Problem Of Locomotion Research Articles

Efficient Locomotion for Space Robots Inspired by the Flying Snake

Robots are becoming an integral part of space facilities construction and maintenance, and may need to move to and from different work locations. Robotic arms that are widely employed, which are mounted on fixed bases, have difficulty coping with increasingly complex missions. The challenge discussed in this paper is the problem of the efficient locomotion of robotic systems. Inspired by the gliding motion of a flying snake launched from a tree and combined with the weightlessness of the space environment, we design similar motions for the robot, including the following three steps. First, the robot folds its body like a snake and initiates flight by accelerating the global center of mass (CM), focusing on the movement direction and generating suitable momentum. Then, during the flight (free-floating) phase, the joint motions are planned using a nonlinear optimization technique, considering the nonholonomic constraints introduced by the momentum conservation and the system states at the initial and final states of the floating. Meanwhile, the difficulties caused by long-distance flights are addressed to reduce the motion computational cost and energy consumption by introducing the phase plane analysis method. Finally, the landing motion is designed to avoid rigid collisions and rollover on the radial plane. The numerical simulations illustrate that the three phases of maneuvers are smooth and continuous, which can help the space robots efficiently traverse the environment.

An Untethered Soft-Swallowing Robot with Enhanced Heat Resistance, Damage Tolerance, and Impact Mitigation

Soft robotics focuses on addressing the locomotion problem in unstructured environments and the manipulation problem of non-cooperative objects, which inevitably leads to soft robots encountering multiple uncertainties and damages. Therefore, improving the robustness of soft robots in hostile environmental conditions has always been a challenge. Existing methods usually improve this robustness through damage isolation, material elasticity, and self-healing mechanisms. In contrast to existing methods, this paper proposes a method to improve the robustness of an untethered soft-swallowing robot based on the physical properties of fluids, such as the high specific heat capacity of water, the viscosity of soft glue, and the shear thickening of non-Newtonian fluids. Based on this method, we developed a soft-swallowing robot with enhanced heat resistance, damage tolerance, and impact mitigation capability by only replacing its fluid working medium. Experiments show that the developed soft-swallowing robot can withstand high temperatures above 600 °C, maintain high performance even after enduring hundreds of damages, and protect grasped object from more than 90% of external impacts. This principle extends beyond the three fluids used in this study. Other fluids, such as magnetic fluid, can increase adhesion to metal materials, whereas oily fluids can reduce frictional resistance between soft structures. Additionally, other solid materials with elasticity and compliance can serve as alternative working mediums for the soft-swallowing robot. This work contributes an effective method for fluid-dependent soft robotic systems to resist the damage from uncertain factors in harsh environments.

An elementary approach based on variational inequalities for modeling a friction-based locomotion problem

We propose an elementary proof based on a penalization technique to show the existence and uniqueness of the solution to a system of variational inequalities modeling the friction-based motion of a two-body crawling system. Here for each body, the static and dynamic friction coefficients are equal.

Learning to Walk with Adaptive Feet

In recent years, tasks regarding autonomous mobility favoredthe use of legged robots rather than wheeled ones thanks to their higher mobility on rough and uneven terrains. This comes at the cost of more complex motion planners and controllers to ensure robot stability and balance. However, in the case of quadrupedal robots, balancing is simpler than it is for bipeds thanks to their larger support polygons. Until a few years ago, most scientists and engineers addressed the quadrupedal locomotion problem with model-based approaches, which require a great deal of modeling expertise. A new trend is the use of data-driven methods, which seem to be quite promising and have shown great results. These methods do not require any modeling effort, but they suffer from computational limitations dictated by the hardware resources used. However, only the design phase of these algorithms requires large computing resources (controller training); their execution in the operational phase (deployment), takes place in real time on common processors. Moreover, adaptive feet capable of sensing terrain profile information have been designed and have shown great performance. Still, no dynamic locomotion control method has been specifically designed to leverage the advantages and supplementary information provided by this type of adaptive feet. In this work, we investigate the use and evaluate the performance of different end-to-end control policies trained via reinforcement learning algorithms specifically designed and trained to work on quadrupedal robots equipped with passive adaptive feet for their dynamic locomotion control over a diverse set of terrains. We examine how the addition of the haptic perception of the terrain affects the locomotion performance.

