Robotics by Joon

This paper introduces a team of Automated Guided Vehicles (AGVs) equipped with open-source, perception-enhancing rotating devices. Each device has set of ArUco markers, employed to compute the relative pose of other AGVs. These markers also serve as landmarks, delineating a path for the robots to follow. The authors combined various control methodologies to track the ArUco markers on another rotating device mounted on the AGVs. Behavior trees are implemented to facilitate task-switching or to respond to sudden disturbances, such as environmental obstacles. The Robot Operating System (ROS) is installed on the AGVs to manage high-level controls. The efficacy of the proposed solution is confirmed through a real experiment. This research contributes to the advancement of AGV technology and its potential applications in various fields.

This paper describes the design and development of a cleaning robot, using adaptive manufacturing technology and its use with a control algorithm for which there is a stability proof. The authors’ goal was to fill the gap between theory and practical implementation based on available low-cost components. Adaptive manufacturing was chosen to cut down the cost of manufacturing the robot. Practical verification of the effectiveness of the control algorithm was achieved with the experiments. The robot comprises mainly three assemblies, a four-wheel-drive platform, a four-degrees-of-freedom robotic arm, and a vacuum system. The inlet pipe of the vacuum system was attached to the end effector of the robotic arm, which makes the robot more flexible to clean uneven areas, such as skirting on floors. The robot was equipped with a LIDAR sensor and web camera, giving the opportunity to develop more complex methods. A low-level proportional–integral–derivative (PID) speed controller was implemented, and a high-level controller that uses artificial potential functions to generate repulsive components, which avoids collision with obstacles. Robot operating system (ROS) was installed in the robot’s on-board system. With the help of the ROS node, the high-level controller generates control signals for the low-level controller

This paper presents a leader–follower mobile robot control approach using onboard sensors. The follower robot is equipped with an Intel RealSense camera mounted on a rotating platform. Camera observations and ArUco markers are used to localize the robots to each other and relative to the workspace. The rotating platform allows the expansion of the perception range. As a result, the robot can use observations that are not within the camera’s field of view at the same time in the localization process. The decision-making process associated with the control of camera rotation is implemented using behavior trees. In addition, measurements from encoders and IMUs are used to improve the quality of localization. Data fusion is performed using the EKF filter and allows the user to determine the robot’s poses. A 3D-printed cuboidal tower is added to the leader robot with four ArUco markers located on its sides. Fiducial landmarks are placed on vertical surfaces in the workspace to improve the localization process. The experiments were performed to verify the effectiveness of the presented control algorithm. The robot operating system (ROS) was installed on both robots.

This paper presents a leader-follower controller combined with localization based on sensor data fusion. The leader robot has a cuboidal shape 3D printed tower on its top. ArUco markers with four different IDs are installed on the leader robot’s cuboidal tower. The follower robot has a one-degree-of-freedom rotating platform having Intel RealSense sensor on it. IMU sensors are installed on both the leader and follower robots. The data from wheel encoders and IMU sensors are fused by Extended Kalman Filter (EKF) to get the pose of robots. The proportional-integral-derivative (PID) controller rotates the Intel RealSense sensor on the follower robot to follow the ArUco markers on the leader robot. The follower robot calculates the pose of the leader robot after the detection of ArUco markers. Experiments are performed to validate the algorithm presented in the paper. Robot operating systems (ROS) is installed on both the robot’s single-board computers. The Opti-Track system is used to validate and plot the data fusion of robot poses.

This paper describes the design of one degree of freedom rotating platform for an Intel RealSense sensor and a leader-follower control algorithm. The rotating platform will be placed on the follower robot to track the ArUco markers on the leader robot. Four ArUco markers will be placed on the leader robot in different directions. The proportional-integral-derivative controller (PID controller) helps the rotating platform track the ArUco marker independently from the motion of the follower robot itself. The pose of the leader robot will be calculated by the follower robot from the ArUco marker detection. This paper involves the simulation of the experiment in gazebo software in the Robot Operating System (ROS).

This paper presents an open-source one-degree-of-freedom rotating device for multi-robot systems. The function of the device is to facilitate mutual, precise localization of robots in a group, using a limited number of sensors. The device can be mounted on the robot but also plays the role of a static observer and landmark if placed in the workspace. The presented approach can be used alongside a variety of control algorithms for teams of mobile robots. Each rotating device has 10 ArUco markers pasted on it. The aim of the rotating device is to determine the pose of another device in the environment. The mechanical design, electrical circuit, and source codes are uploaded on the web sites that provide other researchers with an easy way to replicate the solution. The authors have mentioned two types of controllers to track the ArUco markers on other rotating devices. One controller is Active Disturbance Rejection Control (ADRC) and the other one is a Proportional-Integral-Derivative (PID) controller. Extended Kalman Filter (EKF) is used to filter the data from ArUco marker detection and to calculate the optimized pose of another device in the environment. The Robot Operating System (ROS) is used to perform the experiments and to connect the rotating device with different controllers.

The paper proposes a new approach to distributed target assignment for a group of robots operating in a space with a circular obstacle. The simple approach of comparing functions of Cartesian distances, which works well in free space, does not give good results if a static obstacle exists. The authors aimed to find an approach that is as computationally effective as possible and yet produces a sufficiently good result. The proposed solution makes use of city block distance expressed in a specific reference frame that depends on the mutual positioning of the robot, the target, and the obstacle. In addition to presenting the idea, the results of numerical simulations were given to confirm the effectiveness of the proposed algorithm

Multi-robot systems have been widely used in a variety of applications to perform tasks cooperatively. A greater challenge is to design control when two teams of robots have to compete with each other when performing tasks. This paper presents a control algorithm for teams of differentially-driven mobile robots that perform such a task. The goal of each team is to follow an individual reference trajectory keeping the desired shape of the formation. In the transition state, the robots of one group must penetrate the other team to reach desired poses. Artificial potential functions are used to avoid collisions. They are shaped to reduce the risk of deadlocks between groups of robots. Numerical simulations illustrate the effectiveness of the proposed algorithm