- Onboard 3D Navigation System for UAVs in Unknown and Realistic Indoor Environments
- Navigation and Control of MAVs in Indoor and Outdoor Cluttered Environments (2014-2016)
- Fruit Dove UAV Control (2014-2016)
- Optimal Coverage and Surveillance Using Cooperative Planning and Control of UAVs
- Investigation of Navigation Systems for Unmanned Aerial Vehicles in Outdoor Cluttered Environments (2012-2015)
- Special Project for DARPA UAVForge Competition (2012)
- Development of Autonomous Micro Aerial Vehicles (2011-2013)
- Optimal Motion Planning in Obstacle-Rich Environment (2010-2013)
- Development of a Sophisticated 3D Indoor Navigation System for UAVs (2009-2012)
- Development of Multi-UAV Test Beds & Vision-Based Navigation and Motion Coordination (2009-2013)
- Cooperative Reconfiguration Control for Multiple Unmanned Air Vehicles (2008-2011)
- Technologies to Lead Unmanned Air Vehicles via Manned Air Vehicles (2007-2010)
- Nonlinear Flight Model Identification & Control for VTOL UAV in Formation (2007-2009)
- Nonlinear Control of Unmanned Flying Vehicles (2003-2007)
Onboard 3D Navigation System for UAVs in Unknown and Realistic Indoor Environments
Funding Source: Defence Science & Technology Agency, 2016-2020, S$1,668,000
This project aims to develop a comprehensive and implementable GPS-less navigation system for unmanned aerial vehicles. The system is expected to be fully integrated on-board, light-weight, real-time, robust and without unrealistic assumptions. To achieve the target, the following four problems need to be investigated in depth, where innovation and optimization have to be applied on both hardware and software aspects: (1) Multi-sensory data fusion; (2) 3D real-time simultaneous localization and mapping; (3) Dynamic 3D Path planning in unknown environments; and (4) Multi-UAV cooperative control. back to top
Navigation and Control of MAVs in Indoor and Outdoor Cluttered Environments
Funding Source: DSO National Laboratories, 2014-2016, S$492,000
In this project, we aim to investigate the problem of intelligent navigation of MAVs in realistic indoor and outdoor cluttered environments without global referencing resources. We intend to achieve this target by systematic integration of achievements of the following four tasks: (1) MAV platform design with small size but sufficient payload and endurance; (2) Robust and precise ego-motion estimation in indoor and outdoor cluttered environments via onboard relative sensors; (3) Navigation in cluttered environments with obstacle avoidance; and (4) Smooth navigation transition between indoor and outdoor environments. The proposed algorithms and methods will be tested and verified using actual MAV platforms. Multiple indoor and outdoor scenarios will be defined or developed for the developed MAVs to demonstrate its functionalities. We have also planned to demonstrate and test the developed algorithms and platforms at international and local events. back to top
Funding Source: NUS Temasek Laboratories, 2014-2016, S$150,000
This portion of work also requires the controlled UAV to demonstrate the capability of visual servoing, provided that some natural landmarks are within the view of the UAV onboard camera. During visual servoing, the controlled UAV should be able to perform stable ascending, descending and yawing by visually tracking the selected feature. In addition, the capabilities of the controlled UAV to perform automatic perching on a selected rooftop location (without artificial landmark) and landing on a moving platform (with man-made but discreet landmarks) are to be developed. All these UAV capabilities should be demonstrated in a fully autonomous fashion and without using any GPS device. back to top
Funding Source: Temasek Defence Systems Institute, 2013-2016, S$300,000 (with C Xiang ~ PI, T H Lee, C Chen, W Kang and O. Yakimenko)
We propose a hierarchical approach utilizing the optimization methods, graph theory approaches and image processing techniques to unify the planning and control levels. The optimal planning algorithms equip the team of UAVs with the distributed decision making subject to spatio-temporal performance and constraints. The information acquired from the distributed measurement and optimal coverage flights allocate individual tasks for UAVs to be accomplished by target/trajectory tracking algorithm in the real-time control layer. The techniques proposed in this project are important in many applications, such as air defense and urban application such as traffic control and crisis management. For example, multiple UAVs work as a team for cover a wide area with accurate image measurement and real-time control of the traffic flow. Another example would be recognition and tracking of a suspect/enemy.back to top
Funding Source: Temasek Defence Systems Institute, 2012-2015, S$300,000 (with T H Lee, C Chen and O. Yakimenko)
In this project, we propose to develop an advanced outdoor navigation system to explore the theories and technologies that enable UAVs to realize autonomous navigation in outdoor cluttered environments, especially forest. To realize the navigation system, several main topics need to be investigated, including advanced sensing technologies, sophisticated navigation approaches, as well as simultaneous localization and mapping (SLAM) techniques. A variety of sensing technologies are considered in the project, including electro-optical sensors (EO), light detection and ranging sensors (LIDAR), inertial measurement units (IMU), the global positioning system (GPS), and so on. The fusion techniques are investigated to combine measurements of these sensing technologies to realize obstacle detection and robust navigation even at the loss of GPS signal. In addition, based on obstacle detection, dynamic path planning scheme is studied to determinate a safe path for the UAV to achieve required missions. Moreover, special attention is paid to the simultaneous localization and mapping in largescale environments. It is important to rigorously analyze the theories and techniques on how to integrate a local map into a global map and on how to smoothly transform between these different navigation conditions. back to top
Funding Source: DSO National Laboratories, January-May 2012, S$230,040
The DARPA UAVForge Challegence is a Defense Advanced Research Projects Agency (DARPA) and Space and Naval Warfare Systems Center Atlantic (SSC Atlantic) collaborative initiative to design, build and manufacture advanced small unmanned air vehicle (UAV) systems. The NUS GremLion team has been selected as a finalist for the UAVForge Fly-Off competition. In order to achieve the goals of the UAVForge, a navigation system for UAVs has to be developed to cope with the challenges caused by the high demanding requirements, such as long range navigation, vehicle following, obstacle avoidance, and rooftop landing, and the constraints of the UAV platform with limited payload and energy.