Gait patterns during overground and virtual omnidirectional treadmill walking

BackgroundOmnidirectional treadmills (ODTs) offer a promising solution to the virtual reality (VR) locomotion problem, which describes the mismatch between visual and somatosensory information and contributes to VR sickness. However, little is known about how walking on ODTs impacts the biomechanics of gait. This project aimed to compare overground and ODT walking and turning in healthy young adults.MethodsFifteen young adults completed forward walk, 180° turn, and 360° turn tasks under three conditions: (1) overground, (2) on the Infinadeck ODT in a virtual environment without a handrail, and (3) on the ODT with a handrail. Kinematic data for all walking trials were gathered using 3D optical motion capture.ResultsOverall, gait speed was slower during ODT walking than overground. When controlling for gait speed, ODT walking resulted in shorter steps and greater variability in step length. There were no significant differences in other spatiotemporal metrics between ODT and overground walking. Turning on the ODT required more steps and slower rotational speeds than overground turns. The addition of the stability handrail to the ODT resulted in decreased gait variability relative to the ODT gait without the handrail.ConclusionWalking on an ODT resembles natural gait patterns apart from slower gait speed and shorter step length. Slower walking and shorter step length are likely due to the novelty of physically navigating a virtual environment which may result in a more conservative approach to gait. Future work will evaluate how older adults and those with neurological disease respond to ODT walking.

A continuum mechanical model of cell motion driven by a biphasic traction stress.

The aim of this paper is to place the cell locomotion problem within the general framework of classical continuum mechanics, and while doing so, to account for the deformation of the actin network in the cytoskeleton; the myosin activity on the lamellum including its effect on depolymerization at the trailing edge; model the stress-dependent driving forces and kinetic laws controlling polymerization at the leading edge, depolymerization at the trailing edge and ATP hydrolysis consistently with the dissipation inequality; and, based on the observations in Gardel et al. (Gardel et al. 2008 J. Cell Biol. 183, 999-1005 (doi:10.1083/jcb.200810060)), include a biphasic velocity-dependent traction stress acting on the actin network. While we chose certain specific models for each of these, in part to allow for an analytical solution, the generality of the framework allows one to readily introduce different constitutive laws to describe these phenomena as might be needed, for example, to study some different type of cells. As described in §5, the predictions of the model compare well with observations such as the magnitude of the very different actin retrograde speeds in the lamellum and lamellipodium including their jump at the interface, the magnitude of the cell speed, and the relative lengths of the lamellipodium and lamellum.

Enhancing Object Mapping in SLAM using CNN

Automation is becoming more prevalent among manufacturing and eCommerce companies as a way to better serve their customers. One of the key problems in warehouse management is controlling the internal delivery/movement of goods/objects. It is labor-intensive, time-consuming, and needs additional care based on delicacy goods. Automated guided vehicles (AGVs) that are small in size can serve as a solution to the aforementioned problem of locomotion. For any robot to move autonomously, the initial and critical requirement is to understand the surrounding environment precisely. Simultaneous Localisation and Mapping (SLAM) is the preferred method to build an environment map at runtime. SLAM is designed to work in a static environment and faces a few challenges once it involves dynamic objects. This research proposes Deep Learning to enhance the SLAM technique. It aids the identification of static and dynamic objects and consequently updates the occupancy grid map. The proposed approach has been validated through a simulated environment and a Convolution Neural Network (CNN) for the classification of static and dynamic objects. The simulation results demonstrate the promising nature of the proposed approach.

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living.

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing clinical and performance-based IADL assessments are not feasible for integration into clinical medicine. Virtual reality (VR) is a powerful yet underutilized tool that could advance the diagnosis and treatment of neurological disease. An impediment to the adoption and scaling of VR in clinical neurology is VR-related sickness resulting from sensory inconsistencies between the visual and vestibular systems (i.e., locomotion problem). The Cleveland Clinic Virtual Reality Shopping (CC-VRS) platform attempts to solve the locomotion problem by coupling an omnidirectional treadmill with high-resolution VR content, enabling the user to physically navigate a virtual grocery store to simulate shopping. The CC-VRS consists of Basic and Complex shopping experiences; both require walking 150 m and retrieving five items. The Complex experience has additional scenarios that increase the cognitive and motor demands of the task to better represent the continuum of activities associated with real-world shopping. The CC-VRS platform provides objective and quantitative biomechanical and cognitive outcomes related to the user's IADL performance. Initial data indicate that the CC-VRS results in minimal VR-sickness and is feasible and tolerable for older adults and patients with Parkinson's disease (PD). The considerations underlying the development, design, and hardware and software technology are reviewed, and initial models of integration into primary care and neurology are provided.

Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm.

An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of subtasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multiobjective Evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III, and ε-Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks, and a simulated robot maze-navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.

Graceful Transitions through Maximal Persistence of Behavior

Problems of locomotion typically encounter switching between gaits. In many applications, it is desirable to make such transitions between modes or gaits inconspicuous and graceful. This is achieved by keeping the typical behavior of the system as persistent as possible. It is shown that this problem can be defined in a more abstract sense, and therefore generalized to a new class of problems, called “maximum persistence of behavior.”In this presentation, I will give a precise definition of this concept, for signals and for systems, and illustrate some applications from thermodynamics to signal study and robotics. I will also introduce the dual problem of filtering in the context of “maximal persistence of behavior.” The results stem from joint work with Deryck Yeung (Trinity University, San Antonio, TX), Basit Memon (Habib University, Karachi, PK), and Vishal Murali (now at NVIDIA) while at Georgia Tech.The paradigm starts by defining typical behaviors in the framework initiated by J.C. Willems. Gluing two typical signals or typical system behaviors requires connections (raccordations) that belong to a larger class of behaviors, but are locally close to the typical behaviors. Solutions with persistent behavior are then sought via optimization for a kernel or an image problem. This solution encapsulates the controllability problem of Willems, morphing theory in image analysis, object shaping, quasi-stationary transitions from thermodynamics, and orbit transfer problems, and can be characterized in a geometric way. In the robotics framework, obvious applications are in gait transitions for biomimetic robots as when switching from walking (e.g., while inspecting) to running (evasion), or a gait transition necessitated by a change in terrain (solid, muddy, or granular). In particular, I will discuss a method of smoothly connecting periodic orbits of systems. We first deal with the case when the periodic signals to be connected are of the same period and then move on to discuss the case of different periods, requiring in addition a frequency warping.

Reinforcement Learning-Based Cascade Motion Policy Design for Robust 3D Bipedal Locomotion

This paper presents a novel reinforcement learning (RL) framework to design cascade feedback control policies for 3D bipedal locomotion. Existing RL algorithms are often trained in an end-to-end manner or rely on prior knowledge of some reference joint or task space trajectories. Unlike these studies, we propose a policy structure that decouples the bipedal locomotion problem into two modules that incorporate the physical insights from the nature of the walking dynamics and the well-established Hybrid Zero Dynamics approach for 3D bipedal walking. As a result, the overall RL framework has several key advantages, including lightweight network structure, sample efficiency, and less dependence on prior knowledge. The proposed solution learns stable and robust walking gaits from scratch and allows the controller to realize omnidirectional walking with accurate tracking of the desired velocity and heading angle. The learned policies also perform robustly against various adversarial forces applied to the torso and walking blindly on a series of challenging and unstructured terrains. These results demonstrate that the proposed cascade feedback control policy is suitable for navigation of 3D bipedal robots in indoor and outdoor environments.

"Like For Like" Reconstruction of Heel Pad with Medial Plantar Artery Flap-Functional and Aesthetic Outcomes in A Series of 19 Cases.

Heel pad loss can cause serious problems in weight-bearing and locomotion. The medial plantar artery (MPA) flap is a suitable “like for like” replacement. Nineteen patients whose heels were reconstructed with MPA flap between July 2015 and February 2020 were studied. All patients were assessed based on flap survival, functionality, and patient satisfaction. Loss of heel pad was due to diabetic ulcer (11), trauma (6), tumor (1), and unstable scar (1). The largest flap measured 9 × 7 cm. Sixteen flaps were done as fasciocutaneous flaps and three in combination with abductor hallucis muscle (AbdH). All the flaps survived. The average functional scores at 6, 12, 18, and 24 months were 86.86, 89.62, 89.38 and 97.33 based on AOSAS-AH score. Average patients' satisfaction was 8.7/10. To conclude, the MPA system is a versatile vascular axis providing “like for like” tissue for heel pad reconstruction which is reliable and durable.

