Foot trajectory analysis of a robotic leg

When it comes to walking robots, foot trajectory is a crucial element that can significantly influence the efficiency of the walking robot. This paper analyses the various foot return trajectories, which can provide higher step length while consuming less power. It is done through mathematical analysis and verified using simulations in software such as MSC Adams and Solidworks. This paper also discusses the kinematic and dynamic analysis of the two degrees of freedom leg using theoretical approaches in MATLAB and verifies the results using the simulation in MSC Adams.

Quadrupedal Human-Assistive Robotic Platform (Q-HARP): Design, Control, and Preliminary Testing

With the rapid expansion of older adult populations around the world, mobility impairment is becoming an increasingly challenging issue. For the assistance of individuals with mobility impairments, there are two major types of tools in the current practice, including the passive (unpowered) walking aids (canes, walkers, rollators, etc.) and wheelchairs (powered and unpowered). Despite their extensive use, there are significant weaknesses that affect their effectiveness in daily use, especially when challenging uneven terrains are encountered. To address these issues, the authors developed a novel robotic platform intended for the assistance of mobility-challenged individuals. Unlike the existing assistive robots serving similar purposes, the proposed robot, namely, quadrupedal human-assistive robotic platform (Q-HARP), utilizes legged locomotion to provide an unprecedented potential to adapt to a wide variety of challenging terrains, many of which are common in people’s daily life (e.g., roadside curbs and the few steps leading to a front door). In this paper, the design of the robot is presented, including the overall structure of the robot and the design details of the actuated robotic leg joints. For the motion control of the robot, a joint trajectory generator is formulated, with the purpose of generating a stable walking gait to provide reliable support to its human user in the robot’s future application. The Q-HARP robot and its control system were experimentally tested, and the results demonstrated that the robot was able to provide a smooth gait during walking.

Myoelectric Control of Robotic Leg Prostheses and Exoskeletons: A Review

Myoelectric signals from the human motor control system can improve the real-time control and neural-machine interface of robotic leg prostheses and exoskeletons for different locomotor activities (e.g., walking, sitting down, stair ascent, and non-rhythmic movements). Here we review the latest advances in myoelectric control designs and propose future directions for research and innovation. We review the different wearable sensor technologies, actuators, signal processing, and pattern recognition algorithms used for myoelectric locomotor control and intent recognition, with an emphasis on the hierarchical architectures of volitional control systems. Common mechanisms within the control architecture include 1) open-loop proportional control with fixed gains, 2) active-reactive control, 3) joint mechanical impedance control, 4) manual-tuning torque control, 5) adaptive control with varying gains, and 6) closed-loop servo actuator control. Based on our review, we recommend that future research consider using musculoskeletal modeling and machine learning algorithms to map myoelectric signals from surface electromyography (EMG) to actuator joint torques, thereby improving the automation and efficiency of next-generation EMG controllers and neural interfaces for robotic leg prostheses and exoskeletons. We also propose an example model-based adaptive impedance EMG controller including muscle and multibody system dynamics. Ongoing advances in the engineering design of myoelectric control systems have implications for both locomotor assistance and rehabilitation.

Design and Kinematic Simulation of a Novel Leg Mechanism for Multi-Legged Robots

Among mobile robotic research field, legged locomotion is largely applied for advanced robotic systems due to the higher degree of versatility compared to wheeled robots, which allows them to successfully move and interact in unstructured environments; nevertheless, legged robots present several designing problems and require a much more complex control system. Based on an effective robotic leg, this paper presents a novel design, which integrates a cam joint, aimed to improve the versatility performances minimizing changes in the original model and without increasing the control complexity. Furthermore, the design strategy aims to exploit the coupled action of two actuators, which are disposed in a novel configuration so to gain versatility advantage while maintaining velocity performances of legs equipped with a single actuator. The model is presented through a kinematic analysis, followed by the simulation of the leg mechanism trajectory and a comparison with the original configuration.

Environment Classification for Robotic Leg Prostheses and Exoskeletons using Deep Convolutional Neural Networks

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, the current locomotion mode recognition systems being developed for intelligent high-level control and decision-making use mechanical, inertial, and/or neuromuscular data, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, we designed and evaluated an advanced environment classification system that uses computer vision and deep learning to forward predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust locomotion mode transitions. In this study, we first reviewed the development of the ExoNet database – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a hierarchical labelling architecture. We then trained and tested over a dozen state-of-the-art deep convolutional neural networks (CNNs) on the ExoNet database for large-scale image classification of the walking environments, including: EfficientNetB0, InceptionV3, MobileNet, MobileNetV2, VGG16, VGG19, Xception, ResNet50, ResNet101, ResNet152, DenseNet121, DenseNet169, and DenseNet201. Lastly, we quantitatively compared the benchmarked CNN architectures and their environment classification predictions using an operational metric called NetScore, which balances the image classification accuracy with the computational and memory storage requirements (i.e., important for onboard real-time inference). Although we designed this environment classification system to support the development of next-generation environment-adaptive locomotor control systems for robotic prostheses and exoskeletons, applications could extend to humanoids, autonomous legged robots, powered wheelchairs, and assistive devices for persons with visual impairments.

Mechanisms of neuro-robotic prosthesis operation in leg amputees

Above-knee amputees suffer the lack of sensory information, even while using most advanced prostheses. Restoring intraneural sensory feedback results in functional and cognitive benefits. It is unknown how this artificial feedback, restored through a neuro-robotic leg, influences users’ sensorimotor strategies and its implications for future wearable robotics. To unveil these mechanisms, we measured gait markers of a sensorized neuroprosthesis in two leg amputees during motor tasks of different difficulty. Novel sensorimotor strategies were intuitively promoted, allowing for a higher walking speed in both tasks. We objectively quantified the augmented prosthesis’ confidence and observed the reshaping of the legs’ kinematics toward a more physiological gait. In a possible scenario of a leg amputee driving a conventional car, we showed a finer pressure estimation from the prosthesis. Users exploited different features of the neural stimulation during tasks, suggesting that a simple prosthesis sensorization could be effective for future neuro-robotic prostheses.

THE COST OF TRANSPORT (COT) OF A HIGH ENERGY EFFICIENCY HYBRID ROBOT

Purpose: The cost of transport is one of the most important values to the efficiency and operation autonomy of a walking robot. This analysis involves factors as the weight, consumption of the actuators, speeds, accelerations, work surfaces, step cycle model or distance travelled, which must be studied in detail to produce stable and energy-efficient locomotion. This paper presents the results obtained for the cost of transport of a hybrid robot with two front legs and two rear wheels, with a total weight of 50 kg in different scenarios. Methodology/approach - The transportation cost of the proposed hybrid robot is obtained by carrying out a detailed analysis of the kinematics, dynamics, stability and energy consumption. Findings - A satisfactory value of efficiency has been obtained, in terms of cost of transport, owing to a gravitationally decoupled design of the legs. The cost of transport of the robot proposed is between 0.11 and 0.24, depending on the work environment in which it operates, that is, walking on a smooth horizontal plane without additional load. Originality/value – This work presents a new design of a gravitationally decoupled robotic leg by means of a new scheme in which the leg is composed of three four-bar mechanisms that can be synthesized independently. These three mechanisms involve frontal and vertical movement within the same plane of movement. One mechanism generates a horizontal path for tow, while another generates a vertical path and a third has the specific mission of making the tow velocity constant when the corresponding motor is operated at a constant velocity. The overall goal of the mechanisms is to improve robot's efficiency. Key Words: Cost of transport, gravitationally uncoupled motion, energy efficiency, experimental validation, hybrid robot.