Technological advancements in various domains have broadened the application horizon of robotics to an incredible extent. Highlighting a very recent application area, this paper presents a comprehensive review of robotics application in food industry. Robots essentially have the potential to transform the processes in food processing and handling, palletizing and packing and food serving. Therefore, recent years witnessed tremendously increased trend of robots deployment in food sector. Consequently, the aspects related with robot kinematics, dynamics, hygiene, economic efficiency, human-robot interaction, safety and protection and operation and maintenance are of critical importance and are discussed in the present review. A comparison of actual robots being used in the industry is also presented. The review reveals that the food serving sector is the new potential area in which ample research opportunities exist by integrating advancements from various technology domains. It is anticipated that wider dissemination of research developments in ‘robo-food’ will stimulate more collaborations among the research community and contribute to further developments.

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Prospects of robotics in food industry

Today, robotics is an important cornerstone of modern industrial production. Robots are used for numerous reasons including reliability and continuously high quality of work. The main decision factor is the overall efficiency of the robotic system in the production line. One key issue for this is the optimality of the whole set of robotic movements for industrial applications, which typically consist of multiple atomic tasks such as welding seams, drilling holes, etc. Currently, in many industrial scenarios such movements are optimized manually. This is costly and error-prone. Therefore, researchers have been working on algorithms for automatic computation of optimal trajectories for several years. This problem gets even more complicated due to multiple additional factors like redundant kinematics, collision avoidance, possibilities of ambiguous task performance, etc. This survey article summarizes and categorizes the approaches for optimization of the robotic task sequence. It provides an overview of existing combinatorial problems that are applied for robot task sequencing. It also highlights the strengths and the weaknesses of existing approaches as well as the challenges for future research in this domain. The article is meant for both scientists and practitioners. For scientists, it provides an overview on applied algorithmic approaches. For practitioners, it presents existing solutions, which are categorized according to the classes of input and output parameters.

Robotic Task Sequencing Problem: A Survey

In this paper, we present a distributed fault detection and isolation (FDI) strategy for a team of networked robots that builds on a distributed controller–observer schema. Remarkably different from other works in literature, the proposed FDI approach makes each robot of the team able to detect and isolate faults occurring on other robots, even if they are not direct neighbors. By means of a local observer, each robot can estimate the overall state of the team and it can use such an estimate to compute its local control input to achieve global tasks. The same information used by the local observers is also used to compute residual vectors, whose aim is to allow the detection and the isolation of actuator faults occurring on any robot of the team. Adaptive thresholds are derived based on the dynamics of the residual vectors by considering the presence of nonzero initial observer estimation errors, and noise terms affecting state measurement and model dynamics. The approach is validated via both numerical simulations and experiments involving four Khepera III mobile robots.

Observer-Based Decentralized Fault Detection and Isolation Strategy for Networked Multirobot Systems

Subject review Pressing requirements of improved and enhanced productivity in industrial applications has necessitated deployment of robot to automate tasks. Manipulator based articulated robots for today’s industrial applications vary widely in terms of number of Degree Of Freedom (DOF), payload capacity, Range Of Motion (ROM), control implementation and mountable configurations. This paper presents a comprehensive and systematic review of industrial robots with a focus on their application areas. The study of manipulators for diversified applications has highlighted the need of sophisticated algorithms for their control and trajectory planning. Both of these key concepts are discussed in the paper. The control of industrial manipulator is important for accomplishing tasks requiring high precision, repeatability and reliability by mitigating the effects of disturbances. The trajectory planning is vital for time optimization, energy optimization and collision avoidance to ensure most appropriate trajectory for a given task in an environment. The application oriented review offers readers opportunities to generate ideas applicable to their operations and to conform feasibility of their ideas.

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Automating industrial tasks through mechatronic systems – a review of robotics in industrial perspective

Urban challenges and opportunities to promote sustainable food security through smart cities and the 4th industrial revolution

