Optimal control of nonlinear partially-unknown systems with unsymmetrical input constraints and its applications to the optimal UAV circumnavigation problem

Aimed at solving the optimal control problem for nonlinear systems with unsymmetrical input constraints, we present an online adaptive approach for partially unknown control systems/dynamics. The designed algorithm converges online to the optimal control solution without the knowledge of the internal system dynamics. The optimality of the obtained control policy and the stability for the closed-loop dynamic optimality are proved theoretically. The proposed method greatly relaxes the assumption on the form of the internal dynamics and input constraints in previous works. Besides, the control design framework proposed in this paper offers a new approach to solve the optimal circumnavigation problem involving a moving target for a fixed-wing unmanned aerial vehicle (UAV). The control performance of our method is compared with that of the existing circumnavigation control law in a numerical simulation and the simulation results validate the effectiveness of our algorithm

Contextual Hierarchical Part-Driven Conditional Random Field Model for Object Category Detection

Even though several promising approaches have been proposed in the literature, generic category-level object detection is still challenging due to high intraclass variability and ambiguity in the appearance among different object instances. From the view of constructing object models, the balance between flexibility and discrimination must be taken into consideration. Motivated by these demands, we propose a novel contextual hierarchical part-driven conditional random field (CRF) model, which is based on not only individual object part appearance but also model contextual interactions of the parts simultaneously. By using a latent two-layer hierarchical formulation of labels and a weighted neighborhood structure, the model can effectively encode the dependencies among object parts. Meanwhile, beta-stable local features are introduced as observed data to ensure the discriminative and robustness of part description. The object category detection problem can be solved in a probabilistic framework using a supervised learning method based on maximum a posteriori (MAP) estimation. The benefits of the proposed model are demonstrated on the standard dataset and satellite images

Collision-free Multiple Unmanned Combat Aerial Vehicles Cooperative Trajectory Planning for Time-critical Missions using Differential Flatness Approach

This paper investigates the cooperative trajectory planning for multiple unmanned combat aerial vehicles in performing autonomous cooperative air-to-ground target attack missions. Firstly, the collision-free cooperative trajectory planning problem for time-critical missions is formulated as a cooperative trajectory optimal control problem (CTP-OCP), which is based on an approximate allowable attack region model, several constraints model, and a multi-criteria objective function. Next, a planning algorithm based on the differential flatness, B-spline curves and nonlinear programming is designed to solve the CTP-OCP. In particular, the notion of the virtual time is introduced to deal with the temporal constraints. Finally, the proposed approach is validated by two typical scenarios and the simulation results show the feasibility and effectiveness of the proposed planning approach.Defence Science Journal, Vol. 64, No. 1, January 2014, DOI:10.14429/dsj.64.299

Tactical Trajectory Planning for Stealth Unmanned Aerial Vehicle to Win the Radar Game

In this paper, problem of planning tactical trajectory for stealth unmanned aerial vehicle (UAV) to win the radar game is studied. Three principles of how to win the radar game are presented, and their utilizations for stealth UAV to evade radar tracking are analysed. The problem is formulated by integrating the model of stealth UAV, the constraints of radar detecting and the multi-objectives of the game. The pseudospectral multi-phase optimal control based trajectory planning algorithm is developed to solve the formulated problem. Pseudospectral method is employed to seek the optimal solution with satisfying convergence speed. The results of experiments show that the proposed method is feasible and effective. By following the planned trajectory with several times of switches between exposure and stealth, stealth UAV could win the radar game triumphantly.Defence Science Journal, 2012, 62(6), pp.375-381, DOI:http://dx.doi.org/10.14429/dsj.62.268

A polynomial time optimal algorithm for robot-human search under uncertainty

This paper studies a search problem involving a robot that is searching for a certain item in an uncertain environment (e.g., searching minerals on Moon) that allows only limited interaction with humans. The uncertainty of the environment comes from the rewards of undiscovered items and the availability of costly human help. The goal of the robot is to maximize the reward of the items found while minimising the search costs. We show that this search problem is polynomially solvable with a novel integration of the human help, which has not been studied in the literature before. Furthermore, we empirically evaluate our solution with simulations and show that it significantly outperforms several benchmark approaches

Geometric Pseudospectral Method on SE(3) for Rigid-Body Dynamics with Application to Aircraft

General pseudospectral method is extended to the special Euclidean group SE(3) by virtue of equivariant map for rigid-body dynamics of the aircraft. On SE(3), a complete left invariant rigid-body dynamics model of the aircraft in body-fixed frame is established, including configuration model and velocity model. For the left invariance of the configuration model, equivalent Lie algebra equation corresponding to the configuration equation is derived based on the left-trivialized tangent of local coordinate map, and the top eight orders truncated Magnus series expansion with its coefficients of the solution of the equivalent Lie algebra equation are given. A numerical method called geometric pseudospectral method is developed, which, respectively, computes configurations and velocities at the collocation points and the endpoint based on two different collocation strategies. Through numerical tests on a free-floating rigid-body dynamics compared with several same order classical methods in Euclidean space and Lie group, it is found that the proposed method has higher accuracy, satisfying computational efficiency, stable Lie group structural conservativeness. Finally, how to apply the previous discretization scheme to rigid-body dynamics simulation and control of the aircraft is illustrated

Collaborative Target Tracking in Elliptic Coordinates: a Binocular Coordination Approach

This paper concentrates on the collaborative target tracking control of a pair of tracking vehicles with formation constraints. The proposed controller requires only distance measurements between tracking vehicles and the target. Its novelty lies in two aspects: 1) the elliptic coordinates are used to represent an arbitrary tracking formation without singularity, which can be deduced from inter-agent distances, and 2) the regulation of the tracking vehicle system obeys a binocular coordination principle, which simplifies the design of the control law by leveraging rich physical meanings of elliptic coordinates. The tracking system with the proposed controller is proven to be exponentially convergent when the target is stationary. When the target drifts with a small velocity, the desired tracking formation is achieved within a small margin proportional to the magnitude of the target's drift velocity. Simulation examples are provided to demonstrate the tracking performance of the proposed controller.Comment: 6 pages, 5 figure

A Novel Fusion Scheme for Vision Aided Inertial Navigation of Aerial Vehicles

Vision-aided inertial navigation is an important and practical mode of integrated navigation for aerial vehicles. In this paper, a novel fusion scheme is proposed and developed by using the information from inertial navigation system (INS) and vision matching subsystem. This scheme is different from the conventional Kalman filter (CKF); CKF treats these two information sources equally even though vision-aided navigation is linked to uncertainty and inaccuracy. Eventually, by concentrating on reliability of vision matching, the fusion scheme of integrated navigation is upgraded. Not only matching positions are used, but also their reliable extents are considered. Moreover, a fusion algorithm is designed and proved to be the optimal as it minimizes the variance in terms of mean square error estimation. Simulations are carried out to validate the effectiveness of this novel navigation fusion scheme. Results show the new fusion scheme outperforms CKF and adaptive Kalman filter (AKF) in vision/INS estimation under given scenarios and specifications