This paper presents a novel approach to address the challenge of maintaining connectivity within a multi-agent system (MAS) when utilizing directional visual sensors. These sensors have become essential tools for enhancing communication and connectivity in MAS, but their geometric constraints pose unique challenges when designing controllers. Our approach, grounded in geometric principles, leverages a mathematical model of directional visual sensors and employs a gradient-descent optimization method to determine the position and orientation constraints for each sensor based on its geometric configuration. This methodology ensures network connectivity, provided that initial geometric constraints are met. Experimental results validate the efficacy of our approach, highlighting its practical applicability for a range of tasks within MAS.

This paper proposes robust controllers for a class of unmanned tracked ground vehicles (UTGVs), which are built to autonomously clean carryback or spillage from the conveyor belts used in the mining industry. The UTGV, a nonholonomic system in its nature, needs to follow a given path in a harsh environment with large uncertainties due to the time-varying mass and inertia when the UTGV loads and unloads as well as unknown frictions and flatness of the ground. Moreover, the input constraints coming from motors do exist. It is usually hard to design robust controllers for such complex systems. By utilizing the available autonomous driving system, which is designed to be compatible with the existing remote motion controller in unmanned systems to generate autonomous ability, this paper uses the off-the-shelf motion planner to calculate desired linear and angular velocities based on the given path and sensor perceptions. Consequently, the control design can be simplified as two decoupled linear time-invariant scalar dynamic systems with uncertainties, making the active disturbance rejection controller (ADRC) applicable. By carefully designing the parameters of ADRC with the help of an extended state observer (ESO), it is shown that the proposed ADRC and ESO can achieve good tracking performance in the presence of input saturation and can handle nonsmooth disturbances. The proposed simulation results and experimental results support the theoretical findings.