V2F: Vision-Informed Grasp Force Prediction for Damage-Aware Robotic Handling of Date Fruits

This paper presents a vision-informed grasp force prediction framework for robotic handling of date fruits. Addressing the dual challenge of high detachment forces and low bruise thresholds, we first conduct mechanical characterization on date samples to define a safe grasping envelope and quantify the relationship between fruit geometry and bioyield stress. In this work, we develop a Vision-to-Force (V2F) pipeline that combines computer vision-based segmentation, active-contour refinement, and geometric feature extraction with a physics-informed residual neural network that augments a Hertz contact equation. The resulting model maps non-contact visual descriptors and cultivar metadata to predict a safe grasp force with mean validation performance of $R^2 \approx 0.7$ across unseen cultivar groups, which is a good result given the inherent mechanical variability of biological tissue. Experimental validation using a gripper and load cell indicates that the predicted forces enable stable manipulation of different types of date fruits, with residual deformations below 1 mm and no observable damage. These results show that pre-emptive, vision-driven force estimation% can replace slow and potentially damaging tactile exploration , enabling safer robotic handling of fragile fruits.

Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments

We present a robust control and estimation framework for quadrotors operating in Global Navigation Satellite System(GNSS)-denied, non-inertial environments where inertial sensors such as Inertial Measurement Units (IMUs) become unreliable due to platform-induced accelerations. In such settings, conventional estimators fail to distinguish whether the measured accelerations arise from the quadrotor itself or from the non-inertial platform, leading to drift and control degradation. Unlike conventional approaches that depend heavily on IMU and GNSS, our method relies exclusively on external position measurements combined with a Extended Kalman Filter with Unknown Inputs (EKF-UI) to account for platform motion. The estimator is paired with a cascaded PID controller for full 3D tracking. To isolate estimator performance from localization errors, all tests are conducted using high-precision motion capture systems. Experimental results in a moving-cart testbed validate our approach under both translational in X-axis and Y-axis dissonance. Compared to standard EKF, the proposed method significantly improves stability and trajectory tracking without requiring inertial feedback, enabling practical deployment on moving platforms such as trucks or elevators.