In this study, we propose an end-effector gripper for a weed-removal robot used in mulch sheet agriculture. Unlike conventional open-field agriculture, weeds in mulch sheet agriculture are often located close to crops. Therefore, the proposed gripper adopts a planetary gear structure and uses only a single actuator to realize two different motions, namely, gripping the weed and pulling it out in the vertical direction. The proposed gripper also incorporates a toggle mechanism that provides force amplification, enabling a large grasping force to be generated from a small input torque. Through parameter design of the planetary gear structure, the proposed gripper first completes the grasping of the weed via the rotation of the carrier under continuous torque applied to the sun gear, and then sequentially drives the weed and the entire mechanism to move vertically through the rotation of the outer ring gear to perform the pulling-out motion. A prototype of the gripper was fabricated using 3D printing to verify the effectiveness of the proposed mechanism. The prototype has a total weight of only 538 g. The total time from the start of motion to weed removal is only 0.195 s, and the maximum grasping force can reach 87 N. Field experiments show that the proposed device can quickly remove weeds while maintaining soil integrity, thereby protecting the seeds and roots of crops from damage.
We propose a task-priority-aware motion-control method that links task progress to online modification of model predictive path integral (MPPI) control parameters. Human–robot collaborative assembly with multiple robotic arms requires both task-level coordination and reactive motion generation in a shared workspace. A task planner can determine which robot should proceed first, whereas a motion controller must continuously respond to the positions of the human and the other robot. However, if these functions are designed independently, fixed avoidance settings can cause symmetric yielding, deadlock, or unnecessary suspension of robot motion in spatially conflicting tasks. Therefore, we propose a method in which a task-management layer determines the current priority relation between two robotic arms and updates selected goal-tracking and collision-related cost weights, together with the scales of the robot collision models used by the local MPPI controllers. This mechanism creates task-dependent asymmetric yielding while retaining reactive avoidance of the operator and the other robot. The method does not jointly optimize the trajectories of all controllers; instead, each robot computes its motion locally under task-level priority coordination. The proposed method was implemented in a collaborative assembly-support system involving one operator and two robotic arms. The experiments demonstrate inter-robot conflict resolution, avoidance of an unexpected human approach, and concurrent task execution during human-guided positioning of a heavy object. In the demonstrated task, the mean completion time was 203.7 s under the proposed control condition and 255.4 s under manual sequential control, corresponding to a 20.2
Abstract Introduction Dynamic postural control is a key determinant of balance and an essential factor in fall prevention, with center of pressure (COP) adjustments reflecting the motor mechanisms that ensure balance during forward-reach tasks. A ceiling-mounted fall impact mitigation robot has been introduced for balance-challenging tasks, including the Functional Reach Test (FRT); however, its effect on postural control in older adults remains unclear. Objective To investigate whether a ceiling-mounted fall impact mitigation robot supports changes in dynamic postural control during a challenging forward-reach task in healthy older adults. Methods Fifteen older adults (73.0 ± 7.4 years) were randomly assigned to experimental ( n = 8) or control ( n = 7) groups. All participants completed a pre-test FRT, followed by progressively challenging reaches starting at 98% of the individual maximum (+ 2% per successful reach) until failure, followed by a post-test FRT; the experimental group performed the post-test FRT with and without the robot, whereas the control group performed the post-test FRT without the robot. The COP in the anteroposterior direction was recorded from dual force plates. The primary and secondary outcomes were the anterior COP excursion and FRT distance, respectively. Results In the experimental group, COP excursion significantly increased after the challenging tasks ( p = 0.021), with a significant increase in the COP excursion between the pre-test and post-test FRT in the robotic condition ( p = 0.046). Conclusion The ceiling-mounted fall impact mitigation robot immediately increased the anterior COP excursion during a challenging forward-reach task, thus supporting changes in dynamic postural control in healthy older adults.
Soft robots exhibit rich deformation and contact interactions that are particularly suited to crawling locomotion. At the same time, these properties make modeling and control challenging, especially in systems with continuous deformation and strong ground contact. While simulation-based reinforcement learning has been explored, transferring learned policies to physical systems often suffers from model inaccuracies. As a result, direct real-to-real reinforcement learning has attracted increasing attention, although existing demonstrations for soft crawling robots have largely been limited to one-dimensional sagittal-plane motion. In this study, we extend real-to-real reinforcement learning to two-dimensional planar crawling using an electrically driven tendon-wire soft robot. A soft caterpillar robot equipped with four independently actuated motors enabling twisting and lateral deformation was trained directly on the physical system for approximately 2.5 h. The learned policy enabled the robot to reach a target located about 500 mm away in approximately 50 s, while exhibiting diverse crawling behaviors. Successful goal reaching was also observed from randomly initialized starting positions. These results demonstrate that reinforcement learning can effectively exploit the complex deformation and contact dynamics of soft robots by directly operating a physical robot in the real world, enabling the generation of steerable crawling behaviors.
In recent years, spherical wheels have attracted significant attention for their structural characteristics and are used for step climbing and omnidirectional movement. Although many sphere-driven robots employ friction drive systems and thus face challenges such as traveling on uneven surfaces, roller slippage, and the intrusion of debris between the sphere and the rollers, further improvements in running performance are expected. If a kinematic theory applicable to arbitrary configurations of sphere-based mechanisms is established, their use and applications are expected to expand further. In this paper, we express the general kinematics of mechanisms driven by multiple rollers using forward kinematic equations and discuss their comprehensive relationship with related studies. In addition, we validate the kinematics using a sphere measurement device, assuming various rotational motions of the sphere.
