A Cooperative Localization Method for Leader–Follower Multiple UAVs Using Continuous Relative Ranging Information

Cooperative localization(CL)utilizing multiple unmanned aerial vehides(UAVs)has become the current development trend and research hotspot.The tnditional distance-based CL method.typically uses principle of the spherical intersection to achieve UAV high-precision positioning.However,this distance based CL strategy requires several leader UAVs at the known positions.Thus,a cooperative localization method for leader-follower multiple UA Vs using continuous relative ranging information is proposed in this paper.Aiming at the UAV formation consists of a leader UAV node with the known position and at least a follower UAV that needs to be located.Using the Inertial short-time high-precision relative position constraint and the con-tinuous relative distance constraints with the leader UAV nodes,the closed-form solution of follower UAV's three-dimensional position can be obtained using spherical intersection prin-ciple.On this basis,we derive and construct follower UAV's position observation equation.The:developed strategy requires a single known position leader UAV node to realize high-precision CL,which can efctively solve the taditional distance-based collaborative location problem in case the number of leader UAVs is small.The simulation and experimental results demonstrate that the proposed method significantly improves follower UAV's positioning accuracy,and neglects the limitation of the traditional distance-based CL method on the number of leader UAV nodes at known positions.

Observation-Driven Multiple UAV Coordinated Standoff Target Tracking Based on Model Predictive Control

An observation-driven method for coordinated standoff target tracking based on Model Predictive Control(MPC)is proposed to improve observation of multiple Unmanned Aerial Vehicles(UAVs)while approaching or loitering over a target.After acquiring a fusion estimate of the target state,each UAV locally measures the observation capability of the entire UAV system with the Fisher Information Matrix(FIM)determinant in the decentralized architecture.To facilitate observation optimization,only the FIM determinant is adopted to derive the performance function and control constraints for coordinated standoff tracking.Additionally,a modified iterative scheme is introduced to improve the iterative efficiency,and a consistent circular direction control is established to maintain long-term observation performance when the UAV approaches its target.Sufficient experiments with simulated and real trajectories validate that the proposed method can improve observation of the UAV system for target tracking and adaptively optimize UAV trajectories according to sensor performance and UAV-target geometry.

Bio-inspired Collision-free 4D Trajectory Generation for UAVs Using Tau Strategy

Inspired by the general tau theory in animal motion planning, a collision-free four-dimensional （4D） trajectory generation method is presented for multiple Unmanned Aerial Vehicles （UAVs）. This method can generate a group of optimal or near-optimal collision-free 4D trajectories, the position and velocity of which are synchronously planned in accordance with the arrival time. To enlarge the shape adjustment capability of trajectories with zero initial acceleration, a new strategy named intrinsic tau harmonic guidance strategy is proposed on the basis of general tau theory and harmonic motion. In the case of multiple UAVs, the 4D trajectories generated by the new strategy are optimized by the bionic Particle Swarm Optimization （PSO） algorithm. In order to ensure flight safety, the protected airspace zone is used for collision detection, and two collision resolution approaches are applied to resolve the remaining conflicts after global trajectory optimization. Numerous simulation results of the simultaneous arrival missions demonstrate that the proposed method can effectively provide more flyable and safer 4D trajectories than that of the existing methods.

Multi-UAV collaborative system with a feature fast matching algorithm

We present a real-time monocular simultaneous localization and mapping(SLAM)system with a new distributed structure for multi-UAV collaboration tasks.The system is different from other general SLAM systems in two aspects:First,it does not aim to build a global map,but to estimate the latest relative position between nearby vehicles;Second,there is no centralized structure in the proposed system,and each vehicle owns an individual metric map and an ego-motion estimator to obtain the relative position between its own map and the neighboring vehicles'.To realize the above characteristics in real time,we demonstrate an innovative feature description and matching algorithm to avoid catastrophic expansion of feature point matching workload due to the increased number of UAVs.Based on the hash and principal component analysis,the matching time complexity of this algorithm can be reduced from 0(logN)to 0(1).To evaluate the performance,the algorithm is verified on the acknowledged multi-view stereo benchmark dataset,and excellent results are obtained.Finally,through the simulation and real flight experiments,this improved SLAM system with the proposed algorithm is validated.

Terrain based co-operative UAV mapping of complex obstacles using 2-D splinegon

Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature.It also aims to use an extended Kalman filter(EKF)to estimate the fused position of the UAVs and to apply the 2-D splinegon technique to build the map of the complex shaped obstacles.The path of the UAVs are dictated by the Dubins path planning algorithm.The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.Design/methodology/approach–An extended Kalman filter is used to estimate the position of the UAVs,and the 2-D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.Findings-The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2-D splinegon technique.This is a newly proposed algorithm,the most efficient and a robust way in terrain based mapping of the complex obstacles.The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.Originality/value-The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2-D splinegon technique.This is a new approach where the fused EKF estimated positions are used with the limited number of sensors’measurements in building the map of the complex obstacles.

Periodic event-triggered formation control for multi-UAV systems with collision avoidance

In this paper,periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles(UAVs).Firstly,based on the Artificial Potential Field(APF)method,a novel sliding manifold is proposed for controller design,which can solve the problem of collision avoidance.Then,the event-triggered strategy is applied to the distributed formation control of multi-UAV systems,where the evaluation of the event condition is continuous.In addition,the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound.Next,a periodic event-triggered mechanism is developed for formation control based on the continuous eventtriggered mechanism.The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors,which can reduce the control cost.The stability of the control system is proved by the Lyapunov function method.Finally,some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol.