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The robot offers interesting capabilities, but suffers from a lack of stiffness. The proposed solution is to introduce redundancies for the overall improvement of different capabilities. The management of redundancy associated with the definition of a set of kinematic, mechanical and stiffness criteria enables path planning to be optimized.

The resolution method is based on the projection onto the kernel of the Jacobian matrix of the gradient of an objective function constructed by aggregating kinematic, mechanical and stiffness weighted criteria. Optimized redundancy management is applied to the 11-DoF (degrees of freedom) cells to provide an efficient placement of turntable and track. The final part presents the improvement of the various criteria applied to both 9-DoF and 11-DoF robotic cells.

The first application concerns the optimized placement of a turntable and a linear track using 11-DoF architecture. Improved criteria for two 9-DoF robotic cells, a robot with parallelogram closed loop and a Tricept are also presented. Simulation results present the contributions of redundancies and the leading role of the track.

The redundancy-based optimization presented and the associated simulation approach must be completed by the experimental determination of the optimization criteria to take into account each machining strategy.

This work in robotics machining relates to milling operations for automotive and aerospace equipment. The study is carried out within the framework of the RobotEx Equipment of Excellence programme.

The resolution method to optimized path planning is applied to 9- and 11-DoF robotic cells, including a hybrid robot with a parallelogram closed loop and a Tricept PKM.

The mechanization of the meat cutting companies has become essential due to the lack of skilled workers and to working conditions. This paper deals with the analysis of human gestures in order to improve the performance of a redundant robotic cell. The aim is to define optimization criteria linked to the process and the human gesture analysis to improve the cutting process with a redundant robotic cell.

This paper deals with an optimized path planning of complex tasks based on the human arm analysis. The first part details the operator's manual work. The robotized cutting strategy using bones as a guide associated with an industrial force control leads to the tasks redefinition. Thus, the analysis of the arm during the tasks is presented. With a robotic model, the authors evaluate the relevance of two criteria (kinematic and mechanical) that the operator naturally manages. These criteria are used to improve the robotized cutting process by using redundancy. Simulation work and experimentation are presented to show the enhanced performance.

The paper explains how to define optimization criteria based on human arm analysis to realize cutting operations which require force or dexterity performance. It presents a study on the criteria weighting on a robotic arm model established through human arm analysis. The optimized cutting process clearly shows improvement.

The scalability of the ham implied the definition of iterative trajectories to follow the curvature of the bone. Due to the use of an industrial force control, no online optimization can be achieved. The off-line optimization implies that the boundary of the trajectory space is technically feasible. Nevertheless, more information has to be extracted from the deboning process such as vision data in order to improve cutting quality.

This study was carried out within the framework of several national and European projects (FUI SRDViand, ANR ARMS, FP7 Echord Dexdeb) in collaboration with ADIV (Meat Institute Development Agency). The redundant robotic cell was developed and implemented at ADIV and used for feasibility studies in connection with SME/SMI French sector.

The paper deals with the cutting of soft bodies such as meat and complex human gesture analysis, which constitute an innovative challenge for the coming years in order to help or replace humans in industrial meat companies with difficult working conditions.

The mechanization of the meat cutting companies has become essential. This paper aims to study the feasibility of cutting operations for beef and boning operations for pork ham. The study aims to enhance industrial robots application by using vision or force control.

The paper opted for an industrial robot‐based cell. The first part of this paper focuses on in‐depth study of operators' expertise, so as to translate their actions into automatable operative tasks and to identify the constraints of robotization. It details more particularly a cutting strategy using a bone as a guide which shows the complexity of the process. The analysis of the cutting and task constraint parameters involves the use of a kinematically redundant robotized cell with force control. Then the cell model is developed, and experimentation is performed.

The paper explains how to solve the problem of the high variability of the size for beef carcass. It gives several ideas to realize the boning of pork ham. It develops the strategies, the sensors and the cell architecture to make this type of operations.

Because of the choice of an existing industrial robot, the tool paths with force control are limited. Therefore, new force control instructions have to be developed to continue this work on more complicated operations.

This study was carried out within the framework of the SRDViand project in cooperation with meat industry partners.

The paper fulfils an identified need to study the beef quartering which is a high‐variability operation and ham deboning which is a high precision operation.

Precision aluminium moulding makes possible the production of large‐size, complex and high‐technology cast parts. However, industrial requirements linked to economic and safety reasons call into question the manual performance of finishing operations. The purpose of this paper is to enhance industrial robot applications by using vision and redundancy optimization to improve their capability.

After having presented the concepts associated with machine and kinematics capability, the paper first describes the finishing constraints related to the process and the study of inaccuracy factors. Adjusting the trajectory by vision minimizes some inaccuracy factors but does not take into account the structure loading. Therefore, the authors present the optimization, kinematics and precision criteria as well as the multi‐objective method developed by integrating the loading aspect. This method has been verified by simulation and the results validated on industrial parts.

The paper presents an improvement in machine capacities based on redundancy and an optical 3D measurement system. It develops the strategies, sensors and cell architecture to perform finishing operations.

The finishing of high‐technology structural cast parts requires the completion of the machining and polishing processes adapted to each part. The choice was made to develop a robotic cell dedicated to integrating specific features, in contrast to machine tools.

This study was carried out within the framework of the Eureka SANDCAST project in cooperation with the Alcan group, specialized in high‐technology moulded aluminum parts.

The paper presents an approach to robotic cell capability improvement. The robotic cell is dedicated to finishing operations, by machining and polishing large cast aluminum parts; the objectives are to improve machine capability and kinematics capacity with vision and redundancy management.