Cyborg insects refer to hybrid robots that integrate living insects with miniature electronic controllers to enable robotic-like programmable control. These creatures exhibit advantages over conventional robots in adaption to complex terrain and sustained energy efficiency. Nevertheless, there is a lack of literature on the control of multi-cyborg systems. This research gap is due to the difficulty in coordinating the movements of a cyborg system under the presence of insects’ inherent individual variability in their reactions to control input. Regarding this issue, we propose a swarm navigation algorithm and verify it under experiments. This research advances swarm robotics by integrating biological organisms with control theory to develop intelligent autonomous systems for real-world applications.

This letter presents a safe adaptive coverage controller for multi-agent systems with actuator faults and time-varying uncertainties. The centroidal Voronoi tessellation (CVT) is applied to generate an optimal configuration of multi-agent systems for covering an area of interest. As conventional CVT based controller cannot prevent collisions between agents with non-zero size, a control barrier function (CBF) based controller is developed to ensure collision avoidance with a function approximation technique (FAT) based design to deal with system uncertainties. The proposed controller is verified under simulations.

Multi-agent coverage control plays a crucial role in the modeling and coordination of complex systems, especially for teams of robots that, through frequent interactions with each other and their environment, can accomplish complex tasks in a distributed and parallel manner. However, most existing studies on coverage control for multi-agent systems are limited to two-dimensional environments, with few addressing the height factor critical to three-dimensional spaces. This study proposes a novel approach that adapts centroidal Voronoi tessellation (CVT) with a time-varying density function and Control Barrier Functions (CBFs) for dynamic coverage in 3D environments. By reconfiguring these methodologies, this approach enhances distribution and coordination efficiency within 3D spaces while ensuring safe, collision-free navigation. The simulation results validate the effectiveness of the proposed approach, demonstrating its potential for the efficient deployment of multi-robot systems, such as unmanned autonomous vehicles and unmanned autonomous underwater vehicles, in diverse operational contexts.

Autonomous intersection management (AIM) is gaining increasing attention due to its crucial role in ensuring safety and efficiency. Various methods have been proposed in the literature to address the AIM problem, including traffic light optimization, connected vehicle optimization based on vehicle-to-anything (V2X) communication technology, and multi-agent autonomous systems. However, each of these approaches has its own limitations, such as parking delays, communication latency, or the lack of guaranteed collision avoidance. This paper presents a novel approach to AIM using adaptive control barrier functions (aCBFs). The proposed aCBF first estimates the power transmission efficiency and incorporates it into the CBF design to ensure collision-free operation. Compared to existing methods, the aCBF approach offers several advantages. Firstly, it eliminates parking delays caused by traffic light systems. Secondly, it can be deployed in intersections with limited network coverage, unlike IoT-based solutions that rely heavily on connectivity. Thirdly, it ensures guaranteed collision-free agent movement at intersections. Specifically, our method guarantees that the minimum distance between agents remains no less than the safe distance at all times, significantly enhancing safety. Furthermore, compared to the TriPField algorithm, our approach achieves a 95% success rate in collision avoidance, demonstrating reliability in autonomous intersection management. The effectiveness of the proposed aCBF-based AIM algorithm has been validated through simulations and experiments with multiple autonomous agent-like robots.

This paper presents safe coverage control algorithms for multi-agent systems, integrating Centroidal Voronoi Tessellation (CVT) and control barrier functions (CBFs). This study aims to ensure safety and spatial optimization by combining CVT and CBFs for obstacle avoidance, testing the controller through simulations, and verifying the results with RT mobile robots. This development of safe coverage control algorithms for multi-agent systems achieves a synergy that addresses both safety and spatial optimization, which are crucial for multi-agent systems. The proposed CVT-CBF-based controller has been validated through extensive simulations in the ROS/Gazebo environment and physical experiments with RT robots, demonstrating its effectiveness in achieving collision-free coverage. This study provides a comprehensive understanding of the integration of CVT and CBFs for safe coverage control with obstacle avoidance in multi-agent systems, highlighting both its potential and the necessary considerations for practical deployment.