A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

Abstract

In this paper, the cooperative multi-task online mission planning for multiple Unmanned Aerial Vehicles (UAVs) is studied. Firstly, the dynamics of unmanned aerial vehicles and the mission planning problem are studied. Secondly, a hierarchical mechanism is proposed to deal with the complex multi-UAV multi-task mission planning problem. In the first stage, the flight paths of UAVs are generated by the Dubins curve and B-spline mixed method, which are defined as “CBC)” curves, where “C” stands for circular arc and “B” stands for B-spline segment. In the second stage, the task assignment problem is solved as multi-base multi-traveling salesman problem, in which the “CBC” flight paths are used to estimate the trajectory costs. In the third stage, the flight trajectories of UAVs are generated by using Gaussian pseudospectral method (GPM). Thirdly, to improve the computational efficiency, the continuous and differential initial trajectories are generated based on the “CBC” flight paths. Finally, numerical simulations are presented to demonstrate the proposed approach, the designed initial solution search algorithm is compared with existing methods. These results indicate that the proposed hierarchical mission planning method can produce satisfactory mission planning results efficiently.

Formally Verified Timing Computation for Non-deterministic Horizontal Turns During Aircraft Collision Avoidance Maneuvers

“…Dubins paths, constructed of circular arc segments and straight lines, are used to model horizontal motion in many path planning and collision avoidance algorithms, such as [4,5,9,10,14,15]. These examples are not formally verified, and although some are created with aircraft in mind, not designed for timing analysis nor the adversarial collision avoidance assumptions in our work.…”

Multistage Cooperative Trajectory Planning for Multimissile Formation via Bi-Level Sequential Convex Programming

“…By combining Eq. (5) and Eq (6)., both C L and C D are functions of η. Correspondingly, L and D are functions of η as well.…”

“…where a ij , b ij ,and c i are the nonzero elements in A(x m ), B(x m ), and c(x m ), respectively; p 6 is the costate vector; and µ u , µ − 3 , µ + 3 and µ v are the Lagrangian multipliers. According to the direct adjoining approach in optimal control theory, a constant p 0 ≤ 0 exists such that the following necessary optimality conditions hold for {x * , u * }:…”

“…As a key component of a cooperative attack, cooperative trajectory planning has gained significant attention. Typical cooperative trajectory planning methods include mixed-integer programming [5], nonlinear programming [6], rapidly exploring random…”

Collaborative Task Allocation of Heterogeneous Multi-Unmanned Platform Based on a Hybrid Improved Contract Net Algorithm

“…In this study, a dynamic allocation model is built for collaborative tasks. By analyzing characteristics of collaborative task planning problems of the multi-unmanned platform, the contract net algorithm is improved based on the mental coefficient [7,8], the blackboard model [9] and the buffer pool mechanism [10], proposing improvement strategies for different stages. At last, the hybrid improved contract net algorithm was adopted to settle dynamic allocation problems of collaborative tasks on the multiunmanned platform.…”