In this paper, we computationally model the social behavior of beetles and apply it to the tracking control of manipulators. The beetles demonstrate excellent skills to forage food in a previously unknown environment by merely using their olfactory senses. The goal of the beetle is to search the region with the maximum smell. Therefore, the actions of the beetle can be characterized as an optimization algorithm. This paper mathematically models this behavior in the form of a Recurrent Neural Network (RNN) with a temporal-feedback connection. We apply the formulated RNN controller for the redundancy resolution and tracking control of the redundant manipulators with an unknown kinematic model. Most of the industrial robots have redundant manipulators, and kinematic trajectory tracking is a fundamental problem for any industrial task. The behavior of the beetle allows us to formulate a position-level controller without relying on the manipulation of the Jacobian matrix. It is in contrast with the conventional velocity-level controllers, which require an accurate kinematic model of the manipulator and calculation of pseudo-inverse of Jacobian, a computationally expensive task. The proposed algorithm, called Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm; is capable of driving the manipulator by only using the feedback from the position and orientation sensors. The stability and convergence of the proposed algorithm are theoretically proved, and simulations results using a 7-DOF industrial robotic arm, KUKA LBR IIWA14, are presented to demonstrate the performance of the proposed algorithm.Legend: † g(x) denotes the intensity of smell. ‡ Color intensity ∝ g(x).

This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots. Fabricated using soft materials, soft robots, have gained research attention in academia as well as in industry during recent years because of their inherent safety in human interaction. However, due to structural flexibility and compliance, the mathematical model of these soft robots is nonlinear with an infinite degree of freedom (DOF). Therefore, the accurate position (or orientation) control and optimization of their dynamic response remains a challenging task. Most of the existing soft robots being used in industrial and rehabilitation applications use model-free control algorithms such as PID. However, to the best of our knowledge, there has been no systematic study on the comparative performance of the model-free control algorithms and their ability to optimize the dynamic response i.e., reduce overshoot and settling time. In this paper, we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results. Additionally, most of the existing work on model-free control in pneumatic soft-robotic literature use manually tuned parameters, which is a time-consuming, labor-intensive task. We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically. We presented results for both manually tuned, using the Ziegler-Nichols method, and automatic tuning using the proposed algorithm. We used the experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy. The experiment results show that for soft robots, the PID-controller essentially reduces to the PI controller. This behavior was observed in both manual and automatic tuning experiments; we also discussed a rationale for removing the derivative term.

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Introduction: Obesity has been established as a major risk factor for a number of non-communicable diseases and over the year's multiple strategies have been directed at addressing this issue including minimally invasive procedures like laparoscopic sleeve Gastrectomy, specifically with an end goal of weight reduction for the morbidly obese. This procedure has become the preferred choice for both patients and physicians over the past few years. Laparoscopic sleeve Gastrectomy was introduced at our center recently; we have carried out a retrospective review of charts to evaluate this procedure short-term outcome at our center in our local population. Methods: A retrospective Cohort study, based on a record review for the treatment outcome of laparoscopic sleeve gastrectomy, was carried out at the department of surgery, Aga khan University Hospital, Karachi over a three-year period since its inception and analyzed in June 2015 using SPSS version 20. Results: A total of 17 patients fulfilled the inclusion criteria, out of which 12 were females (70.6%). The mean age of study participants was 41.53 years. Only one patient had undergone liposuction previously for weight loss. The most common comorbidities observed were diabetes mellitus (23.6%), hypertension (23.6%) and polycystic ovarian syndrome (17.7%). A statistically significant mean reduction in excess body weight of 28.9±14.90 Kg, CI 21.27-36.59 was observed along with reduction in BMI at 1 year with a mean difference of 11.1±5.38 Kg/m 2 , CI 21.27-36.60. Results were further analyzed for reduction in percentage excess body weight which showed a mean reduction of 43.6% for the study participants. Co-morbidity improvement was seen as reduction in systolic blood pressures in 9 patients (52%) though these were not found to be significant. Conclusion: Laparoscopic Sleeve Gastrectomy shows great potential for the Indian sub-continent population, especially for patients requiring rapid weight loss for better health outcomes, although long term follow up and out comes will determine the effectiveness of the procedure over extended periods and its role as a first line intervention for obesity.