Intelligent robot companions contribute significantly in improving living standards in the modern society. Therefore human-like decision making skills are sought after during the design of such robots. On the one hand, such features enable the robot to be easily handled by its non-expert human user. On the other hand, the robot will have the capability of dealing with humans without causing any disturbance by the robot’s behavior. Mimicing human emotional intelligence is one of the best and reasonable ways of laying the foundation for robotic emotional intelligence. As robots are widely deployed in social environments, perception of the situation or intentions of a user prior to an interaction is required to be proactive. Proactive robots are required to understand what is communicated by the human body language prior to approaching a human. Social constraints in an interaction could be demolished by this assessment in this regard. In this review, we incorporate various findings of Human-Robot Interaction, Social Robotics and Psychophysiology to assess intelligent systems which were capable of evaluating the emotional state of humans prior to an interaction. Secondly, we identify the cues and evaluation techniques that were utilized by such intelligent agents to simulate and evaluate the suitability of a proactive interaction. Available literature has been evaluated to distinguish limitations of existing methods and suggest possible improvements. These limitations, guiding principles to be adhered to and suggested improvements are presented as an outcome of the review.

Ports and container terminals play an important role in global logistic system. Handling containers inside container terminals and rail terminals are mostly carried out by rubber tire gantry (RTG) cranes. These cranes have quite different power profile compared to hybrid vehicles. They have a broad power demand, varying from 10kW to 350kW, 170kW regenerating power and a maximum of 30kW auxiliary power. The high peak demand due to acceleration of hoist drivetrain determines the prime mover (diesel generator) capacity. This capacity is highly over-rated when comparing with crane’s average power demand. Such power profiles having high peak power to average power ratio can be supplied through hybrid systems which can downsize the diesel generator, improve fuel efficiency, reduce 2 CO emissions and reduce maintenance cost. In this study a hybrid energy source is presented for a RTG crane. The hybrid energy source comprises of a Lithium ion battery bank, supercapacitor (SC) bank connected to DC-link through bi-directional DC/DC converters and a downsized variable speed diesel generator (VSDG) connected to DC-link through an active rectifier. The narrow band operation of the battery bank helps to increase the healthy life of the battery system reducing risk due to unhealthy conditions during faults and abnormal situations. In this paper, sizing of a hybrid energy system controlled by a state machine controller is presented. Unlike traditional optimization-based sizing techniques, unique features of demand profile, operating environment, system redundancy, backup operation, readily available components and specific features of state machine controller were highly considered which differentiate the method of sizing from others.

This paper evaluates possibility of using a new hybrid system based on variable speed diesel generator (VSDG), Li-ion battery bank and supercapacitor bank (SC) for a rubber tire gantry crane (RTGC) used in container terminals. Existing commercial hybrid systems face difficulties producing high efficiencies, higher life span, and lower initial investment cost due to inheriting characteristics of batteries and supercapacitors. In the proposed power system, a variable speed diesel generator act as the principal energy source, while a Li-ion battery bank and SC bank act as an energy storage system. The battery supports the diesel generator during steady demand and further, it absorbs a part of energy during regeneration. The energy management strategy, control the power flow from different sources while maintaining battery state of charge (SOC) level within a narrow band. Unlike most battery systems, this narrow band operation of battery system increases its life span while reducing capacity fade. The originality of this study can be emphasized from this narrow band SOC control technique. Simulation results for real operational load cycles are presented showing a stable system operating under defined current limits which can enhance lifetime of battery system and increase fuel saving by downsizing 400 kW constant speed diesel generator to 200 kW VSDG.

Elderly and disabled population is rapidly increasing. It is important to uplift their living standards by improving the confidence towards daily activities. Navigation is an important task, most elderly and disabled people need assistance with. Replacing human assistance with an intelligent system which is capable of assisting human navigation via wheelchair systems is an effective solution. Hand gestures are often used in navigation systems. However, those systems do not possess the capability to accurately identify gesture variances. Therefore, this paper proposes a method to create an intelligent gesture classification system with a gesture model which was built based on human studies for every essential motion in domestic navigation with hand gesture variance compensation capability. Experiments have been carried out to evaluate user remembering and recalling capability and adaptability towards the gesture model. Dynamic Gesture Identification Module (DGIM), Static Gesture Identification Module (SGIM), and Gesture Clarifier (GC) have been introduced in order to identify gesture commands. The proposed system was analyzed for system accuracy and precision using results of the experiments conducted with human users. Accuracy of the intelligent system was determined with the use of confusion matrix. Further, those results were analyzed using Cohen’s kappa analysis in which overall accuracy, misclassification rate, precision, and Cohen’s kappa values were calculated.

To generate context-aware behaviors in robots, robots are required to have a careful evaluation of its encounters with humans. Unwrapping emotional hints in observable cues in an encounter will improve a robot’s etiquettes in a social encounter. This article presents an extended human study conducted to examine how several factors in an encounter influence a person’s preferences upon an interaction at a particular moment. We analyzed the nature of conversation preferred by a user considering the type of conversation a robot could have with its user, having the interaction initiated by the robot itself. We took an effort to explore how such preferences differ as the factors present in the surrounding alter. A social robot equipped with the capability to initiate a conversation is deployed to conduct the study by means of a wizard-of-oz (WoZ) experiment. During this study, conversational preferences of users could vary from “no interaction at all” to a “long conversation.” We changed three factors in an encounter which can be different from each other in each circumstance: the audience or outsiders in the environment, user’s task, and the domestic area in which the interaction takes place. Conversational preferences of users within the abovementioned conditions were analyzed in a later stage, and critical observations are highlighted. Finally, implications that could be helpful in shaping future social human-robot encounters were derived from the analysis of the results.