Assistive robots are developed for supporting the daily activities of human beings to uplift the living standards. Assistive robots should be friendly, reliable, active, comprehensible and capable of creating interactive conversations with users in order to be a friendly companion for the human. Humans tend to include uncertain terms related to directions and distances to describe or express ideas. Furthermore, an assistive robot should be capable of analyzing and understanding the numerical meaning of uncertain terms for the purpose of creating a cognitive map in order to build friendly interactions between the user and the robot. Moreover, this paper proposes a method to identify the spatial relation between the objects in a given environment and describes such environments using uncertain terms related to spatial information based on a cognitive map created by the proposed system while having interactive conversations with the user. Conversation Management Module (CMM) and Spatial Information Processor (ISP) and Cognitive Map Creator (CMC) have been introduced in order to create interactive conversations while processing the uncertain information based on a cognitive map. Capabilities of the robot has been demonstrated and validated from the experimental result.

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.

Assistive robots are developed to uplift living standards of human being. An assistive robot needs to be friendly, reliable, and understandable in order to be a human like companion. A robot should be able to understand its user effectively and the robot should be able to expand its knowledge based on experience gain from activities or conversations. Therefore the assistive robot should have a memory and a knowledge base regarding objects that it comes across in daily activities. It should be able to process data and understand relationships between attributes of objects to self-understand the need of a user. Therefore this paper propose a method to create an object memory and a knowledge base to understand relations between objects attributes and create a cognitive map to enhance interaction between human and robot which will help to make a robot more human-friendly. Conversation Management Module (CMM), Attribute Analyzing Module (AMM) and Object Knowledge Base (OKB) have been introduced in order to create a cognitive map of objects. Capabilities of the robot have been demonstrated and evaluated from experimental results.

The continuous rising of the elderly/disabled population has created a requirement for assistive robotics devices to counter the lack of trustworthy servants. Intelligent Wheelchairs are developed for that particular purpose. Intelligent Wheelchairs differ depending on the interactive modality and most commonly found modalities are speech-controlled. Since these are assistive devices that need to act as human companions, it is necessary to have a dialogue between the device and the user. Even though the wheelchair is fully automated, the user should have control over it at some point. However, this exchange of control should be intelligent and transitions need to be executed in order to safeguard the user. Therefore the purpose of this paper is to propose an intelligent system that would navigate an intelligent voice-controlled wheelchair facilitating the intelligent exchange of control between the user and the wheelchair. This control is not simultaneous and one can override the other only when navigation could lead to collisions. In the proposed method, users can control the wheelchair using fixed vocal commands, and execution of those commands will be performed using the spatial and control parameters. Control of the wheelchair will be exchanged between the user and the wheelchair itself considering specific parameters such as obstacle distance, collision time, the velocity of the wheelchair among others. User control mode has 5 definite vocal commands with classifiers to identify any navigation command into the command model and considers uncertain terms such as ‘little’ and ‘hard’ for ‘Turn’ commands. Command classification had produced a Cohen’s Kappa value of 0.9462 and the classifier for the uncertain terms had produced a Cohen’s Kappa value of 0.7325. Both were acceptable values for those particular classifications. As per the experiment results, the proposed system reduced the vocal command frequency and risk of collisions through proper control of the velocity levels and intelligent exchange of control at given locations.

This paper is focused on designing and development of interactive service robot arm for object manipulation in domestic environment. The neck mechanism is developed and it is integrated with the arm and the vision sensor. The robot is capable of identifying user hand gestures, identifying the objects according shape and color and understanding user voice commands. The robot is intended to perform pick and place operation according to the user voice and gesture command. Inverse kinematics analysis has been done using a geometric approach. This paper proposes an iterative methodology for generating inverse kinematic solution for robot arm. The image and voice acquisition is done using the Kinect V2 sensor. This research reveals a novel method of the use of capabilities of Kinect.

Assitive robot technology is rapidly increasing in the modern era. Robot companions become more human-like over the time with the advancement of technology. Personal assitive robot applications are escalated to a vast extend featuring to medical robots, supportive robots for disabled or elderly people as companions. It is essential to possess the capabilities in relevance to navigation in an unknown environment within the assistive robot. In order to posses such competencies the robot should be able to create spatial cognitive maps and virtual maps. With aid of those constructed maps and the actual spatial map obtained by the sensory inputs such as laser scanner, robot should be able to identify the objects without perceiving any visual information. Therefore this paper proposes a method to use the spatial cognitive map to create virtual visualization of previously unknown environment based on spatial data conveyed through interactive conversation with the user and link that information with the actual spatial map obtained from the laser scanner to identify the position of objects in the domestic environment. The Conceptual map creator(CMC) and Virtual Spatial Map Link Creating (VISMALC) module have been introduced in order to combine cognitive virtual maps with the actual spatial map to identify objects without using any visual information. Capabilities of the robot have been demonstrated and validated from the experimental results.