Unmanned Systems

We consider the problem of solving hybrid discrete-continuous Markov Decision Processes (MDPs) that are often encountered in computing optimal policies for complex multi-agent missions with both continuous vehicle dynamics and discrete mission-state transition models, in the presence of potential health degradations and failures of individual agents. A comprehensive Health Aware Planning (HAP) framework is proposed that establishes a feedback between mission planning and vehicle-level learning-focused adaptive controllers through online learned own models of agent health and capabilities. The HAP framework accounts for predicted likelihood of vehicle health degradations captured through probabilistic state-dependent models that are integrated into the MDP formulation. This proactive ability to anticipate health degradation and plan accordingly enables the HAP approach to consistently outperform planners that change the policies only after failures have occurred (reactive planners). The approach is tested on a large-scale (≈ 10¹⁰ state–action pairs) long-duration (persistent) target tracking scenario using a novel on-trajectory planning algorithm, and demonstrated to sustain higher mission performance by reducing the number of failures and re-assessing Unmanned Aerial Vehicle (UAV) capabilities.

Valuable energy resources of sensor network should be utilized wisely to prolong network's lifetime. Clustering technique helps wireless sensor network (WSN) to enhance its lifetime by reducing energy consumption on every individual sensor node in the network. In multi-hop data forwarding model, difference in energy consumption among cluster heads (HS) causes hot-spot problem in the network. While data is being transferred, the CH close to base station are burdened with heavy relay traffic from several data routes and tend to die early. Unequal clustering avoids this hot-spot problem by establishing different sized clusters at various levels in the network. Since unequal clustering technique does not control number of CHs it creates, it forms huge number of clusters in the network. This increases hop count between source and destination, and leads to impose more over head on each data forwarding route in the network. Also, rapid variation in cluster size causes imbalance in energy dissipation among clustered nodes in the network. This uneven energy consumption influences network performance and lifetime. In this paper, we present an energy-efficient hybrid clustering mechanism for wireless sensor network using equal and unequal clustering techniques to create limited number of clusters in varied sizes at various level of the network. This avoids hot-spot problem with minimum hop count between the source and destination and achieves uniform energy dissipation between intra- and inter-cluster communication. Simulation results show that the proposed clustering mechanism balances the energy consumption among clusters with its hybrid cluster formation mechanism and elevates sensor network lifetime.

The paper proposes a nonlinear control approach for the underactuated hovercraft model based on differential flatness theory and uses a new nonlinear state vector and disturbances estimation method under the name of derivative-free nonlinear Kalman filter. It is proven that the nonlinear model of the hovercraft is a differentially flat one. It is shown that this model cannot be subjected to static feedback linearization, however it admits dynamic feedback linearization which means that the system's state vector is extended by including as additional state variables the control inputs and their derivatives. Next, using the differential flatness properties it is also proven that this model can be subjected to input–output linearization and can be transformed to an equivalent canonical (Brunovsky) form. Based on this latter description the design of a state feedback controller is carried out enabling accurate maneuvering and trajectory tracking. Additional problems that are solved in the design of this feedback control scheme are the estimation of the nonmeasurable state variables in the hovercraft's model and the compensation of modeling uncertainties and external perturbations affecting the vessel. To this end, the application of the derivative-free nonlinear Kalman filter is proposed. This nonlinear filter consists of the Kalman Filter's recursion on the linearized equivalent model of the vessel and of an inverse nonlinear transformation based on the differential flatness features of the system which enables to compute estimates for the state variables of the initial nonlinear model. The redesign of the filter as a disturbance observer makes possible the estimation and compensation of additive perturbation terms affecting the hovercraft's model. The efficiency of the proposed nonlinear control and state estimation scheme is confirmed through simulation experiments.

In this paper, we present the development of a reconfigurable hybrid unmanned aerial vehicle (UAV): U-Lion [Ang et al., 11th IEEE Int. Conf. Control Automation (ICCA), pp. 750–755]. U-Lion is a small-scale UAV that is capable of vertical takeoff and landing (VTOL) and fixed-wing Cruise modes through its unique mechanical design. Mainly built with carbon fiber and expanded polyolefin (EPO) foam, U-Lion is equipped with an array of avionic components which enable stable control of the UAV both in VTOL and Cruise modes. It was employed by the National University of Singapore (NUS) Unmanned System Research Group to participate in the 2013 UAV Grand Prix (UAVGP) competition held in Beijing, China. Its design adopts a reconfigurable wing and a tail-sitter structure, which combines the advantages of a fixed-wing plane and a rotor helicopter effectively. U-Lion could transit from vertical takeoff to a hovering stage before flying in Cruise mode to realize efficient long duration flight. The propulsion of U-Lion comes from a self-fabricated contra-rotating motor fixed on a gimbal mechanism which can change the direction of the motor for the required thrust. This thrust-vectored propulsion system primarily provides control in the VTOL mode but also enhances flight capabilities in Cruise mode. The maximum thrust provided by the motor can be as high as 40 N and it provides six degree of motion controls in VTOL mode. U-Lion has a few special internal designs to empower its capabilities: (1) Reconfigurable wings allow the U-Lion to adapt to different flying modes. (2) Adaptive center of gravity (CG) by adjusting the battery position to fulfill the different requirements of CG for VTOL mode and Cruise mode. (3) Unique contra-rotating thrust-vectored propulsion system. The detailed design and implementation procedure have been presented in this paper along with our computational fluid dynamics (CFD) simulation results, real flight tests and competition performance.

A body sensor network system has been developed for ubiquitous health monitoring of multiple mobile subjects, which is referred to as UbiHealth. On the body, there are micro-sensors to capture physiological signals of electrocardiography (ECG), blood pressure, respiration and temperature, as well as context information of activity and position. Sensors are coordinated by an on-body gateway, where data are collected, pre-processed and wirelessly sent to the server. The server receives, stores and processes signals from multiple gateways, providing overview of those subjects on a local map, and real-time health status of individual subjects. The application scenarios include, for example, health monitoring for rescue team members in a hazard, and elderly health monitoring in a community.