Today's industrial manipulators are more and more demanding in terms of productivity. This goal could be achieved by increasing speed of the robot manipulator and/or by optimizing the trajectories followed by manipulators while performing manufacturing, assembling, welding or similar tasks. Focusing on the second aspect, this research proposes a method based on genetic algorithms by exploiting CAD (computer aided design) capabilities to optimize and simulate cycle time in performing classical manufacturing tasks. The goal is to determine the shortest distance traveled by the robot manipulator in the coordinate space for every pair of successive points. In addition, our optimization procedure considers supplementary factors such as inverse kinematic model (IKM) and relative position/orientation of the manipulator w.r.t task points. All these factors were statistically assessed to determine both individual and cross influences in finding the optimal solution. The proposed approach has been validated on a real life setup, involving a 6-DOFs (degrees of freedom) industrial robot manipulator when performing a spot welding task on a car body. The obtained results are promising and show the effectiveness of the proposed strategy. HighlightsThis paper proposes a strategy for planning and scheduling of the industrial robotic manipulators.The multi-objective optimization strategy is based on Genetic Algorithms (GAs).A statistical study to investigate influence of various factors on optimization is detailed.The proposed strategy is validated by considering an industrial task of spot welding on a car body.The obtained results are promising and show the efficacy of the proposed strategy.

Time scheduling and optimization of industrial robotized tasks based on genetic algorithms

This paper presents Industrial Robotics Simulation Design Planning and Optimization platform named IRoSim, which is based on SolidWorks Application Programming Interface (API) to offer an intuitive and convertible environment for designing and simulating robotized tasks. The core idea is to integrate features of mechanical CAD and robotics CAD into the same platform to facilitate the development process through the designed Graphical User Interface (GUI) which permits user friendly interaction. The platform includes various 3D models that are essential for developing any robotized task and offers possibility to integrate new models in simulation. Robotic manipulator library is one such example which contains several types of serial arms with different combinations of revolute and prismatic joints. The platform provides most important steps such as defining the task, CAD learning of the end-effector's trajectory, checking the manipulator's reachability to perform a task, simulating the motion and finally validating the manipulator's trajectory to avoid possible collisions. To demonstrate the efficiency of the proposed approach, two frequent and important tasks (spot welding and painting) using a 6-Degree Of Freedom (DOF) industrial robotic manipulator have been considered. The output of the proposed strategy provides collision-free trajectory of the manipulator's motion which can be directly mapped to a real site. Moreover, the approach permits addressing the problems related with the real implementation of robotized tasks.

IRoSim: Industrial Robotics Simulation Design Planning and Optimization platform based on CAD and knowledgeware technologies

Industrial implementation of robots is to perform the assigned tasks in the minimum possible time in the cycle comes up to increase productivity and reduce the cost. The cycle time is strongly linked to the robot trajectory cycle to the task. However, the optimization of the robot trajectory cycle the robot visited a set of points which represent the robotics task. Similar to persons in traveling the robot execute the task into shorter time if has a shorter path. However the trajectory cycle of the robot is strongly related to the displacement in coordinate space rather than operational space. In fact, the shorter distance between two task points is the shorter distance between two configurations. Since robot has different configurations in each task point the minimum trajectory should be chosen between each successive configuration. However the order of visiting the task point also affects the trajectory distance. Moreover the relative robot position to the task also has a trivial effect on the task time. In this work we develop a method to optimize the order of visiting the task point taking into consideration the robot configuration and the placement of the robot in the robotized site. Mainly, this method is based on Genetic Algorithms and it takes into consideration the multiplicity solutions of the robot Inverse Kinematics Model (IKM), the task point visit order and the placement of robot at the same time.

Genetic Algorithms based method for time optimization in robotized site

Industrial robot manipulators must work as fast as possible in order to increase the productivity. This goal could be achieved by increasing robots speed or/and optimizing the trajectories followed by robots while performing assembly, welding or similar tasks. In our contribution, we focus on the second aspect and we target the shortening of paths between task-points. In other words, the goal is to find the shorter traveled distance between different configurations in the coordinate space. In addition to the short distance goal, we aim as well to impose both IKM (Inverse Kinematic Model) and the relative position and orientation of the manipulator regarding the task-points. To this end, we propose an optimization method based on Genetics Algorithms. The method is validated via numerical and graphical simulation, where, results show that the total cycle time required to perform a spot-welding task of an industrial car-body by a 6-DOFs (Degree Of Freedoms) industrial manipulator was drastically reduced.

Amal Meddahi

Papers

Time scheduling and optimization of industrial robotized tasks based on genetic algorithms

IRoSim: Industrial Robotics Simulation Design Planning and Optimization platform based on CAD and knowledgeware technologies

Genetic Algorithms based method for time optimization in robotized site

Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators

Modelling of Robotized Site and Simulation of Robot's Optimum Placement and Orientation Zone