Drones have a wide range of applications in urban environments as they can both enhance people's daily activities and commercial activities through various operations and deployments. With the increasing number of drones, flight safety and efficiency become the main concern, and effective drone operations can make a difference. Accordingly, 4D path planning for drone operations is the focus of this paper, and the swarm-based method is proposed to solve this complicated optimization problem. Under the framework of ‘AirMatrix’, the problem is solved in two levels, i.e., 3D path planning for a single drone and conflict resolution among drones. In the multi-path planning level, multiple alternative flight paths for each drone are generated to increase the acceptance rate of a flight request. The constraints on a single flight path and two different flight paths are considered. The goal is to obtain several different short flight paths as alternatives. A clustering improved ant colony optimization CIACO) algorithm is employed to solve the multi-path planning problem. The crowding mechanism is used in clustering, and some improvements are made to strengthen the global and local search ability in the early and later phases of iterations. In the task scheduling level, the conflicts between two drones are defined in two circumstances. One is for the time interval of passing the same path point, another one is for the right-angle collision between two drones. A three-layer fitness function is proposed to maximize the number of permitted flights according to the safety requirement, in which the airspace utilization and the operators’ requests are both considered. A ‘cross-off’ strategy is developed to calculate the fitness value, and a ‘distributed-centralized’ strategy is applied considering the task priorities of drones. A genetic algorithm GA)-based task scheduling algorithm is also developed according to the characteristic of the established model. Simulation results demonstrate that 4D flight path of each drone can be generated by the proposed swarmed-based algorithms, and safe and efficient drone operations in a specific airspace can be ensured.

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Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

Conflict-free four-dimensional path planning for urban air mobility considering airspace occupancy

Drones have been greatly developed to facilitate the progress of various industries. The safe operation of drones in the urban airspace is critical to ensure a reliable and high-efficient urban air traffic system. This work presents a fusion scheme to achieve autonomous drone collision-free path planning considering static obstacles and dynamic threats detected. Firstly, a 3D voxel jump point search (JPS) based path planning model is developed to generate the static collision-free reference path. With the optimization, the reference path is then de-diagonalized, reconstructed, and smoothed to obtain the desired path. Subsequently, a local collision resolution method is proposed to avoid near mid-air collision of the dynamic threats. The method is based on the Markov decision process (MDP) to implement real-time dynamic collision avoidance. Simulations are conducted to verify the performance of the proposed model. The simulation results demonstrate that the proposed model is effective to achieve the autonomous path planning and real-time collision resolution of multirotor drones.

3D path planning and real-time collision resolution of multirotor drone operations in complex urban low-altitude airspace

A hybrid particle swarm optimization-gauss pseudo method for reentry trajectory optimization of hypersonic vehicle with navigation information model

A meta-heuristic assisted underwater glider path planning method

Meta-heuristic algorithms have turned out to be good methods to address optimization problems with complicated constraints, and those algorithms have been widely applied to many aircraft motion planning problems. An optimal flight path is crucial for the aircraft to complete the specific task safely and efficiently. The existing surveys lack a broad review on the population-based meta-heuristic algorithms focusing on the aircraft motion planning problem, which fails to summarize the latest progress in this field. Therefore, a comprehensive survey on this topic is carried out from new perspectives. The mathematical model, the population-based meta-heuristic algorithms and their modifications regarding the aircraft motion planning problem are all included. Discussion is also made based on the statistical data from the collected literatures. It is anticipated that this survey will provide the researchers with some suggestions on how to select appropriate population-based meta-heuristic algorithms for a particular aircraft motion planning problem.

Yu Wu

Papers

A survey on population-based meta-heuristic algorithms for motion planning of aircraft