Swarm and Multi-agent Time-based A* Path Planning for Lighter-Than-Air Systems

Abstract

This work develops and implements a multi-agent time-based path-planning method using A*. The purpose of this work is to create methods in which multi-agent systems can coordinate actions and complete them at the same time. We utilized A* with constraints defined by a dynamic model of each agent. The model for each agent is updated during each time step and the resulting control is determined. This results in a translational path that each of the agents is physically capable of completing in synchrony. The resulting path is given to the agents as a sequence of waypoints. Periodic updates of the path are calculated, utilizing real-world position and velocity information, as the agents complete the task to account for external disturbances. Our methodology is tested in a dynamic simulation environment as well as on real-world lighter-than-air robotic agents.

Dynamic anti-collision A-star algorithm for multi-ship encounter situations

“…For the ship local path planning, the COLREGS proposed by IMO is often considered (mainly related to rules 13-17). Some scholars have adopted the method of dynamic real-time planning, that is, updating the map information with a certain frequency and then re-planning the path periodically (Jiang et al, 2014;Gibson et al, 2020). The threedimensional reconstruction method is used to obtain the surrounding obstacle information in real time, and then the surrounding obstacles as static obstacles are regarded, and A-star algorithm is applied to re-plan the optimal path (Jiang et al, 2014).…”

An Anti-Disturbance Resilience Enhanced Algorithm for UAV 3D Route Planning

“…To solve the problems of autonomous anti-disturbance, obstacle avoidance and planning of UAVs, a variety of UAV-assisted algorithms for specific needs have been developed. Traditional UAV autonomous route planning algorithms mainly include the A* algorithm [ 1 , 2 , 3 ], ant colony algorithm [ 4 , 5 , 6 , 7 ], simulated annealing algorithm [ 8 , 9 , 10 , 11 ], Particle Swarm algorithm [ 12 , 13 , 14 ], artificial potential fields algorithm [ 15 , 16 , 17 , 18 , 19 ], etc. The A* algorithm, represented by a small amount of calculations and fast speed, has difficulties ensuring the safe operation of the UAV in an environment involving disturbance.…”

Swing-Reducing Flight Control System for an Underactuated Indoor Miniature Autonomous Blimp

“…Yuan is with the School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: yye@hust.edu.cn). including 3D field mapping [10], [11], human-robot interaction (HRI) [12], [13], and testbed for deep-learning-based localization and multi-agent path planning [14], [15]. However, lateral and longitudinal oscillation is often observed due to the underactuated design and the unique aerodynamic shape.…”