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The disposal of non-biodegradable materials like polyethylene, tetrapak etc have been a severe threat to the surrounding environment especially in developing/ under developing countries. Out of that, the polyethylene materials are commonly used to pack food and essential commodities like milk, curd, ghee, oil, snacks etc. After usage of these products, the polyethylene packs are left as solid wastes which was disposed either incineration or by dumping as landfills. An effort to find useful application of those polyethylene material wastes is on one side. In other side the existing pavements are not able to withstand the increase in traffic loads and thereby results to premature failure causing various pavement distress. In these aspects, to explore new suitable and sustainable alternative bituminous mix is a demanding need. In order to address both these issues, the present study investigated the suitability of polyethylene wastes in to the bitumen concrete mix. For this study, the polyethylene wastes of milk packets are shredded to a varying length of 1.5 cm, 3 cm, and 6 cm are mixed in dry pieces with the heated aggregates at temperature of 150–170 °C. Marshall specimen of bituminous concrete with and without shredded polyethylene wastes are prepared and tested as per the standard procedure. The Marshall characteristics like Marshall Stability, flow value, unit weight, air voids etc are determined. Based on the performance evaluation, all the Marshall characteristics of the mix are found to be satisfied for the utilization of shredded polyethylene waste strip 3.0 cm at 0.5% by weight of aggregate for both bitumen content 5% and 6%.

Actuators are a vital component, and more often than not one of the limiting factors in robotics and robotics related applications. For the use of actuators in robotics related to humanoids, exoskeletons, prosthetics and orthoses, there are more factors that influence the selected actuator than the basic mechanical outputs, size, backlash, material and power consumption. The main interest within these applications is that because the device is being carried by the human user, thus weight, power consumption and form factor are important selection parameters. The correct selection of an actuator for these applications is a difficult and lengthy process to perform. This paper creates an organizational framework and database for searching the wide range of actuators. This dataset is organized into a design tool that plots the properties of each actuator on varying graphs creating trade-off Ashby charts to rapidly narrow the selection space for designers. A case study is performed to demonstrate the use of this design tool in human-centric actuation applications. The database is utilized in the selection of the ideal actuator based on lines of best fit and a multivariate regression analysis for the optimization of parameters about the required specifications. In addition, a meta-analysis identifies clusters of current actuators, gaps for new developments, and trends. This work provides research direction into developing specific actuators to fit into these trend gaps which offers substantial benefits to humanoids, exoskeletons, prosthetics and orthosis.

The biggest pollution concern today is the overflow of garbage. This creates unhygienic conditions for individuals and creates a bad smell in the environment which in turn leads to the spreading of deadly viruses and human diseases. In order to avoid such issues, smart dustbin control is carried out and implemented with the help of various sensors. Various sensors are used in this system to monitor the level and specific IDs will be given for each dustbin in the cities so that it is easy to recognize which waste bin is complete. The GSM module is used to send a message forupdating the status of the bin to the respective authorities. When the bin level exceeds the threshold limit; the system transmits the reading with the given unique ID. After that, the dustbin will automatically close the door. Once the bins are complete, the user will not be able to access the bins. The status of the bin will be indicated on the LCD display and it will send a message with the help of GSM to the concerned authorities from their venues. So in order to clean the over flowing bins, the concern authority will take immediate action. The rain sensor is used to track rain. The door will automatically gets closed when the rain is detected in order to prevent the water entering inside the bin.

© 2019 IEEE. Physical Human-Robot Collaboration (pHRC) is about the interaction between one or more human operator(s) and one or more robot(s) in direct contact and voluntarily exchanging forces to accomplish a common task. In any pHRC, the intuitiveness of the interaction has always been a priority, so that the operator can comfortably and safely interact with the robot. So far, the intuitiveness has always been described in a qualitative way. In this paper, we suggest an objective way to evaluate intuitiveness, known as prediction error negativity (PEN) using electroencephalogram (EEG). PEN is defined as a negative deflection in event related potential (ERP) due to cognitive conflict, as a consequence of a mismatch between perception and reality. Experimental results showed that the forces exchanged between robot and human during pHRC modulate the amplitude of PEN, representing different levels of cognitive conflict. We also found that PEN amplitude significantly decreases (mathrm {p} lt 0.05) when a mechanical resistance is being applied smoothly and more time in advance before an invisible obstacle, when compared to a scenario in which the resistance is applied abruptly before the obstacle. These results indicate that an earlier and smoother resistance reduces the conflict level. Consequently, this suggests that smoother changes in resistance make the interaction more intuitive.

