The applications of robotics are becoming more and more common in non-traditional industries such as the medical industry including robotic surgery and sample microtoming as well as food industry that include the processing of meats, fruits and vegetables. In this paper, the influence of the blade edge-shape and its slicing angle on the cutting of biomaterials are formulated and discussed based on the stress analysis that has been presented in part I. Through modeling the cutting force, an optimal slicing angle can be formulated to maximize the feed rate while minimizing the cutting forces. Moreover, the method offers a means to predict cutting forces between the blade and the biomaterials, and a basis for design of robust force control algorithms for automating the cutting of biomaterials

Bio-material cutting, such as meat deboning, is one the most common operations in food processing. Automating this process using robotic devices with closed-loop force control has shown some promise. The control of the force trajectory directly relates to the internal stress in the material being cut, and must provide enough force to initiate the cut. The ability to model the stress distribution in the bio-materials being cut would provide a better understanding of the influencing factors and help predict the required cutting force for the design of the cutting mechanism and for automating the cutting operations. This research is presented in two parts: part I models the stress distribution when a blade acts on the bio-material and part II discusses the principles of biomaterial cutting. Starting with modeling a point force in the normal and tangential direction on the boundary of a semi-infinite body, an analytical expression for the stress tensor has been obtained and simulated using direct integral method. This paper provides the theoretical basis for explaining the cutting phenomena and predicting the cutting forces, a topic to be presented in part II

The need for simultaneous measurement of more-than-one degrees-of-freedom (DOF) motions of an object can be found in numerous applications such as robotic assembly, precision machining, optical tracking, wrist actuators, and active joysticks. Conventional single-axis encoders, though capable of providing high-resolution linear or angular measurements, require additional mechanical linkages that often introduce frictions, backlashes, and singularities. In this paper, a non-contact optical sensor for three-DOF orientation measurement is present. Unlike other contemporary designs, which often require a specific pattern to be drawn on a surface, this optical sensor presented here relies only on microscope features on natural surfaces to detect incremental motions. Here we offer a detailed description of the design and the development of a dual-sensor system capable of measuring three-DOF motions in real time. The analysis that is essential for optimizing the sensor design has been validated experimentally.

The foot force/position adjustment method, the hybrid ground-learning model and active compliance force control method (or hybrid GLAC method), is introduced. The proposed method combines the active compliance force control with a ground-learning model for controlling the leg motion of the robot. The ground model reflects the relationship between the leg-end force and the sinkage. To verify the control quality of the proposed method together with the active compliance (AC) control and ground learning model control, experiments based on a robotic leg have been carried out on hard and soft terrain, respectively. The results show that the proposed method can ensure the actual leg-end force to follow closely the changing of reference forces both on hard and soft terrain. Therefore, by using the hybrid GLAC control, the error of the actual forces relative to the reference forces can be reduced more quickly than by using the AC method alone.

An essential consideration in the development of any motion planning method for a multilegged vehicle is to maintain stability during walking. We propose a leg-end supporting moment (LSM) method, to measure the stability margin of walking robots. The LSM is defined as the product of the force acting on a supporting leg and the distance between the supporting leg and the opposite edge of the support polygon. These forces and distances can be obtained online from the internal robot sensors. The stability margin evaluated by the LSM method, expressed as LSM/sub m/, is defined as the quotient of the leg-end supporting moment over the weight of the robot. If the LSM/sub m/ is greater than zero, the robot is in stable state. Otherwise the robot will be in marginally stable state or will lose its stability. The simulation results show that the LSM method is suitable for the stability analysis in cases with and without external forces acting on the body. The method is not only effective for flat terrain, but also for uneven terrain. These characteristics enable the LSM method to measure the stability margin of a walking robot in the case of drilling, dragging and manipulating, also in the case when the robot is used in forestry.