Understanding user perceptions is particularly important in developing social robots, which tend to have a high degree of interaction with humans. However, psychometric measures of robot acceptability have only recently started to become available. The present critical review outlines the psychometrically validated questionnaires to measure social acceptability factors related to social robots. Using an iterative search strategy, articles were identified that reported on the development of such questionnaires as well as information about their psychometric properties. Six questionnaires were identified that provide researchers with options varying in length, content, and factor structure. Two of these questionnaires inquire about attitudes and anxieties related to robots, while two others capture a larger range of attitudes that extends to positive and neutral aspects as well. One of the questionnaires reviewed here was specific to inquiring about ethical issues related to the use of social robots for therapy with children with autism, and the last one was designed to provide an assessment of expectations of participants prior to interacting with a robot. Overall, the use of robot acceptability measures is still relatively new, and further psychometric work is necessary to provide confidence in the validity and reliability of these scales.

This paper puts forward the potential for designing a parrot-inspired robot and an indirect teaching technique, the adapted model-rival method (AMRM), to help improve learning and social interaction abilities of children with autism spectrum disorder. The AMRM was formulated by adapting two popular conventional approaches, namely, model-rival method and label-training procedure. In our validation trials, we used a semi-autonomous parrot-inspired robot, called KiliRo, to simulate a set of autonomous behaviors. A proposed robot-assisted therapy using AMRM was pilot tested with nine children with autism spectrum disorder for five consecutive days in a clinical setting. We analyzed the facial expressions of children when they interacted with KiliRo using an automated emotion recognition and classification system, Oxford emotion API (Application Programming Interface). Results provided some indication that the children with autism spectrum disorder appeared attracted and happy to interact with the parrot-inspired robot. Short qualitative interviews with the children’s parents, the pediatrician, and the child psychologist who participated in this pilot study, also acknowledged that the proposed parrot-inspired robot and the AMRM may have some merit in aiding in improving learning and social interaction abilities of children with autism spectrum disorder.

A two-wheeled robotic wheelchair (TWRW) has a better manoeuvrability than a conventional four-wheeled wheelchair. However, it is not statically stable near the upright posture or a posture desired by the rider, and an active stability controller is required. Stability control becomes more challenging when a TWRW is also required to move in a desired direction. To rely on wheels’ motions to achieve both stability and direction control tend to impose a large burden on the wheels’ driving motors or other types of actuators in terms of their driving torque and power consumption. Various disturbances in the system also affect the performancerequiredrequired controller. To solve these problems, this paper presents a stability and direction controller based on the motion of a pendulum-like movable mechanism added to assist the wheels to produce control actions. The dynamic model of the TWRW is established through the Euler-Lagrange formulation in which the disturbances caused by model uncertainties and rider’s motion are considered. A robust second-order sliding mode control is then developed for the stability and the direction control of a TWRW. Simulation results are presented to validate the effectiveness of the proposed method.

Offshore aquaculture fish farming faces labor shortage, safety, productivity and high operating cost issues. Unmanned underwater vehicles (UUVs) are being deployed to mitigate these issues. One of their applications is the fish net-pen visual inspection. This paper aims to develop and simulate with high-fidelity several trajectory tracking control schemes for a UUV to visually inspect a fish net-pen in a standard task scenario in offshore aquaculture under 0.0m/s, 0.5m/s and 0.9m/s underwater current disturbances. Three controllers, namely (1) Proportional-Derivative control with restoring force & moment compensation (Compensated-PD), (2) Proportional-Integral-Derivative control with restoring force & moment compensation (Compensated-PID), and (3) computed torque (or) inverse dynamics control (CTC/IDC) were conducted on a 6 degrees-of-freedom (DoF) BlueROV2 Heavy Configuration dealing with 12 error states (pose and twist). A standard task scenario for the controllers was formulated based on the Blue Endeavour project of the New Zealand King Salmon company located 5 kilometres due north of Cape Lambert, in northern Marlborough. This simulated experimental study gathered and applied many available and physically quantifiable parameters of the fish farm and a UUV called BlueROV2 Heavy Configuration. Results show that while utilizing the minimum thrust, CTC/IDC outperforms Compensated-PID and Compensated-PD in overall trajectory tracking under different underwater current disturbances. Numerical results measured with root-mean-square-error (RMSE), mean-absolute-error (MAE) and root-sum-squared (RSS) are reported for comparison, and simulation results in the form of histograms, bar charts, plots, and video recordings are provided. Future work will explore into advanced controllers, with a specific emphasis on energy-optimal control schemes, accompanied by comprehensive stability and robustness analyses applied to linear and nonlinear UUV models.

Potential field method is widely used for path planning of mobile robots, which in many applications, is to find the direction of the robot when the target and obstacles are stationary. In this paper, it is extended to plan the robot's speed as well as its path (direction) when the robot tracks a moving target among obstacles. Within the general steepest descent control and Lyapunov frameworks, the direction and the speed of the robot are determined by the velocities of the target or the obstacles and the relative position between the robot and the environment. The speed-direction relation , and the effects of planning parameters on the planing results are also analyzed. Simulation studies are provided to verify the effectiveness of the proposed approach in various situations.

This paper presents an extensive review of "bio-inspired therapeutic pet robots", an emerging class of social robots deployed for therapeutic scenarios with a wide range of human stakeholders from elderly to autistic children. We begin by covering prominent literature that studies the roles and benefits of biological pets in therapeutic situations. We then present an overview on biologically inspired therapeutic pet robots. Finally, we discuss the needs and future potential for a parrot inspired robot and related research challenges.