Being motivated by ceiling inspection applications via unmanned aerial vehicles (UAVs) which require close proximity flight to surfaces, a systematic control approach enabling safe and accurate close proximity flight is proposed in this work. There are two main challenges for close proximity flights: (i) the trust characteristics varies drastically for the different distance from the ceiling which results in a complex nonlinear dynamics; (ii) the system needs to consider physical and environmental constraints to safely fly in close proximity. To address these challenges, a novel framework consisting of a constrained optimization-based force estimation and an optimization-based nonlinear controller is proposed. Experimental results illustrate that the performance of the proposed control approach can stabilize UAV down to 1 cm distance to the ceiling. Furthermore, we report that the UAV consumes up to 12.5% less power when it is operated 1 cm distance to ceiling, which is promising potential for more battery-efficient inspection flights.

This paper presents an unmanned aerial vehicle (UAV) push recovery operation using model predictive control (MPC) and moving horizon estimation (MHE) in a symmetric manner. This utilization is motivated by the active use of UAVs, particularly for the contact based inspection of the surrounding's ceilings. To enable a physical interaction operation by an optimization-based algorithm, a primal-dual quadratic programming (QP) solver is structured for the MPC and MHE. The designed system consists of (a) an interaction model to be implemented both on the control and the estimation; (b) an integral action in the predictive controller; (c) a disturbance estimation by MHE to update the MPC. Consequently, the nominal MPC, the integral action in MPC, and the disturbance observer based MPC are compared for a UAV push recovery operation. The numerical investigations demonstrate the applicability of the proposed approach.

This note presents a preliminary study on a high-level model based task programming framework for secure multimodal human-robot interaction in industrial context. The case study of robotic polishing tasks, in particular the task of polishing the whole surfaces of objects of interest, is considered. Our main focus is on system modeling and optimal synthesis of surface coverage trajectories in robotic polishing tasks. Two heuristics are proposed to solve the optimal synthesis problem approximately, based on different considerations and assumptions. We then briefly discuss about polishing tasks other than coverage problem that are specifiable in the language of linear temporal logic and state space reduction heuristics based on learning from expert operators. Finally, we introduce a design of the specification input device for the task specification module.

This paper presents the design and implementation of a multimodal person-following system for a mobile telepresence robot. A color histogram matching and position matching algorithm was developed for a person-recognition function using Kinect sensors. Robot motion was controlled by adjusting its velocity according to the humans position in relation to the robot. The robot was able to follow the targeted person in various person-following modes, such as the back-following mode, the side-by-side accompaniment mode as well as the front-guiding mode. An obstacle avoidance function was also implemented using the virtual potential field algorithm.

The evolution of interactions between humans and robots has reached a stage close to where multiple robots can be robustly deployed. The domain of Urban Search and Rescue stands to benefit from such a capability. However, even autonomous robots must be supervised by humans. Yet, reasons such as economy, efficiency, added reliability as well as improving overall group capability and addressing payload constraints, make multiple robot deployment (MRD) an appealing possibility. Single-Human Multiple Robot Systems facilitate MRD while ensuring the supervision of robots deployed. However, the design of the single-human multiple-robot system requires consideration of key design attributes such as collective size, composition and structure as well as of the MRD issues of communication costs, computational costs, cognitive workload and situation awareness. The experiments to determine the effects of the key design attributes on the issues of MRD have been designed and are described in this paper as well.

To develop useful applications for the underwater robots, the first step is to study its performance, such as thrust force or swimming velocity. In order to avoid numerous parameter studies in evaluating its performance, the prediction model plays an important role. As fish's swimming includes the kinematic of its own body and the hydrodynamic interaction with the surrounding fluid, it is difficult to formulate a precise mathematical model by purely analytical approaches. This paper offers a semi-empirical method to model the performance of a BCF (body and/or caudal fin) biomimetic fish robot. By using a dimensional statistical method, a semi-empirical model for predicting the thrust force generated by a BCF oscillation swimming mode is derived. This model shows good prediction capability. The predicted results are in good agreement with the experiment data. Therefore, the proposed modeling method can be used to solve the engineering problem concerned without a complex theory derivation.

In this paper, the system design issues of the Propulsion and Control System of the ROV II are analyzed and addressed. The design concept, some of the upgraded features of the ROV II in comparison to ROV I and the unified pilot training and control system developed, will also be briefly discussed in this paper.

In this paper, Axiomatic Design (AD) theory was adopted for the design analysis of an underactuated Remotely Operated Vehicle (ROV) system and its subcomponents. The system design issues of the Propulsion and Control System of the ROV II are analyzed and addressed based on the Independence Axiom methodology. The top-level Functional Requirements (FRs) for the thruster design and configuration are identified and its corresponding Design Parameters (DPs) are also presented.

An eyeball-class remotely operated vehicle (ROV) is currently being developed by the Robotic Research Centre (RRC), in the Nanyang Technological University (NTU). From the experience of the first generation single-cylinder vehicle, RRC ROV I, modified from the Super Safir, originally manufactured in France, a "Twin Barrel" ROV is being developed-RRC ROV II. This paper discusses the modelling and control of ROV II in comparison to ROV I. Both vehicles are underactuated, utilising identical thrusters and with similar thruster configurations. ROV II is larger in size, as compared to ROV I, so as to facilitate its need to mount additional sensors and equipment. The additional sensors and improved intelligent control significantly improved the performances of ROV II. This paper briefly describes and demonstrates the PD station-keeping controllers designed for both ROV I and II. A comparison of simulation results, for vehicle yawing at 60/spl deg/ and 90/spl deg/, are also shown.

Underwater robotic vehicles (URVs), generally, consist of two main classes, namely the remotely operated vehicles (ROVs) and the autonomous underwater vehicles (AUVs). In this paper, a simplified model of a thruster and a general equation of rigid body motion for an underactuated ROV are presented. Then the nonlinear and coupling effects on the ROV, from its shape and design, are derived and presented. Lastly, the steady-state nonlinear behaviour of the thrusters observed are used to design and develop a simple PID controller. Simulation results obtained demonstrated the capability of the controller in the regulating of the desired amount of thrust while maintaining a relatively small degree of steady-state error.

A method of free gait generation for quadrupeds is presented by introducing the primary/secondary gait. The primary gait is a fixed sequence of leg transfers with modified leg-ends kinematic limits according to the obstacle presence, while the secondary gait is a flexible gait which is generated to adjust the leg-end position. Using the proposed method, a machine tends to move with primary gait during the motion. When the primary gait cannot move the vehicle, the secondary gait is adopted to serve as a complement of the primary gait. Four constraints, namely, stability constraint, kinematic constraint, sequential constraint and neighboring constraint are considered when gaits are generated. The influence of the obstacle is taken into account to modify the gait parameters. With the proposed method based on primary/secondary gait, it is expected to improve the efficiency of free gait generation while maintaining the mobility of the vehicle. Simulation results are given to demonstrate the efficiency of the proposed algorithm.

This paper proposes a system that utilize a large number of autonomous intelligent "micro" systems to search and identify pre-defined objects. Robotic agents would be deployed in large numbers to collectively search an unknown area. The agents will search for objects of interest and upon locating the objects, either secure itself to that object and await further instruction from a base station or leave behind a marker indicating the object's location so that the object can be acquired later on. Simulation experiments on the deployment of these robotic agents have been conducted and the results of some of these experiments are discussed.