The main tasks of this project involve the hardware construction of an embedded vision aided navigation system, development and realization of the required mission algorithms, and implementation of the proposed system on actual UAV platforms. First, the vision aided navigation system is developed, which includes multiple sensors, a vision processing unit, data and video links, etc. Second, mission algorithms are investigated and implemented on the navigation system. The mission algorithms consist of vision-aided obstacle detection and avoidance, and vision-based autonomous rooftop landing and target following. Finally, the proposed navigation system and mission algorithms will be tested by using the UAV platform provided by DSO in real flight. back to top
Funding Source: DSO National Laboratories, 2011-2013, S$625,800 (with T. H. Lee and P. Tan)
In this proposed project, we take the challenge to develop an ultra compact micro aerial vehicle (MAV) with about 100 g in mass and 8 minutes flight time. The developed MAV is able to safely navigate through indoor environment and complete autonomously necessary flight missions. The main tasks of this project involve the construction of the custom-made aerial vehicles, development of an embedded avionic system, modeling of the MAV system and design of robust flight controllers. The systematic design methodology and innovative technologies will be utilized to optimize the vehicle itself and the avionic system to fulfill the required specifications. A robust flight controller will be designed and implemented for the MAV in terms of the identified model. The entire system of the MAV will be tested in the actual flight. back to top
Funding Source: Temasek Defence Systems Institute, 2010-2013, US$148,521 (with W Kang from NPS ~ PI)
The main objective of this project is to develop theory and algorithms of computational methods for optimal UAV trajectory planning in obstacle-rich environment. The proposed research is to explore the application of the state-of-the-art in computational optimal control to optimal UAV trajectory planning in a challenging environment with obstacles, a problem that cannot be satisfactorily solved using existing technologies. Autonomous UAV trajectory planning that optimizes performance has significant potential in various applications of military, anti-terrorist, and civilian operations. back to top
Funding Source: Temasek Defence Systems Institute, 2009-2012, $300,000 (with H Lin and T H Lee)
The recent success of unmanned aerial vehicles (UAVs) in the military and civilian applications has brought great interests in developing new generation of UAVs that are capable of flying fully autonomously in an unknown environment, especially in an indoor environment. A UAV equipped with a sophisticated indoor navigation system is expected to be an ideal platform for a wide range of military and civilian applications. To realize such applications, the indoor navigation system for UAVs has to be developed to cope with the challenges caused by the complicated indoor environment (such as scattered obstacles and denied reception of GPS signals) and the constraints of the UAV platform (such as its instability and limited payload).
In this proposed project, we aim to develop a 3D indoor navigation system, which is able to aid UAVs to safely navigate through the unknown and complicated indoor environment and complete autonomously necessary flight missions. The main tasks of this project involves hardware construction of an embedded navigation system (including multiple sensors, data and image processing units, and data and video links), the development and realization of robust 3D indoor navigation algorithms, and the tests of the system on the actual UAV platforms. In the proposed navigation scheme, sophisticated machine vision algorithms such as robust feature extraction, an optical flow method and stereo vision approach are utilized to stabilize the UAVs and avoid the obstacles. The output of multiple sensors including inertial measurement sensors, visual sensors, and range sensors are employed to realize the 3D simultaneously localization and mapping (SLAM) for UAVs. back to top
Funding Source: Temasek Laboratories , 2009-2013, $200,000 (with K Y Lum and K Peng)
The success of using machine vision technologies for multi-agent system (MAS) in military and civilian applications has aroused great interests in its potential in future. One of the leading edge goals of the next generation vision-based MAS aims to the capability of navigation in complex environments such as indoor areas. Two challenges of this goal are the efficient and reliable testbeds design and the advanced vision-based navigation system for MAS to assist GPS-only or gyro-less navigation systems. So far, there has been very little research related to this field and many core issues left to be studied to support this application.
