Design, analysis, and control of a cable-driven parallel platform with a pneumatic muscle active support

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The neck is an important part of the body that connects the head to the torso, supporting the weight and generating the movement of the head. In this paper, a cable-driven parallel platform with a pneumatic muscle active support (CPPPMS) is presented for imitating human necks, where cable actuators imitate neck muscles and a pneumatic muscle actuator imitates spinal muscles, respectively. Analyzing the stiffness of the mechanism is carried out based on screw theory, and this mechanism is optimized according to the stiffness characteristics. While taking the dynamics of the pneumatic muscle active support into consideration as well as the cable dynamics and the dynamics of the Up-platform, a dynamic modeling approach to the CPPPMS is established. In order to overcome the flexibility and uncertainties amid the dynamic model, a sliding mode controller is investigated for trajectory tracking, and the stability of the control system is verified by a Lyapunov function. Moreover, a PD controller is proposed for a comparative study. The results of the simulation indicate that the sliding mode controller is more effective than the PD controller for the CPPPMS, and the CPPPMS provides feasible performances for operations under the sliding mode control

Characteristics and Performance of CAUTO (CAssino hUmanoid TOrso) Prototype

An artificial torso is a fundamental part of a humanoid robot for imitating human actions. In this paper, a prototype of CAUTO (CAssino hUmanoid TOrso) is presented. Its design is characterized by artificial vertebras actuated by cable-driven parallel manipulators. The design was conceived by looking at the complex system and functioning of the human torso, in order to develop a solution for basic human-like behavior. The requirements and kinematic structure are introduced to explain the peculiarities of the proposed mechanical design. A prototype is presented, and built with low-cost and high-performance features. Tests results are reported to show the feasibility and the characteristics in replicating human torso motions. In addition, the power consumption has been measured during the tests to prove the efficiency of the Li-Po battery supply, employed for a fully portable solution of the designed torso

Kinematic Modelling and Motion Analysis of a Humanoid Torso Mechanism

This paper introduces a novel kinematic model for a tendon-driven compliant torso mechanism for humanoid robots, which describes the complex behaviour of a system characterised by the interaction of a complex compliant element with rigid bodies and actuation tendons. Inspired by a human spine, the proposed mechanism is based on a flexible backbone whose shape is controlled by two pairs of antagonistic tendons. First, the structure is analysed to identify the main modes of motion. Then, a constant curvature kinematic model is extended to describe the behaviour of the torso mechanism under examination, which includes axial elongation/compression and torsion in addition to the main bending motion. A linearised stiffness model is also formulated to estimate the static response of the backbone. The novel model is used to evaluate the workspace of an example mechanical design, and then it is mapped onto a controller to validate the results with an experimental test on a prototype. By replacing a previous approximated model calibrated on experimental data, this kinematic model improves the accuracy and efficiency of the torso mechanism and enables the performance evaluation of the robot over the reachable workspace, to ensure that the tendon-driven architecture operates within its wrench-closure workspace

The design, analysis and evaluation of a humanoid robotic head

Where robots interact directly with humans on a ‘one-to-one’ basis, it is often quite important for them to be emotionally acceptable, hence the growing interesting in humanoid robots. In some applications it is important that these robots do not just resemble a human being in appearance, but also move like a human being too, to make them emotionally acceptable – hence the interest in biomimetic humanoid robotics. The research described in this thesis is concerned with the design, analysis and evaluation of a biomimetic humanoid robotic head. It is biomimetic in terms of physical design - which is based around a simulated cervical spine, and actuation, which is achieved using pneumatic air muscles (PAMS). The primary purpose of the research, however, and the main original contribution, was to create a humanoid robotic head capable of mimicking complex non-purely rotational human head movements. These include a sliding front-to-back, lateral movement, and a sliding, side-to-side lateral movement. A number of different approaches were considered and evaluated, before finalising the design. As there are no generally accepted metrics in the literature regarding the full range of human head movements, the best benchmarks for comparison are the angular ranges and speeds of humans in terms on pitch (nod), roll (tilt) and yaw (rotate) were used for comparison, and these they were considered desired ranges for the robot. These measured up well in comparison in terms of angular speed and some aspects of range of human necks. Additionally, the lateral movements were measured during the nod, tilt and rotate movements, and established the ability of the robot to perform the complex lateral movements seen in humans, thus proving the benefits of the cervical spine approach. Finally, the emotional acceptance of the robot movements was evaluated against another (commercially made) robot and a human. This was a blind test, in that the (human) evaluators had no way of knowing whether they were evaluation a human or a robot. The tests demonstrated that on scales of Fake/Natural, Machinelike/Humanlike and Unconcsious/Conscious the robot the robot scored similarly to the human

Ground Reference Points in Legged Locomotion: Definitions, Biological Trajectories and Control Implications

