A comparison between a two-feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

Highly-compliant elastic actuators have become progressively prominent over the last years for a variety of robotic applications. With remarkable shock tolerance, elastic actuators are appropriate for robots operating in unstructured environments. In accordance with this trend, a novel elastic actuator was recently designed by our research group for Serpens, a low-cost, open-source and highly-compliant multi-purpose modular snake robot. To control the newly designed elastic actuators of Serpens, a two-feedback loops position control algorithm was proposed. The inner controller loop is implemented as a model reference adaptive controller (MRAC), while the outer control loop adopts a fuzzy proportional-integral controller (FPIC). The performance of the presented control scheme was demonstrated through simulations. However, the efficiency of the proposed controller is dependent on the initial values of the parameters of the MRAC controller as well as on the effort required for a human to manually construct fuzzy rules. An alternative solution to the problem might consist of using methods that do not assume a priori knowledge: a solution that derives its properties from a machine learning procedure. In this way, the controller would be able to automatically learn the properties of the elastic actuator to be controlled. In this work, a novel controller for the proposed elastic actuator is presented based on the use of an artificial neural network (ANN) that is trained with reinforcement learning. The newly designed control algorithm is extensively compared with the former approach. Simulation results are presented for both methods. The authors seek to achieve a fair, non-biased, risk-aware and trustworthy comparison

A comparison between a two feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

publishedVersio

Modelling and Control of a 2-DOF Robot Arm with Elastic Joints for Safe Human-Robot Interaction

Collaborative robots (or cobots) are robots that can safely work together or interact with humans in a common space. They gradually become noticeable nowadays. Compliant actuators are very relevant for the design of cobots. This type of actuation scheme mitigates the damage caused by unexpected collision. Therefore, elastic joints are considered to outperform rigid joints when operating in a dynamic environment. However, most of the available elastic robots are relatively costly or difficult to construct. To give researchers a solution that is inexpensive, easily customisable, and fast to fabricate, a newly-designed low-cost, and open-source design of an elastic joint is presented in this work. Based on the newly design elastic joint, a highly-compliant multi-purpose 2-DOF robot arm for safe human-robot interaction is also introduced. The mechanical design of the robot and a position control algorithm are presented. The mechanical prototype is 3D-printed. The control algorithm is a two loops control scheme. In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load. The control algorithm is first validated in simulation. Then the effectiveness of the controller is also proven by experiments on the mechanical prototype.publishedVersio

A comparison between a two feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

Highly-compliant elastic actuators have become progressively prominent over the last years for a variety of robotic applications. With remarkable shock tolerance, elastic actuators are appropriate for robots operating in unstructured environments. In accordance with this trend, a novel elastic actuator was recently designed by our research group for Serpens, a low-cost, open-source and highly-compliant multi-purpose modular snake robot. To control the newly designed elastic actuators of Serpens, a two-feedback loops position control algorithm was proposed. The inner controller loop is implemented as a model reference adaptive controller (MRAC), while the outer control loop adopts a fuzzy proportional-integral controller (FPIC). The performance of the presented control scheme was demonstrated through simulations. However, the efficiency of the proposed controller is dependent on the initial values of the parameters of the MRAC controller as well as on the effort required for a human to manually construct fuzzy rules. An alternative solution to the problem might consist of using methods that do not assume a priori knowledge: a solution that derives its properties from a machine learning procedure. In this way, the controller would be able to automatically learn the properties of the elastic actuator to be controlled. In this work, a novel controller for the proposed elastic actuator is presented based on the use of an artificial neural network (ANN) that is trained with reinforcement learning. The newly designed control algorithm is extensively compared with the former approach. Simulation results are presented for both methods. The authors seek to achieve a fair, non-biased, risk-aware and trustworthy comparison

A robust two-feedback loops position control algorithm for compliant low-cost series elastic actuators

Author's accepted manuscript (postprint).© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio

Modelling and Control of a 2-DOF Robot Arm with Elastic Joints for Safe Human-Robot Interaction

Collaborative robots (or cobots) are robots that can safely work together or interact with humans in a common space. They gradually become noticeable nowadays. Compliant actuators are very relevant for the design of cobots. This type of actuation scheme mitigates the damage caused by unexpected collision. Therefore, elastic joints are considered to outperform rigid joints when operating in a dynamic environment. However, most of the available elastic robots are relatively costly or difficult to construct. To give researchers a solution that is inexpensive, easily customisable, and fast to fabricate, a newly-designed low-cost, and open-source design of an elastic joint is presented in this work. Based on the newly design elastic joint, a highly-compliant multi-purpose 2-DOF robot arm for safe human-robot interaction is also introduced. The mechanical design of the robot and a position control algorithm are presented. The mechanical prototype is 3D-printed. The control algorithm is a two loops control scheme. In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load. The control algorithm is first validated in simulation. Then the effectiveness of the controller is also proven by experiments on the mechanical prototype