Using virtual reality treadmill as a locomotion technique in a navigation task: Impact on user experience – case of the KatWalk

This paper reports empirical results from a study on the impact of the KatWalk virtual reality omnidirectional treadmill on the user experience. The omnidirectional treadmill is a mechanical device, that allows the user to perform locomotive motion in any direction, allowing for 360 degrees of horizontal movement. This locomotion method appeared as a potential solution of the locomotion problem in virtual reality after the emergence and the democratization of the new generation of head-mounted display systems such as HTC Vive and Oculus Rit. However, little empirical work has been done to test the efficiency of the tool as a locomotion technique in virtual environments. Twenty-four subjects (13 females, mean age = 30.38, SD = 6.32) participated in the experiment. Results showed that the tool is suitable for traveling in virtual environments seen through head-mounted display systems, whether they are composed of plan or bumpy ground, with or without obstacles.

Novel method for preventing shin-collisions in six-legged robots by utilising a robot–terrain interference model

Solving the problem of interference and adaptive locomotion is crucial to the success of urban rescue missions that use legged robots. However, previous studies have mainly focused on locomotion over moderate terrain without considering leg interference. As a result, the current legged robots can easily get stuck when traversing rough terrain such as stairs. Therefore, a method for preventing interference is proposed so that legged robots can take full advantage of their inherent ability to walk over rough terrain. This study investigates the gait planning task for a six-legged robot climbing stairs. This paper proposes a novel robot–terrain interference model, which can be efficiently established online using a terrain map. The simplicity of the interference model allows the rapid computation of an interference criterion. Such a criterion assists in the foothold generation and configuration optimisation of the robot while traversing terrain irregularities. We performed simulations and experiments on a novel six-legged robot named QingZhui by utilising the online planning method realised with a hierarchical control framework. The results validated the effectiveness of the method.

Towards More Possibilities: Motion Planning and Control for Hybrid Locomotion of Wheeled-Legged Robots

This letter proposed a control framework to tackle the hybrid locomotion problem of wheeled-legged robots. It comes as a hierarchical structure with three layers: hybrid foot placement planning, Centre of Mass (CoM) trajectory optimization and whole-body control. General mathematical representation of foot movement is developed to analyze different motion modes and decide hybrid foot placements. Gait graph widely used in legged locomotion is extended to better describe the hybrid movement by adding extra foot velocity information. Thereafter, model predictive control is introduced to optimize the CoM trajectory based on the planned foot placements considering terrain height changing. The desired trajectories together with other kinematic and dynamic constraints are fed into a whole-body controller to produce joint commands. In the end, the feasibility of the proposed approach is demonstrated by the simulation and experiments of hybrid locomotion running on our wheeled quadrupedal robot Pholus.

Deep Learning-Based Stair Segmentation and Behavioral Cloning for Autonomous Stair Climbing

Mobile robots are widely used in the surveillance industry, for military and industrial applications. In order to carry out surveillance tasks like urban search and rescue operation, the ability to traverse stairs is of immense significance. This paper presents a deep learning-based approach for semantic segmentation of stairs, behavioral cloning for stair alignment, and a novel mechanical design for an autonomous stair climbing robot. The main objective is to solve the problem of locomotion over staircases with the proposed implementation. Alignment of a robot with stairs in an image is a traditional problem, and the most recent approaches are centered around hand-crafted texture-based Gabor filters and stair detection techniques. However, we could arrive at a more scalable and robust pipeline for alignment schemes. The proposed deep learning technique eliminates the need for manual tuning of parameters of the edge detector, the Hough accumulator and PID constants. The empirical results and architecture of stair alignment pipeline are demonstrated in this paper.

Swimming with small and large amplitude waves in a confined liquid crystal

Boundaries can have a significant impact on the physics of microorganism locomotion. Here we examine the effects of confinement by a rigid boundary or symmetric channel on undulatory locomotion in an anisotropic fluid, treated as a nematic liquid crystal. The competition between hydrodynamics, fluid elasticity, and anchoring conditions results in a complex locomotion problem with unique transport properties. We examine this problem analytically using a well-known mathematical model, an infinite swimming sheet with small wave amplitude, and numerically for large amplitude waves using a modification of the immersed boundary method. For a prescribed stroke and strong planar anchoring in the narrow channel, we demonstrate that the swimming speed approaches its Newtonian value, though the power required to maintain the swimmer’s speed depends on the properties of the liquid crystal. We also show that an unusual prograde swimming (in the direction of transverse wave propagation) theorized to exist at small wave amplitude persists at large amplitude, and that the presence of a sufficiently close boundary returns the swimming behavior to the more standard retrograde motion (opposite the direction of the traveling wave).