The Zero Moment Point (ZMP) and Centroidal Moment Pivot (CMP) are important ground reference points used for motion identification and control in biomechanics and legged robotics. Using a consistent mathematical notation, we define and compare the ground reference points. We outline the various methodologies that can be employed in their estimation. Subsequently, we analyze the ZMP and CMP trajectories for level-ground, steady-state human walking. We conclude the chapter with a discussion of the significance of the ground reference points to legged robotic control systems. In the Appendix, we prove the equivalence of the ZMP and the center of pressure for horizontal ground surfaces, and their uniqueness for more complex contact topologies. Since spin angular momentum has been shown to remain small throughout the walking cycle, we hypothesize that the CMP will never leave the ground support base throughout the entire gait cycle, closely tracking the ZMP. We test this hypothesis using a morphologically realistic human model and kinetic and kinematic gait data measured from ten human subjects walking at self-selected speeds. We find that the CMP never leaves the ground support base, and the mean separation distance between the CMP and ZMP is small (14 % of foot length), highlighting how closely the human body regulates spin angular momentum in level ground walking

Design and Experiments of a Novel Humanoid Robot with Parallel Architectures

In this paper, the mechanical design of the LARMbot 2, a low-cost user-oriented humanoid robot was presented. LARMbot 2 is characterized by parallel architectures for both the torso and legs. The proposed design was presented with the kinematics of its main parts—legs, torso, arms—and then compared to its previous version, which was characterized by a different leg mechanism, to highlight the advantages of the latest design. A prototype was then presented, with constructive details of its subsystems and its technical specifications. To characterize the performance of the proposed robot, experimental results were presented for both the walking and weight-lifting operations

The Robotic Lumbar Spine: Dynamics and Feedback Linearization Control

The robotic lumbar spine (RLS) is a 15 degree-of-freedom, fully cable-actuated robotic lumbar spine which can mimic in vivo human lumbar spine movements to provide better hands-on training for medical students. The design incorporates five active lumbar vertebrae and the sacrum, with dimensions of an average adult human spine. It is actuated by 20 cables connected to electric motors. Every vertebra is connected to the neighboring vertebrae by spherical joints. Medical schools can benefit from a tool, system, or method that will help instructors train students and assess their tactile proficiency throughout their education. The robotic lumbar spine has the potential to satisfy these needs in palpatory diagnosis. Medical students will be given the opportunity to examine their own patient that can be programmed with many dysfunctions related to the lumbar spine before they start their professional lives as doctors. The robotic lumbar spine can be used to teach and test medical students in their capacity to be able to recognize normal and abnormal movement patterns of the human lumbar spine under flexion-extension, lateral bending, and axial torsion. This paper presents the dynamics and nonlinear control of the RLS. A new approach to solve for positive and nonzero cable tensions that are also continuous in time is introduced

Development of a Low Motion-Noise Humanoid Neck: Statics Analysis and Experimental Validation

Abstract-This paper presents our recently developed humanoid neck system that can effectively mimic motion of human neck with very low motion noises. The feature of low motion noises allows our system to work like a real human head/neck. Thus the level of acoustic noises from wearable equipments, such as donning respirators or chemical-resistant jackets, induced by human head motion can be simulated and investigated using such a system. The objective of this investigation is to facilitate using head-worn communication devices for the person who wears the protective equipment/uniform that usually produces communication-noise when the head/neck moves. Our low motion-noise humanoid neck system is based on the spring structure, which can generate 1 Degree of Freedom (DOF) jaw movement and 3DOF neck movement. To guarantee the low-noise feature, no noise-makers like gear and electrodriven parts are embedded in the head/neck structure. Instead, the motions are driven by seven cables, and the actuators pulling the cables are sealed in a sound insulation box. Furthermore, statics analysis of the system has been processed completely. Experimental results validate the analysis, and clearly show that the head/neck system can greatly mimic the motions of human head with an A-weighted noise level of 30 dB or below

Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning

Reinforcement learning (RL) requires either manually specifying a reward function, which is often infeasible, or learning a reward model from a large amount of human feedback, which is often very expensive. We study a more sample-efficient alternative: using pretrained vision-language models (VLMs) as zero-shot reward models (RMs) to specify tasks via natural language. We propose a natural and general approach to using VLMs as reward models, which we call VLM-RMs. We use VLM-RMs based on CLIP to train a MuJoCo humanoid to learn complex tasks without a manually specified reward function, such as kneeling, doing the splits, and sitting in a lotus position. For each of these tasks, we only provide a single sentence text prompt describing the desired task with minimal prompt engineering. We provide videos of the trained agents at: https://sites.google.com/view/vlm-rm. We can improve performance by providing a second ``baseline'' prompt and projecting out parts of the CLIP embedding space irrelevant to distinguish between goal and baseline. Further, we find a strong scaling effect for VLM-RMs: larger VLMs trained with more compute and data are better reward models. The failure modes of VLM-RMs we encountered are all related to known capability limitations of current VLMs, such as limited spatial reasoning ability or visually unrealistic environments that are far off-distribution for the VLM. We find that VLM-RMs are remarkably robust as long as the VLM is large enough. This suggests that future VLMs will become more and more useful reward models for a wide range of RL applications