This paper presents a novel model-free spatial iterative learning (IL) framework to enhance the efficiency of vector field (VF) navigation for mobile robots. By integrating the idea of iterative learning control (ILC) with VF, this framework utilizes historical data to enhance navigation efficiency significantly, reducing traversal time and expanding the applicability of IL to rapid navigation. Importantly, it has low time complexity with$O(n)$per iteration, where$n$denotes the waypoints number, preventing the significant computational overhead caused by the increasing waypoints in existing methods, which often exceeds$O(n^{2})$, making it well-suited for real-time planning. Moreover, the approach is inherently model-free, leaning on historical data, thus enabling agile navigation with limited reliance on intricate model details. The paper presents a comprehensive theoretical analysis of the stability, time optimality, time complexity, parameter insensitivity, robustness, and usage. Extensive simulations and experiments highlight its efficiency, promising a transformative impact on mobile robot navigation through the proposed IL.

Swarm robotics plays a non-negligible role in actual practice because of its scalability and robustness. Besides some specific studies, there is still a lack of overall approaches to solving the search and rescue problem in a communication-denied environment. This paper presents a bee-inspired swarm cooperation approach without information exchange, including a target grouping method suitable for multi-objective and multi-robot, a finite behavior state machine, and the corresponding control law. Finally, the effectiveness of the proposed approach is shown via simulation. The overall approach proposed in this paper does not require two-way information exchange, and it is robust against relative and own position errors, making swarm robotics search and rescue in a communication-denied environment possible.

It is often necessary for drones to complete delivery, photography, and rescue in the shortest time to increase efficiency. Many autonomous drone races provide platforms to pursue algorithms to finish races as quickly as possible for the above purpose. Unfortunately, existing methods often fail to keep training and racing time short in drone racing competitions. This motivates us to develop a high-efficient learning method by imitating the training experience of top racing drivers. Unlike traditional iterative learning control methods for accurate tracking, the proposed approach iteratively learns a trajectory online to finish the race as quickly as possible. Simulations and experiments using different models show that the proposed approach is model-free and is able to achieve the optimal result with low computation requirements. Furthermore, this approach surpasses some state-of-the-art methods in racing time on a benchmark drone racing platform. An experiment on a real quadcopter is also performed to demonstrate its effectiveness.

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.