Autonomous navigation requires real-time trajectory optimization with limited onboard computational resources, where traditional optimization-based methods often impose heavy computational burdens and require extensive parameter tuning. To achieve transparent navigation results and minimize computational burden, this paper introduces an iterative learning planning (ILP) approach for navigation. By dynamically integrating the control layer into the planning layer, ILP can achieve efficient trajectory optimization through functional iterative learning, providing transparent results that enhance navigation efficiency. This approach significantly improves real-time performance, achieving a time complexity ofO(k*n), wherek*represents the number of iterations andnrepresents the number of waypoints. Simulations and quadcopter experiments are conducted to verify the proposed framework. Results show that ILP significantly improves computational efficiency, ensures safe navigation, and maintains robustness against disturbances, demonstrating its potential as a practical solution for real-world autonomous systems.

With the rapid development of uncrewed aerial vehicle (UAV) technology in recent years, research on large-scale low-altitude UAV air traffic management (ATM) has gained attention. Unlike the traditional ATM, the number of small UAVs in the airspace may be in the millions, making air traffic management challenging. In an ATM, airspace is composed of airways, intersections, and nodes. In this paper, a three-dimensional (3-D) roundabout model is utilized as an airspace structure for air traffic intersections of known traffic network models, which is decomposed into a central island, several ramps, and buffer zones. In this paper, for simplicity, the distributed coordination of the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass through a 3-D roundabout is focused on, which is formulated as a 3-D roundabout passing-through problem. The corresponding control objectives include inter-agent conflict-free, keeping within the 3-D curved virtual tube, and avoiding local minima. Lyapunov-like functions are designed elaborately, and formal analysisismade to show that all UAVs can pass through the 3-D roundabout without getting trapped. Taking the kinematic model of VTOL UAVs into consideration, the horizontal control and attitude control channels are decoupled, which is more reasonable for practical applications. Numerical simulation and real experiment are given to show the effectiveness of the proposed method.

This paper proposes a model of a class of regular virtual tubes that can generate safe, feasible, and smooth space for a robotics swarm in an obstacle-dense environment, especially for a drone swarm based on the flocking model. The regular principles are first proposed, and the regular conditions are then formulated based on the principles. A method to obtain a regular virtual tube is also presented based on trajectory planning and regular conditions. The proposed method's effectiveness and robustness are comprehensively demonstrated in a simulation environment with random obstacles.

This paper aims to present a drone swarm light show design platform to support STEAM (science, technology, engineering, art and mathematics) education for K-12 children. With this platform, children can use this platform to design a drone swarm light show easily. To this end, the architecture of this platform contents three layers: UI layer, command layer, and physical layer. The UI layer has an easy-to-use interface for children. Children can feed parameters about the light show by clicking buttons and dragging sliders of four tracks. All actions designed for the swarm in the UI layer will be generated automatically in the form of the drone’s desired trajectories through the command layer. The physical layer includes a router for communication and a drone swarm for the light show. Our experimental results demonstrate that this platform works efficiently and suits for being applied to real STEAM education.