Robust, reliable, low-cost, and precise target docking solutions are in high demand in the industry. This paper investigates the docking problem of two non-holonomic robots with a fork attached to one of them and a docking station attached to the other. To secure latching the fork to the docking station, it must be oriented in a particular direction and distance from the docking station. Toward this and to perform docking operations, a navigation strategy is proposed which enables orientations and distances to be matched with the desired values. In addition, the mobile robot must intercept the target at a desired closing velocity for the docking mission to latch the fork and the docking station. This method also allows the final closing velocity to be set at any arbitrary value. This paper considers the docking operation of the assembling robots, and also proposes a solution to the docking problem, when the target moves at a constant velocity. As a result, the presented method allows the assembly of the robots, while moving. In addition, since safety is a critical factor in robotic systems operating in dynamic environments, a control-barrier-functions-based technique is adopted to avoid both static and dynamic obstacles. The performance of the proposed method is validated through a simulation and comparison study, then real-world implementation is performed using QBot robots. Note to Practitioners —Autonomous target docking for mobile robots has gained prominence, driven by its diverse applications like pallet picking, parking, refueling, automatic charging, and robot assembly. This paper specifically addresses the challenges of mobile robot docking in industrial settings, where shared workspaces involve static and dynamic obstacles, including equipment and humans. Two main challenges emerge: navigating a complex environment with obstacles and the time-consuming requirement for the robot to come to a complete stop before docking, particularly in scenarios with multiple docking targets or frequent operations. The paper proposes a solution allowing mobile robots to dock with a moving target at a constant velocity. This approach not only improves efficiency by eliminating the need for stopping during docking but also enhances safety through clear navigation paths and controlled closing velocity for precise docking. The presented solution shows promise in addressing the autonomous target docking problem in industrial environments, offering possibilities for further advancements and applications. Simulation and experimental results validate the effectiveness of the proposed method.

This paper introduces a novel control strategy to ensure safety in the navigation of a mobile manipulator comprising a fixed-base manipulator mounted on a mobile platform. The approach initially addresses the trajectory tracking problem of a non-holonomic mobile manipulator (NH-MM) by employing decoupling dynamic control through model predictive control-optimizable control barrier function (MPCOCBF). This allows for independent control of the end-effector and mobile platform trajectories, obstacle avoidance, and simultaneously adjusting the joint and control input limitations. The objective is to leverage the system's redundancy to enable the mobile platform to effectively navigate feasible obstacle avoidance scenarios without affecting the primary task performance of the end-effector. Additionally, the method aims to perform obstacle avoidance when the redundancy of the manipulator is insufficient, addressing non-feasible obstacle avoidance scenarios. Through this approach, the mobile manipulator effectively avoids obstacles, allowing the end-effector to autonomously carry out its intended task. The effectiveness of the proposed method is validated through simulations and comparison with existing approaches. Additionally, a quantitative analysis is provided to evaluate and compare the performance of the controllers.

This paper presents a safe trajectory generation framework for formation production in Multi-Agent Systems (MASs). The proposed approach integrates coverage control with Control Barrier Functions (CBFs) to generate safe trajectories that guide agents from arbitrary initial positions to a desired formation. The environment is partitioned into informative and non-informative regions, where the informative regions characterize the desired geometric structure, which effectively defines the final formation. A coverage-control-based algorithm first generates nominal trajectories that drive agents toward these informative regions. To ensure safety, CBFs are incorporated as a supervisory layer that enforces obstacle avoidance and inter-agent collision avoidance while minimally modifying the nominal control inputs. Consequently, the resulting trajectories remain safe while preserving convergence toward the desired formation.