Zhengmei Ma

A two-level closed-loop calibration framework is proposed to reduce odometry scale errors during motion and pose drift during stationary periods in Mecanum-wheeled mobile robots. At the upper calibration level, the planar displacement between the initial and final poses is calculated using the L2-norm, which reduces the influence of lateral deviation on distance measurements based on a single coordinate axis. Rotational displacement is obtained by accumulating normalized angular increments, thereby avoiding discontinuities when the yaw angle crosses the ±π boundary. A relay controller with a tolerance deadband is also introduced to reduce static-friction-induced stalling and oscillation near the target during low-speed calibration. At the lower odometry interface, the covariance assigned to wheel odometry measurements is adjusted according to the commanded zero-velocity state. During stationary periods, this adjustment increases the contribution of near-zero velocity measurements and limits the effect of residual velocity estimates and sensor noise on the fused pose. The identified longitudinal and rotational compensation factors are then updated online in the dead-reckoning node through an ROS 2 service. Unlike conventional ZUPT implementations, the proposed method does not require an additional zero-velocity pseudo-measurement node. Experiments were conducted on three near-horizontal surfaces: ceramic tile, epoxy resin, and asphalt. Across 720 bidirectional in-place rotation trials, the angular Error Reduction Rate ranged from (59.13%) to (96.58%). In 540 straight-line trials covering nine combinations of surface type and target distance, the overall mean absolute error decreased from 53.22 mm before calibration to 9.69 mm after calibration. Intermittent stop-and-go experiments were further performed using the EKF, UKF, RCKF, and a graph-based SLAM optimization framework implemented by slam_toolbox. For each estimation back-end, the estimated trajectory was evaluated by calculating its deviation from the corresponding synchronized /odom trajectory under the fixed-covariance and proposed ZUPT-based adaptive-covariance configurations; /odom was used as a common comparison baseline rather than as an absolute localization ground truth. The adaptive covariance strategy reduced the positional RMSE by (19.38%–67.44%) across the evaluated filtering back-ends. These results show that the proposed framework can reduce both motion-dependent odometry scale errors and stationary pose drift under the tested surface conditions.

Sharing with others is an important prosocial behavior, which is widely developed in early childhood. Numerous studies have demonstrated that single characteristics (routine behaviors or inter-group relations) of the recipient impact children’s sharing behavior. However, there is still a lack of understanding of the factors that influence sharing decisions in children with dual characteristics. In Experiment 1, 112 children aged 4–6 years (M_age = 5.55, girls account for 50%) participated in a sticker-sharing task, and the results indicated that older children (aged 5 and 6 years) were more willing to share and shared more stickers. Additionally, children shared more stickers with recipients who exhibited good routine behaviors compared to those who had poor behaviors. In total, 134 children aged 4–6 years (M_age = 5.47, girls account for 50.1%) participated in Experiment 2. The results indicated that children share more stickers with a friend than with a stranger. When recipients possessed dual characteristics, they shared more stickers with a stranger who had good routine behaviors than with a friend who had poor ones. Therefore, children are selective in their sharing, and when confronted with a recipient with dual characteristics, children prioritize the recipient’s prior performance of moral norms over interpersonal distance.