This paper presents the development of a 3D printed humanoid robotic hands of SignBot, which can perform Malaysian Sign Language (MSL). The study is considered as the first attempt to ease the means of communication between the general community and the hearing-impaired individuals in Malaysia. The signed motions performed by the developed robot in this work can be done by two hands. The designed system, unlike previously conducted work, includes a speech recognition system that can feasibly integrate with the controlling platform of the robot. Furthermore, the design of the system takes into account the grammar of the MSL which differs from that of Malay spoken language. This reduces the redundancy and makes the design more efficient and effective. The robot hands are built with detailed finger joints. Micro servo motors, controlled by Arduino Mega, are also loaded to actuate the relevant joints of selected alphabetical and numerical signs as well as phrases for emergency contexts from MSL. A database for the selected signs is developed wherein the sequential movements of the servo motor arrays are stored. The results showed that the system performed well as the selected signs can be understood by hearing-impaired individuals.

Agriculture production is a critical human intensive task, which takes place in all regions of the world. The process to grow and harvest crops is labor intensive in many countries due to the lack of automation and advanced technology. Much of the difficult, dangerous and dirty labor of crop production can be automated with intelligent and robotic platforms. We propose an intelligent, agent-oriented robotic team, which can enable the process of harvesting, gathering and collecting crops and fruits, of many types, from agricultural fields. This paper describes a novel robotic organization enabling humans, robots and agents to work together for automation of gathering and collection functions. The focus of the research is a model, called HARMS, which can enable Humans, software Agents, Robots, Machines and Sensors to work together indistinguishably. With this model, any capability-based human-like organization can be conceived and modeled, such as in manufacturing or agriculture. In this research, we model, design and implement a technology application of knowledge-based robot-to-robot and human-to-robot collaboration for an agricultural gathering and collection function. The gathering and collection functions were chosen as they are some of the most labor intensive and least automated processes in the process acquisition of agricultural products. The use of robotic organizations can reduce human labor and increase efficiency allowing people to focus on higher level tasks and minimizing the backbreaking tasks of agricultural production in the future. In this work, the HARMS model was applied to three different robotic instances and an integrated test was completed with satisfactory results that show the basic promise of this research.

This research focuses on controlling robots and their formations using rough mereology as a means for spatial reasoning. The authors present the state of the art theory behind path planning, robot cooperation domains and ways of creating robot formations. Furthermore, the theory behind Rough Mereology as a way of implementing mereological potential field based path creation and navigation for single and multiple robots is described. An implementation of the algorithm is shown in simulation using RoboSim simulator. Five formations are tested (Line, Rhomboid, Snake, Circle, Cross) along with three decision systems (First In, Leader First, Horde Mode) as compared to other methods.

This study evaluates the efficacy of a class robust control scheme namely active force control in performing a joint based trajectory tracking of an upper limb exoskeleton in rehabilitating the elbow joint. The plant of the exoskeleton system is obtained via system identification method whilst the PD gains were tuned heuristically. The estimated inertial parameter that enables the AFC disturbance rejection effect is attained by means of a non-nature based metaheuristic optimisation technique known as simulated Kalman filter (SKF). It was demonstrated from the present investigation that the proposed PDAFC scheme outperformed the classical PD algorithm in tracking the prescribed trajectory both in the presence and without the presence of disturbance attributed by the mannequin limb weights (1 kg and 1.5 kg) that mimics the weight of actual human limb weight. Therefore, it is apparent from the results obtained from the present study that the proposed control scheme, i.e., PDAFC is suitable for the application of exoskeleton for stroke rehabilitation.

Various studies have been performed to coordinate robots in transporting objects and different artificial intelligence algorithms have been considered in this field. In this paper, we investigate and solve Multi-Robot Transportation problem by using a combined auction algorithm. In this algorithm each robot, as an agent, can perform the auction and allocate tasks. This agent tries to clear the auction by studying different states to increase payoff function. The algorithm presented in this paper has been applied to a multi-robot system where robots are responsible for transporting objects. Using this algorithm, robots are able to improve their actions and decisions. To show the excellence of the proposed algorithm, its performance is compared with three heuristic algorithms by statistical simulation approach.