An Adaptive Bioinspired Foot Mechanism Based on Tensegrity Structures.

Traditional robotic feet have received considerable attention for adaptive locomotion on complex terrain. As an alternative, tensegrity structures have the essential characteristics of deformability, adaptability to the environment, and impact resistance. This article proposes ways to solve the problem of adaptive locomotion on complex terrain based on a tensegrity structure and shows that this approach is particularly useful. On the basis of the locomotion mechanism and morphological structure of the human foot, a structural mapping model of a tetrahedral mast tensegrity structure is established through bionic mapping. A model of an adaptive foot mechanism is established through bioinspired design. Theoretical calculations of the behavior of the mechanism are derived, and the spring stiffnesses are matched. A theoretical method based on mechanical kinematics is presented, and a kinematic solution is realized through inverse kinematics. In addition, the locomotion of the mechanism, which is similar to that of the human foot, is simulated using ADAMS, and the effectiveness of the proposed theory and design method is verified by comparing the simulation output with the theoretically calculated results. Finally, a physical prototype manufactured using three-dimensional printing technology is used to experimentally verify the functional characteristics of the terrain-adaptive locomotion of the proposed mechanism. The results show that the proposed adaptive bioinspired foot mechanism exhibits good stability in an unstructured environment and can mimic the adaptive locomotion characteristics of the human foot on complex terrain remarkably well.

ATRIBUTOS QUALITATIVOS E FATORES DE SATISFAÇÃO COM O TRANSPORTE PÚBLICO URBANO POR ÔNIBUS

O crescimento das cidades tem influenciado a mobilidade urbana. Os meios de transporte coletivo representam uma maneira de amenizar os problemas de locomoção, sendo a satisfação dos usuários um dos principais indicadores da qualidade do serviço prestado. Objetivando identificar os atributos qualitativos e os fatores que impactam na satisfação com a qualidade de ônibus urbanos, realizou-se este estudo. Inicialmente, fez-se uma investigação exploratória e qualitativa com 25 usuários de ônibus, levantando os atributos qualitativos. Posteriormente, avaliou-se a satisfação com tais atributos, envolvendo uma amostra de 203 usuários. Resultou uma correlação significativa entre a satisfação geral dos usuários e a intenção de utilizar novamente o serviço de transporte público. Também se identificaram seis fatores preponderantes para a satisfação com o serviço de ônibus urbano: atendimento ao passageiro, informações ao passageiro, veículo de transporte, serviço de transporte público, estrutura das paradas de ônibus e ambiente das paradas.

Learning symmetric and low-energy locomotion

Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific knowledge to guide the motion generation algorithms. Deep reinforcement learning (DRL) is a promising approach for the automatic creation of locomotion control. Indeed, a standard benchmark for DRL is to automatically create a running controller for a biped character from a simple reward function [Duan et al. 2016]. Although several different DRL algorithms can successfully create a running controller, the resulting motions usually look nothing like a real runner. This paper takes a minimalist learning approach to the locomotion problem, without the use of motion examples, finite state machines, or morphology-specific knowledge. We introduce two modifications to the DRL approach that, when used together, produce locomotion behaviors that are symmetric, low-energy, and much closer to that of a real person. First, we introduce a new term to the loss function (not the reward function) that encourages symmetric actions. Second, we introduce a new curriculum learning method that provides modulated physical assistance to help the character with left/right balance and forward movement. The algorithm automatically computes appropriate assistance to the character and gradually relaxes this assistance, so that eventually the character learns to move entirely without help. Because our method does not make use of motion capture data, it can be applied to a variety of character morphologies. We demonstrate locomotion controllers for the lower half of a biped, a full humanoid, a quadruped, and a hexapod. Our results show that learned policies are able to produce symmetric, low-energy gaits. In addition, speed-appropriate gait patterns emerge without any guidance from motion examples or contact planning.