The use of robotic vision to cut deformable food objects is a challenge in robotics that has the potential to improve autonomy and create new opportunities in industries such as medicine, the food industry, and services. While cutting rigid objects is relatively simple, cutting deformable objects like food items, which change shape during the cutting process, is a significant challenge that requires advances in various aspects of robotics, including vision, modeling, hardware design, and control. This paper discusses recent developments in vision-based approaches for robots cutting food items and highlights the main challenges that must be overcome to succeed in this task and outline some potential future research directions.

This paper proposes a novel fast adaptive back-stepping robust controller, based on the barrier Lyapunov function ( BLF ), to address the position and velocity constraints typically imposed in the design of Euler–Lagrange systems . The aim is to improve upon various aspects of conventional L 1 adaptive control and Model Reference Adaptive Control ( MRAC ). The proposed controller reduces complexity by eliminating the low-pass filter from the design process in L 1 adaptive control, resulting in faster convergence and enhanced robustness against nonlinear uncertainties, external disturbances, and actuator dynamics , which are crucial in real-world applications. The performance of the proposed scheme is evaluated on two different Euler–Lagrange systems: a 6-degree-of-freedom (6- DOF ) remotely operated vehicle ( ROV ) and a single-link robot manipulator. Key performance indicators such as settling time, overshoot percentage, control effort, and trajectory tracking error are used for assessment. The results confirm that the proposed controller outperforms both L 1 adaptive control and MRAC in terms of tracking accuracy and state estimation errors for both position and velocity outputs. Additionally, the proposed method demonstrates superior performance in handling actuator dynamics, mitigating matched nonlinear time-varying disturbances, and achieving precise trajectory tracking, even in the presence of input gain uncertainties. These improvements establish the proposed controller as a more robust and efficient alternative to traditional adaptive control methods.

MOMAV (Marco’s Omnidirectional Micro Aerial Vehicle) is a multirotor drone that is fully actuated, meaning it can control its orientation independently of its position. MOMAV is also highly symmetrical, making its flight efficiency largely unaffected by its current orientation. These characteristics are achieved by a novel drone design where six rotor arms align with the vertices of an octahedron, and where each arm can actively rotate along its long axis. Various standout features of MOMAV are presented: The high flight efficiency compared to arm configuration of other fully-actuated drones, the design of an original rotating arm assembly featuring slip-rings used to enable continuous arm rotation, and a novel control allocation algorithm based on sequential quadratic programming (SQP) used to calculate throttle and arm-angle setpoints in flight. Flight tests have shown that MOMAV is able to achieve remarkably low mean position/orientation errors of 6.6 mm, 2.1°( σ : 3.0 mm, 1.0°) when sweeping position setpoints, and 11.8 mm, 3.3°( σ : 8.6 mm, 2.0°) when sweeping orientation setpoints. • Analysis of arm geometries of fully-actuated drones shows that highly symmetrical drones with rotating arms outperform previously proposed geometries in terms of minimum flight efficiency across orientations. • Slip-rings are successfully used to transmit power to propeller motors trough a rotating joint. • A sequential quadratic programming (SQP) based control allocation algorithm for drones with rotating arms is presented, that enables easy tuning trough an arbitrary objective function.