of recovering the scene and associated camera poses is significantly improved by 2-4 orders of magnitude.

Improved Template-Based RRT for Efficient Kinodynamic Motion Planning

Improved Template-Based RRT for Efficient Kinodynamic Motion Planning

Multilevel Spatial-Temporal Excited Graph Network for Skeleton-based Action Recognition.

The ability to capture joint connections in complicated motion is essential for skeleton-based action recognition. However, earlier approaches may not be able to fully explore this connection in either the spatial or temporal dimension due to fixed or single-level topological structures and insufficient temporal modeling. In this paper, we propose a novel multilevel spatial-temporal excited graph network (ML-STGNet) to address the above problems. In the spatial configuration, we decouple the learning of the human skeleton into general and individual graphs by designing a multilevel graph convolution (ML-GCN) network and a spatial data-driven excitation (SDE) module, respectively. ML-GCN leverages joint-level, part-level, and body-level graphs to comprehensively model the hierarchical relations of a human body. Based on this, SDE is further introduced to handle the diverse joint relations of different samples in a data-dependent way. This decoupling approach not only increases the flexibility of the model for graph construction but also enables the generality to adapt to various data samples. In the temporal configuration, we apply the concept of temporal difference to the human skeleton and design an efficient temporal motion excitation (TME) module to highlight the motion-sensitive features. Furthermore, a simplified multiscale temporal convolution (MS-TCN) network is introduced to enrich the expression ability of temporal features. Extensive experiments on the four popular datasets NTU-RGB+D, NTU-RGB+D 120, Kinetics Skeleton 400, and Toyota Smarthome demonstrate that ML-STGNet gains considerable improvements over the existing state of the art.

ISAR Imaging for Maneuvering Targets via Fast Rotation Parameter Estimation

In inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, traditional high-order phase compensation methods are usually limited by the heavy computational burden of parameter estimation, making it hard to apply in real-time cases. In this letter, an efficient ISAR motion compensation method is proposed based on fast parameter estimation. A novel analytic expression of the image entropy is derived with the help of a compensation matrix based on sinc function interpolation, which can eliminate the high-order phase term. Hence, the parameter estimation is transformed to an optimization problem where the gradient descent algorithm can be adopted to accelerate the computation speed. Compared with other recently proposed methods, our method is superior in high robustness and low computing cost. Experiments with simulated and real data have verified that without affecting the image quality, the computing time of our method can be reduced to about 1/4 of the traditional search algorithm.

The assessment and management of sesamoiditis: a focus group study of podiatrists in Aotearoa New Zealand

BackgroundSesamoiditis is a common inflammatory condition affecting the sesamoid bones at the plantar aspect of the first metatarsophalangeal joint (1MTPJ). However, there are currently no recommendations or clinical guidelines to support podiatrists in their assessment or management of sesamoiditis. The aim of this study was to explore the views of podiatrists in Aotearoa New Zealand on their approaches to the assessment and management of patients with sesamoiditis.MethodsThis qualitative study included focus group discussions with registered podiatrists. Focus groups took place online via Zoom and were guided by a detailed focus group question schedule. The questions were designed to encourage discussion around assessment approaches used in the diagnosis of sesamoiditis and the treatment tools used to manage patients with sesamoiditis. Focus groups were audio-recorded and transcribed verbatim. Reflexive thematic analysis was used to analyse the data.ResultsA total of 12 registered podiatrists participated in one of three focus groups. Four themes were constructed relating to the assessment of sesamoiditis: (1) obtaining a patient history; (2) recreating patient symptoms; (3) determining contributing biomechanical factors; and (4) ruling out differential diagnoses. Seven themes were constructed relating to the management of sesamoiditis: (1) consideration of patient factors; (2) patient education; (3) cushioning of the sesamoids to allow more comfortable weightbearing of the 1MTPJ; (4) pressure redistribution and offloading of the sesamoids; (5) immobilisation of the 1MTPJ and sesamoids; (6) facilitating efficient sagittal plane motion during gait; (7) referring to other health professionals to find different ways to treat or manage patient symptoms.ConclusionPodiatrists in Aotearoa New Zealand demonstrate an analytical approach in the assessment and management of patients with sesamoiditis based on their clinical experience and knowledge of lower limb anatomy. A range of assessment and management techniques are selected based on the practitioners personal preferences, as well as the patient’s social factors, symptomology, and lower limb biomechanics.

