This paper proposes a velocity-proportional virtual force-based admittance control method to suppress desired position accumulation during contact in robot direct teaching. In conventional admittance control, the force that an operator applies to the handle is converted into a torque component about the robot drive axis and then used to generate the desired position. When the end fixture is constrained by contact, however, the desired position can keep moving away from the actual position, thereby increasing the tracking error and motor torque. The proposed method detects this accumulation state on the basis of the motor torque magnitude and the sign consistency between the motor torque and the converted torque component. Upon detection, the converted torque component is replaced with a virtual component of opposite sign that is proportional to the desired velocity, thereby damping the desired velocity and limiting further desired position updates. In contact experiments on the third rotational axis of an ARF module, the conventional method reached 300% of the rated torque and caused a servo driver overload error. In contrast, the proposed method limited the maximum position error to approximately 0.086° and the maximum motor torque to approximately 63% of the rated torque, without an overload error.
This paper presents a tiltable cable-suspended aerial manipulation (SAM) system designed to improve the utility of aerial manipulators in industrial settings. Although drone-robot arm systems have shown promise, suspended configurations encounter notable stability challenges, particularly during inclined operations. To tackle these challenges, we performed simulation-based analyses focusing on the system's kinematics, dynamic response, and thrust requirements under tilted conditions. We utilized Monte Carlo sampling and forward kinematics to assess the workspace and manipulability. The findings indicated that each propeller needs to generate over 32 N of thrust to maintain stable control. Additionally, simulation experiments showed that the system can uphold its attitude and execute end-effector motions effectively, even in the presence of disturbances. This study establishes a foundational verification step toward developing a physical SAM system capable of safe and robust operation in inclined scenarios.
This paper introduces PongBot, a quadruped robot developed for preparation in the Dronebot Challenge held in Jangseonggun, Jeollanam-Do, South Korea in November 2020. The Dronebot Challenge, hosted by the Army Headquarters, is a competition to demonstrate that drones and robots can be useful for military purposes. In 2020, this competition consisted of a total of 8 events and we participated in the ‘Traveling on rough terrain’ event, which consisted of various terrains, such as, slopes, unpaved roads, and streams. PongBot is a quadruped robot that uses an electric motor and can walk for more than an hour on various terrains. Also, according to the rules of the competition, the robot had a system which could be remotely controlled from a ground control station. In addition, by applying the SLAM algorithm, the robot operator received information about its surrounding environment, thereby deriving records to facilitate the operation. The performance of this robot system and SLAM algorithm was verified through this competition.
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