For telepresence mobile robots, avoiding collisions in unknown narrow environments has been a significant challenge. This paper defines an unknown environment as having no prior map and a narrow environment as having obstacles close to the robot’s size and sensor range. Autonomous navigation algorithms may failed in unknown narrow environment because of local minima. In conventional state-fusion shared control (SFSC), obstacle avoidance in narrow areas requires high-precision map information; therefore, the conventional SFSC cannot be applied in unknown environments. This paper proposes SFSC with dynamic window approach (DWA). The proposed method integrates human control commands with autonomous controller. Human commands are generated according to the input of the control device. Environmental information is obtained using a Lidar, and autonomous controller generates velocity candidates. The velocity commands are decided by DWA and humand commands. The proposed method significantly improves the robot’s maneuverability in unknown narrow environment. The effectiveness of the proposed method was validated through simulation and experiments. © 2013 IEEE.

In recent years, stiffness measurements of micro-order objects are required. For example, analysis of the mechanical properties of a new material, such as a soft crystal, has been attracting attention. In the conventional methods to measure the stiffness, the grasping object is held without force feedback. Therefore, there is a possibility of damaging the objects. To solve this problem, this paper proposes a stiffness measurement method based on micro-macro bilateral control for micro-order objects. The micro-macro bilateral system consists of the leader system that is easy for the operator to manipulate, and the follower system that is sized to operate in a small environment. For the precise measurement, grasping the object is achieved by the micro-macro bilateral control. In this grasping, the total stiffness, including the object and the follower system, is measured in real-time. The stiffness of the follower system is identified by the preliminary experiment. As a result, the stiffness of the object is estimated by subtracting the total stiffness from the identified stiffness of the follower system. The validity of the proposed method was confirmed by the experimental results. © 2025 Wiley Periodicals LLC.

This paper proposes a narrow-route path planning for a mobile robot using deep deterministic policy gradient (DDPG) considering a drive system. In this paper, a narrow road is defined as a space in which a mobile robot with a non-holonomic constraint cannot move without performing turnabouts. There are various drive systems for mobile robots such as an independent two-driven wheels type, a steering type, and a car-like type. In an independent two-driven wheels type, the mobile robot plans the path including on-the-spot turning. On the other hand, in a steering type and a car-like type, the mobile robot performs the turnabouts on a narrow road. In wheeled robots, differences in drive systems can be expressed as a turning radius limit. The proposed method generates narrow-route path planning considering a turning radius limit due to a drive system. The proposed method is based on machine learning and uses DDPG as reinforcement learning. The trained model determines the translational and angular velocities that include turnabouts / on-the-spot turning according to environmental information in real time. In the simulation and experiments, we confirmed that the proposed method allowed a mobile robot, with or without a turning radius limit, to pass through a narrow road. In addition, the robustness against the trained model was evaluated by several narrow roads that differed from the learning environment. In the case of the drive system with the turning radius limit, the success rate of driving on narrow roads including different learning environments was 94% in simulation and 85% in experiments. Therefore, the effectiveness of the proposed method was confirmed by the simulation and experimental results.