In this project, we will focus on two main issues: The first one is to design and implement more sophisticated mechatronic system design methodology to construct reliable testbeds of the multi-agent system (MAS), based on off-the-shelf ground and aerial vehicles; the second part is to investigate an advanced vision-based navigation system, aided by machine vision technologies, to guide MAS in complex environments. For the former, a small fleet of low-cost, ultra-light weight, but reliable MAS testbeds will be developed. For the vision-based navigation system, advanced vision-augmented inertial or GPS approaches will be employed to stabilize the MAS in GPS-only or gyro-less environments. Vision-based motion coordination, target detection and tracking algorithms will be designed for MAS, which fuse information of multiple platforms, prior knowledge and human commands in diverse navigation modes. back to top
Funding Source: Temasek Defence Systems Institute , 2008-2011, $300,000 (with H Lin - PI, T H Lee and C Chen)
Cooperative control of multiple Unmanned Air Vehicles (UAVs) is still in its infancy and poses significant theoretical and technical challenges. Usually, the cooperative control scheme is organized in a hierarchy manner, where the low-level control signals and the higher-level supervisory logical rules are designed separately. This project aims to propose a new hybrid system approach that serves as a unified framework to study the coupling between the essentially discrete features of the cooperative supervisory logic and the continuous dynamics of UAVs. This perspective allows us to develop new techniques to determine the effects of the coupling in the performance of the system, and, more importantly, innovative hybrid control technologies for the cooperative reconfiguration control of multiple UAVs. It is believed that significant benefits can potentially be obtained through a joint design of the supervisory logic and the continuous control algorithms. The outcome of the project will be on a novel hybrid cooperative control concepts, modeling and design framework for UAV groups so as to achieve quick responses and autonomous reconfiguration ability for different tasks or scenarios. Actual flight testing will be conducted to verify the developed hybrid control technologies. back to top
Funding Source: Temasek Defence Systems Institute , 2007-2010, $300,000 (with T H Lee and Rodney Teo)
UAVs aroused a great interest in past decades because of their military and civil applications. An autonomous UAV can, however, carry out only certain scheduled tasks and it has no capability of responding to unscheduled and expected events. An operating platform is thus needed to enhance the capability of UAVs . Because of the limitation of radio wave range, the platform should be mobile. A manned air vehicle (MAV) is a good moving platform to lead UAVs in flight formation. Flight formation of UAVs via an MAV has great advantages compared to UAVs or MAVs in separation. It can make full use of either human abilities or properties of UAVs to improve possibility and feasibility to complete designated missions. So far, there is a little research related to this field and much core issues left to be studied to support this application.
This project is to explore new concepts, definitions and technologies applicable to lead UAVs via MAVs . We aim to develop a flight control platform for formation flight and test the developed technologies and concepts using actual UAV helicopters. In particular, we aim
To develop systematic concepts and methods to model and control flight formation of UAVs via MAVs .
To develop a flight control platform for formation flight.
To verify the developed technologies and concepts using actual UAV helicopters. back to top
Funding Source: Temasek Laboratories , 2007-2009, $100,000 (with K Y Lum and K Peng)
Flight formation comprising UAV helicopters and piloted helicopters has become a hot topic because of its potential applications in both civil and military domains. The challenges in designing flight control systems for UAV helicopters and their flight formation are nonlinearities in the helicopter aerodynamics and complexity associated with flight formation. Diverse control techniques have been proposed in the literature to tackle such challenges. But there is still much left to be further investigated.
In this project, we will focus on investigating identification methodologies for complex systems and nonlinear system control methods for solving the proposed problem. A systematic method incorporating the Monte Carlo process will be utilized to identify the nonlinear dynamical model of UAV helicopters with in-flight data. A perfect formulation methodology in hybrid frameworks will be used to formulate model of flight formation of UAV helicopters. A nonlinear system control method based on the so-called composite nonlinear feedback control technique will be proposed to design automatic flight control laws for UAV helicopters and their formation flight. Time constraints and communication delay/breakdown will be key factors in the design of flight control laws. A supervisory controller will be designed to handle the task assignment and monitoring of flight formation. These proposed technologies will be verified with actual flight tests.back to top
Funding Source: Defence Science & Technology Agency , 2003-2007, $700,000
This project is to pursue advance nonlinear control methods to design flight control systems for flying vehicles, namely a radio-controlled helicopter and an ST Aerospace FanTail air vehicle. The following topics will be focused in the researches:
To build a test bed of the flight control systems for the researches in this project.
To pursue systematic methods to model the helicopter and FanTail with in-flight data and to establish a base of the helicopter for the design of helicopter flight control systems.
To pursue and verify advance nonlinear control methods to design helicopter and FanTail flight control systems for super maneuver flights to complete the mission of terrain follow & obstacle avoidance or attacks onto the targets on the ground.
The proposed research has both major potential theoretical impact and engineering impact. It is to be focused at systematic methods to model the helicopters and FanTail with in-flight data and nonlinear control methods to design integrated flight control systems to improve the ability of the helicopter and FanTail to complete the designated missions. These methods will be verified on the test bed to be built, a radio-controlled helicopter and FanTail . The proposed methods will be significantly useful to improve the level of the design of both military and civil helicopters as well as other flying vehicles. back to top