A Hierarchical Collision Avoidance Architecture for Multiple Fixed-Wing UAVs in an Integrated Airspace

This paper studies the collision avoidance problem for autonomous multiple fixedwing UAVs in the complex integrated airspace. By studying and combining the online path planning method, the distributed model predictive control algorithm, and the geometric reactive control approach, a three-layered collision avoidance system integrating conflict detection and resolution procedures is developed for multiple fixed-wing UAVs modeled by unicycle kinematics subject to input constraints. The effectiveness of the proposed methodology is evaluated and validated via test results of comparative simulations under both deterministic and probabilistic sensing conditions.

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

In this paper, a new design scheme is presented to solve the optimal control problem for nonlinear systems with unsymmetrical input constraints. This method also relaxes the assumption in the current study for the adaptive optimal control, that is, the internal dynamics should hold zero when the state of the system is in the origin. Particularity, the partially-unknown system is investigated and the procedure to obtain the corresponding optimal control policy is introduced. The optimality of the obtained control policy and the stability for the closed-loop dynamics are proved theoretically. Meanwhile, the proposed method in this paper can be further applied to nonlinear control systems whose dynamics are completely known or unknown. Besides, we apply the control design framework proposed in this paper 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.

Rethinking the Mathematical Framework and Optimality of Set-Membership Filtering

Set-Membership Filter (SMF) has been extensively studied for state estimation in the presence of bounded noises with unknown statistics. Since it was first introduced in the 1960s, the studies on SMF have used the set-based description as its mathematical framework. One important issue that has been overlooked is the optimality of SMF. In this work, we put forward a new mathematical framework for SMF using concepts of uncertain variables. We first establish two basic properties of uncertain variables, namely, the law of total range (a non-stochastic version of the law of total probability) and the equivalent Bayes rule. This enables us to put forward a general SMFing framework with established optimality. Furthermore, we obtain the optimal SMF under a non-stochastic Markov condition, which is shown to be fundamentally equivalent to the Bayes filter. Note that the classical SMF in the literature is only equivalent to the optimal SMF we obtained under the non-stochastic Markov condition. When this condition is violated, we show that the classical SMF is not optimal and it only gives an outer bound on the optimal estimation.

Mission Oriented Miniature Fixed-wing UAV Swarms: A Multi-layered and Distributed Architecture

UAV swarms have triggered wide concern due to their potential application values in recent years. While there are studies proposed in terms of the architecture design for UAV swarms, two main challenges still exist: (1) Scalability, supporting a large scale of vehicles; (2) Versatility, integrating diversified missions. To this end, a multi-layered and distributed architecture for mission oriented miniature fixed-wing UAV swarms is presented in this paper. The proposed architecture is built on the concept of modularity. It divides the overall system to five layers: low-level control, high-level control, coordination, communication and human interaction layers, and many modules that can be viewed as black boxes with interfaces of inputs and outputs. In this way, not only the complexity of developing a large system can be reduced, but also the versatility of supporting diversified missions can be ensured. Furthermore, the proposed architecture is fully distributed that each UAV performs the decision-making procedure autonomously so as to achieve better scalability. Moreover, different kinds of aerial platforms can be feasibly extended by using the control allocation matrices and the integrated hardware box. A prototype swarm system based on the proposed architecture is built and the proposed architecture is evaluated through field experiments with a scale of 21 fixed-wing UAVs. Particularly, to the best of our knowledge, this paper is the first work which successfully demonstrates formation flight, target recognition and tracking missions within an integrated architecture for fixed-wing UAV swarms through field experiments.