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot arm with elastic actuators is addressed by proposing a two-loop adaptive controller. For the outer control loop, an adaptive sliding mode controller (ASMC) is adopted to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loops, model reference adaptive controllers (MRAC) are utilised to handle the uncertainties on the motor side of the robot arm. To show the effectiveness of the proposed controller, extensive simulation experiments and a comparison with the conventional sliding mode controller (SMC) are carried out. As a result, the ASMC has a 50.35% lower average RMS error than the SMC controller, and a shorter settling time (5% criterion) (0.44 s compared to 2.11 s)

<i>Phaeanthus vietnamensis</i> Ban Ameliorates Lower Airway Inflammation in Experimental Asthmatic Mouse Model via Nrf2/HO-1 and MAPK Signaling Pathway

Asthma is a chronic airway inflammatory disease listed as one of the top global health problems. Phaeanthus vietnamensis BÂN is a well-known medicinal plant in Vietnam with its anti-oxidant, anti-microbial, anti-inflammatory potential, and gastro-protective properties. However, there is no study about P. vietnamensis extract (PVE) on asthma disease. Here, an OVA-induced asthma mouse model was established to evaluate the anti-inflammatory and anti-asthmatic effects and possible mechanisms of PVE. BALB/c mice were sensitized by injecting 50 μg OVA into the peritoneal and challenged by nebulization with 5% OVA. Mice were orally administered various doses of PVE once daily (50, 100, 200 mg/kg) or dexamethasone (Dex; 2.5 mg/kg) or Saline 1 h before the OVA challenge. The cell infiltrated in the bronchoalveolar lavage fluid (BALF) was analyzed; levels of OVA-specific immunoglobulins in serum, cytokines, and transcription factors in the BALF were measured, and lung histopathology was evaluated. PVE, especially PVE 200mg/kg dose, could improve asthma exacerbation by balancing the Th1/Th2 ratio, reducing inflammatory cells in BALF, depressing serum anti-specific OVA IgE, anti-specific OVA IgG1, histamine levels, and retrieving lung histology. Moreover, the PVE treatment group significantly increased the expressions of antioxidant enzymes Nrf2 and HO-1 in the lung tissue and the level of those antioxidant enzymes in the BALF, decreasing the oxidative stress marker MDA level in the BALF, leading to the relieving the activation of MAPK signaling in asthmatic condition. The present study demonstrated that Phaeanthus vietnamensis BÂN, traditionally used in Vietnam as a medicinal plant, may be used as an efficacious agent for treating asthmatic disease

Tumour microbiomes and Fusobacterium genomics in Vietnamese colorectal cancer patients.

Perturbations in the gut microbiome have been associated with colorectal cancer (CRC), with the colonic overabundance of Fusobacterium nucleatum shown as the most consistent marker. Despite its significance in the promotion of CRC, genomic studies of Fusobacterium is limited. We enrolled 43 Vietnamese CRC patients and 25 participants with non-cancerous colorectal polyps to study the colonic microbiomes and genomic diversity of Fusobacterium in this population, using a combination of 16S rRNA gene profiling, anaerobic microbiology, and whole genome analysis. Oral bacteria, including F. nucleatum and Leptotrichia, were significantly more abundant in the tumour microbiomes. We obtained 53 Fusobacterium genomes, representing 26 strains, from the saliva, tumour and non-tumour tissues of six CRC patients. Isolates from the gut belonged to diverse F. nucleatum subspecies (nucleatum, animalis, vincentii, polymorphum) and a potential new subspecies of Fusobacterium periodonticum. The Fusobacterium population within each individual was distinct and in some cases diverse, with minimal intra-clonal variation. Phylogenetic analyses showed that within four individuals, tumour-associated Fusobacterium were clonal to those isolated from non-tumour tissues. Genes encoding major virulence factors (Fap2 and RadD) showed evidence of horizontal gene transfer. Our work provides a framework to understand the genomic diversity of Fusobacterium within the CRC patients, which can be exploited for the development of CRC diagnostic and therapeutic options targeting this oncobacterium