During physical Human-Robot Interaction (pHRI) in industrial applications such as human-robot collaborative abrasive blasting, the operator often interacts with the robot using two hands, exchanging forces through handle bars. For the robot to provide appropriate assistance to the operator and for safe interaction, it would be benefficial for the robot to know the pose of the user. This problem is often challenging due to environmental factors, limited sensing capability in the environment and the robot, and redundancy of the human upper-limb. This paper presents experimental study on how two-hand interaction and force exchange affect the operators upper-limb pose, which can be characterized by swivel angle. The poses of ten subjects were recorded as they interacted with a collaborative robot. Differences in the adopted upper limb pose were analyzed with respect to factors such as unimanual versus bimanual operation, and the amplitude of interaction force between an operator and the robot. The results discovered that the the effect of bimanual operation on the upper limb pose differs between individuals and the magnitude of the force had a varying effect on the pose. The requirement of applying a force forward produced an overall lower swivel angle.

This paper describes how a team of final year mechatronic engineering students developed an autonomous robotic system intended to act as a tour guide during events such as University open days and explores the opportunities this project presented to extend their knowledge and skills. The specifications of the project required the system to localise and navigate autonomously within a known environment while avoiding collisions with any people or obstacles not included in the prior area map. In addition to these requirements, the system needed to locate humans as potential clients, approach and greet them, offer directions and if required take the guest on a guided tour of the university. While taking the subject Advanced Robotics the students were able to develop a functional first prototype of the system and carry out initial tests. Following the completion of the subject a small number of the students opted to continue working on the project developing a second prototype using the knowledge gained and further enhancing their learning experiences. While this project mainly involved integrating existing well known algorithms, software and hardware, it provided an excellent opportunity to enhance the mechatronic engineering skills of the students involved.

In physical Human-Robot Interaction (pHRI), knowing the pose of the operator is beneficial and may allow the robot to better accommodate the human operator. Due to a large redundancy in the human body, determining the pose of the human operator is difficult to achieve in unstructured environments especially in human-robot collaborative operations where the robot often occludes the human from vision-based sensors. This work presents an upper body pose estimation method based on exploiting known positions of the human operator's hands while performing a task with the robot. Upper body pose is estimated using upper limb kinematic models alongside sensor information and model approximations to produce solutions that are biomechanically feasible. The pose estimation method was compared to upper body poses obtained using a motion capture system. It was shown to be able to perform robustly with varying amounts of available information. This approach is well suited in applications where robots are controlled using well-defined interfaces such as handlebars, operating in unstructured environments.

© VDE VERLAG GMBH · Berlin · Offenbach In cooperative Human-Robot operations with physical contact, the human is generally in control while the robot assists the human. However, if the performance of the human were to decrease due to factors such as fatigue or distractions, there should be a mechanism that allows the robot to measure the performance of human operator and intervene in the interaction if needed. This becomes more important in physical Human-Robot Interactions such as cooperative grit-blasting, as the safety of the human may be affected if their performance decreases. In this work, a method for measuring the confidence of a robot in its human operator is presented. This method is then verified in a Human-Robot cooperative grit-blasting operation.

Kinematic singularity is a fundamental and well understood problem of robot manipulators, with many methods having been developed to ensure safe and robust operation in proximity to singularity. However little attention has been given to the scenario where the robot and human are working in physical contact to collaboratively perform a task. In such a scenario the feelings and impressions of the human operator should be considered when developing solutions for handling singularity. This work presents an experimental study comparing three modes of handling kinematic singularities with respect to the impressions of the human operator. Two of the modes are based on traditional Damped-Least-Squares. The third method uses an asymmetric damping behavior proposed as being well suited for applications involving physical human-robot interaction. The three modes are tested and compared by subjects performing a mock industrial task, and feedback from the subjects analyzed to identify the preferred mode. Results indicate that the choice of method used affects the user's impressions of the interaction, and the asymmetrical damping behavior can produce a preferred interaction experience with human operators during tasks.

© VDE VERLAG GMBH · Berlin · Offenbach This paper is concerned with identifying the orientation of the human hand relative to a cylindrical handlebar. In physical Human-Robot Interaction, a handlebar is commonly used as the point of contact between the human operator and the robot. Identifying the orientation of the operator’s hand provides the robot with additional information on how the operator interacts with the robot. A flexible sensor array composed of 160 pressure sensing cells was wrapped around a cylindrical handlebar. Grasping patterns of ten subjects was recorded. Support Vector Machine (SVM) and Bayesian Inference classifiers were implemented to identify the hand orientation of a subject relative to the handlebar. Principal Component Analysis (PCA) was used to reduce the number of features in the classification. Comparisons between the classifiers of SVM and Bayesian Inference, with/without PCA, were conducted for evaluating their accuracy. Two scenarios were used in the comparisons: in the first scenario, the training data and the test data were different but from the same subject; in the second scenario, the training data and the test data were from different subjects.