Intelligent Motion Planning for Collision Free Autonomous Docking of Satellite Emulation Platform Using Reinforcement Learning

A reinforcement learning (RL) enabled intelligent motion planning for collision-free autonomous docking manoeuvre explicitly designed for a robotic floating satellite emulation platform is presented in this article. The Twin Delayed Deep Deterministic Policy Gradient-based RL algorithm involving deep neural network architecture in the actor-critic framework is considered to obtain the collision-free safe docking policy. The RL agents have been trained to perform a resilient target acquisition, ensuring its terminal position and velocity requirements while enabling the capability to avoid both static and dynamic obstacles. The resulting optimal policy is implemented as a feedback control law to enable computationally efficient onboard reactive motion planning for autonomous safe docking of the robotic floating satellite platform in a complex dense debris environment. The efficacy of the proposed motion planning scheme is validated with numerous simulation studies, where it is depicted that the trained RL-based planner has the potential to address the target acquisition with a sufficient degree of accuracy in the presence of both static and dynamic obstacle scenarios.

Collision-free motion control with learning features for automated vehicles in roundabouts

Control design for safe and time-efficient motion of automated vehicles in roundabout scenarios poses various challenges, especially adaptation to the actual traffic scenario and coordination of the vehicles. This paper proposes the design of a hierarchical motion control with learning feature for roundabout scenarios. The control is designed on two levels, such as on cloud level and on vehicle level. The control on the cloud level is designed by using reinforcement learning (RL), with which the energy efficient motion of the vehicle is achieved. The vehicle level contains a robust controller and a supervisor, with which the collision avoidance of the vehicles is guaranteed. The proposed control on a Hardware-in-the-Loop environment with small-scaled indoor vehicles in augmented reality is implemented. The effectiveness of the control and the safe motion of the automated vehicles under multi-vehicle scenario are demonstrated. The provided scenario illustrates that safe, i.e., collision-free motion of all automated vehicles can be guaranteed.

Path-Analysis-Based Reinforcement Learning Algorithm for Imitation Filming

Imitation filming has been applied to autonomous filming by mimicking human operators. To imitate the operation of cameramen when filming multiple human actions, existing methods plan the camera motion through time series prediction or train multiple models to handle a particular style in a specific situation. As a result, these methods require various settings to adapt to different scenarios. In this work, we overcome such limitations and propose an end-to-end imitation learning framework for drone cinematography systems. The framework consists of two main components: (1) an efficient motion feature extraction module for generating a compact motion feature space, (2) a path-analysis-based reinforcement learning (PABRL) algorithm for imitating multiple filming styles from demonstrations and incorporating aesthetical features for improved perspective shots. Our PABRL method is based on the actorcritic network, which regards multiple human motion variables, camera translations, and image composition as inputs and then outputs an aesthetical filming strategy related to the subject motion. In addition, we propose an attention mechanism and a long-short-term rewarding function to enhance the motion feature space and the integrity of the generated trajectory, respectively. Extensive experimental results in simulated and real outdoor environments demonstrate that compared with state-of-the-art methods, our method can achieve 69.8% higher performance in terms of trajectory planning accuracy while successfully incorporating aesthetical features into the captured videos.

Optimal and Reactive Control for Agile Drone Flight in Cluttered Environments

We present a control framework for agile and reactive collision avoidance with quadrotor drones, applied to the state-based tier of the DodgeDrone Challenge, a simulation competition where the goal is to reach a finish line as fast as possible without collisions. The approach consists of an optimal control scheme with a Log-Sum-Exponential (LSE) obstacle avoidance formulation for efficient motion planning through cluttered environments with a high number of obstacles. The framework copes with a lower update frequency than required in classical Model Predictive Control by combining low-frequency smooth on-trajectory motion planning updates with computationally inexpensive high-frequency linear feedback control. This control structure is augmented with a monitoring function to allow for emergency reactive control with artificial repulsive potential fields in case of an optimal control failure to find a feasible solution in time. The approach is evaluated in extensive simulation runs, which show (1) the effectiveness of the total approach, (2) the increased added value of the LSE formulation for high numbers of obstacles, and (3) the trade-off between the execution time and the success rate. We show that our performance is competitive with the top five teams, even though the approach is, due to its model-based design, not specifically tailored to or trained for this particular challenge but is generally extendable towards other drone applications.

Motion Planning for Dynamic Scenario Vehicles in Autonomous-Driving Simulations

Scenario vehicles are an important part of the dynamic environment utilized in autonomous-driving simulations. They are required to meet the demands of traffic-scenario diversity and form a larger coverage scale in the road network. However, the current motion planning of scenario vehicles either adheres to the classical microscopic traffic-flow model or follows a predefined path; thus, interacting with the vehicle under test in a dynamic bidirectional fashion is difficult. This study researches a motion-planning method for a broader category of unmanned vehicles and proposes a motion-planning method for scenario vehicles based on Pontryagin’s minimal principle, used in optimal control theory and the closed-form solution of the minimum snap method. The study reclassifies actions received from the behavior layer according to the boundary conditions and final times and derives an analytical solution for each of them. The analytical solution is then experimentally verified. The proposed method not only accomplishes efficient motion planning but also exhibits variant driving styles, which provides a practical solution for the motion planning of scenario vehicles in simulations.