An iterative strategy for task assignment and path planning of distributed multiple unmanned aerial vehicles

A joint mid-course and terminal course cooperative guidance law for multi-missile salvo attack

Curvature-constrained path elongation with expected length for Dubins vehicle

As a vital part of autonomous navigation of mobile robot, path planning is a hot research direction which aims at searching a shortest collision-free path from the starting position to the goal position in a complex environment. In this paper, a method for global dynamic path planning is designed based on improved self-adaptive harmony search algorithm (ISAHS) and Morphin algorithm. Firstly, to improve the quality of new solution vector, a neighbors and optimal learning strategy is introduced. Secondly, two key parameters are adjusted adaptively and a probability disturbance strategy is designed for renewing harmony memory, and then an improved self-adaptive harmony search algorithm is proposed to obtain an initial optimal path in the static environment. Finally, the Morphin algorithm is introduced to avoid the moving obstacles in real time. Simulation results indicate that the proposed method performs well in planning an initial static optimal path and it can avoid all preset moving obstacles effectively.

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Mobile Robot Dynamic Path Planning Based on Self-Adaptive Harmony Search Algorithm and Morphin Algorithm

In this paper, the traditional shortest path planning problem for vehicle is advanced to length-targeted path planning problem, i.e., to plan path with its length being as close to a specified value as possible. Lengthening and shortening of given initial paths are used to solve this problem. Based on an operation set consisting of three basic path homotopies, we build a comprehensive and systematic framework to plan paths with target length, which is a generalization of the existing related studies. Thereby, the expected paths can be independently searched through such deformation processes within topological classes. The proposed framework can produce the largest length coverage in different scenarios and under different conditions, and it can also generate expected paths with arbitrary topological classification in terms of the curvature constraint and the obstacle constraint. Examples show that our lengthening and shortening method can effectively solve the length-targeted path planning problem in environment without or with obstacles. Note to Practitioners —This paper presents a curvature-bounded lengthening and shortening approach for path planning of vehicles. The curvature and obstacle constraints in the path planning problem are handled using the topological technique. In addition, a rigorous classification system is presented to systematically categorize the potential paths. Within a path class, any path can be deformed to other paths while keeping the curvature bounded. Then, the path deformation process is conducted via a series of basic homotopy operations to approach the target length. The length coverage range of this process is reduced when some endpoint conditions are meet or the vehicle is trapped into a closed region.

Curvature-Bounded Lengthening and Shortening for Restricted Vehicle Path Planning

Bounded curvature path planning with expected length for Dubins vehicle entering target manifold

To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of “launch-recovery-relaunch” will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.

Cooperative Path Planning for Aerial Recovery of a UAV Swarm Using Genetic Algorithm and Homotopic Approach

Aerial recovery technology for aircrafts plays a significant role in practical applications, which presents a challenging problem in cooperative trajectory planning. To solve the problem, a hierarchical trajectory planning architecture is proposed in this paper. Firstly, the waypoints of the mother aircraft trajectory are obtained based on the distribution of the child aircrafts by using a hierarchical clustering algorithm. A greedy algorithm is used to obtain the traverse sequence for the waypoints and the mother aircraft trajectory is generated based on the Dubins path. Secondly, a genetic algorithm is employed to optimize the recovery position and recovery time for each child aircraft. Lastly, the trajectory of each child aircraft is generated by using a double-stage trajectory generation method. Simulation results validate the effectiveness of the proposed method for cooperative trajectory planning in aerial recovery.

Jun Zhao

Papers

Bounded curvature path planning with expected length for Dubins vehicle entering target manifold

Cooperative Path Planning for Aerial Recovery of a UAV Swarm Using Genetic Algorithm and Homotopic Approach

Cooperative trajectory planning for mother/child aircrafts in aerial recovery