Category Archives: Industrial Robots

Review of NNs for solving manipulator inverse kinematics

January 25, 2024 12:49 ,

Daniel Cagigas-Mu�iz, Artificial Neural Networks for inverse kinematics problem in articulated robots, Engineering Applications of Artificial Intelligence,
Volume 126, Part D, 2023 DOI: 10.1016/j.engappai.2023.107175.

The inverse kinematics problem in articulated robots implies to obtain joint rotation angles using the robot end effector position and orientation tool. Unlike the problem of direct kinematics, in inverse kinematics there are no systematic methods for solving the problem. Moreover, solving the inverse kinematics problem is particularly complicated for certain morphologies of articulated robots. Machine learning techniques and, more specifically, artificial neural networks (ANNs) have been proposed in the scientific literature to solve this problem. However, there are some limitations in the performance of ANNs. In this study, different techniques that involve ANNs are proposed and analyzed. The results show that the proposed original bootstrap sampling and hybrid methods can substantially improve the performance of approaches that use only one ANN. Although all of these improvements do not solve completely the inverse kinematics problem in articulated robots, they do lay the foundations for the design and development of future more effective and efficient controllers. Therefore, the source code and documentation of this research are also publicly available to practitioners interested in adapting and improving these methods to any industrial robot or articulated robot.

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Review of RL applied to robotic manipulation

November 24, 2023 16:55 ,

��igo Elguea-Aguinaco, Antonio Serrano-Mu�oz, Dimitrios Chrysostomou, Ibai Inziarte-Hidalgo, Simon B�gh, Nestor Arana-Arexolaleiba, A review on reinforcement learning for contact-rich robotic manipulation tasks, Robotics and Computer-Integrated Manufacturing, Volume 81, 2023 DOI: 10.1016/j.rcim.2022.102517.

Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain.

Posted in: Applications of reinforcement learning to robots, Industrial robots , Tagged: ,

Adaptation of model-free RL to variations in the task under continuous state and action spaces applied to robot grasping

October 6, 2023 08:38 ,

Shahid, A.A., Piga, D., Braghin, F. et al. Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning, Auton Robot 46, 483\u2013498 (2022) DOI: 10.1007/s10514-022-10034-z.

This paper presents a learning-based method that uses simulation data to learn an object manipulation task using two model-free reinforcement learning (RL) algorithms. The learning performance is compared across on-policy and off-policy algorithms: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). In order to accelerate the learning process, the fine-tuning procedure is proposed that demonstrates the continuous adaptation of on-policy RL to new environments, allowing the learned policy to adapt and execute the (partially) modified task. A dense reward function is designed for the task to enable an efficient learning of the agent. A grasping task involving a Franka Emika Panda manipulator is considered as the reference task to be learned. The learned control policy is demonstrated to be generalizable across multiple object geometries and initial robot/parts configurations. The approach is finally tested on a real Franka Emika Panda robot, showing the possibility to transfer the learned behavior from simulation. Experimental results show 100% of successful grasping tasks, making the proposed approach applicable to real applications.

Posted in: Applications of reinforcement learning to robots, Industrial robots , Tagged: ,

A hierarchical POMDP system for robot manipulation

July 7, 2023 11:42 ,

Wenrui Zhao, Weidong Chen, Hierarchical POMDP planning for object manipulation in clutter, . Robotics and Autonomous Systems, Volume 139, 2021 DOI: 10.1016/j.robot.2021.103736.

Object manipulation planning in clutter suffers from perception uncertainties due to occlusion, as well as action constraints required by collision avoidance. Partially observable Markov decision process (POMDP) provides a general model for planning under uncertainties. But a manipulation task usually have a large action space, which not only makes task planning intractable but also brings significant motion planning effort to check action feasibility. In this work, a new kind of hierarchical POMDP is presented for object manipulation tasks, in which a brief abstract POMDP is extracted and utilized together with the original POMDP. And a hierarchical belief tree search algorithm is proposed for efficient online planning, which constructs fewer belief nodes by building part of the tree with the abstract POMDP and invokes motion planning fewer times by determining action feasibility with observation function of the abstract POMDP. A learning mechanism is also designed in case there are unknown probabilities in transition and observation functions. This planning framework is demonstrated with an object fetching task and the performance is empirically validated by simulations and experiments.

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State of the art in standards for Robotics

September 3, 2021 11:10 ,

Z.M. Bi, Zhonghua Miao, Bin Zhang, Chris W.J. Zhang, The state of the art of testing standards for integrated robotic systems, Robotics and Computer-Integrated Manufacturing
Volume 63, June 2020, DOI: 10.1016/j.rcim.2019.101893.

Technology standards facilitate the transparency in market and the supplies of products with good quality. For manufacturers, standards make it possible to reduce the costs by mass production, and enhance system adaptabilities through integrating system modules with the standardized interfaces. However, International standards on industrial robots such as ISO-9283 were developed in 1998, and they have not updated since then. Due to every-increasing applications of robots in complex systems, there is an emerging need to advance existing standards on robots for a broader scope of system components and system integration. This paper gives an introduction of the endeavors by National Institute of Standards and Technology (NIST); especially, it overviews the recent progresses on the standardized tests of robotic systems and components. The presented work aims to identify the limitations of existing industrial standards and clarify the trend of technology standardizations for industrial robotic systems.

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A MATLAB toolbox for controlling and programming KUKA robots and a list of robotics toolboxes

March 15, 2019 10:59 ,

M. Safeea and P. Neto, KUKA Sunrise Toolbox: Interfacing Collaborative Robots With MATLAB, IEEE Robotics & Automation Magazine, vol. 26, no. 1, pp. 91-96, 2019 DOI: 10.1109/MRA.2018.2877776.

Collaborative robots are increasingly present in our lives. The KUKA LBR iiwa, equipped with the KUKA Sunrise.OS controller, is one example of a collaborative/sensitive robot. This tutorial presents the KUKA Sunrise Toolbox (KST), a MATLAB toolbox that interfaces with KUKA Sunrise.OS. KST contains functionalities for networking, soft control in real time, point-to-point motion, parameter setters/getters, general purpose, and physical interaction. It includes approximately 100 functions and runs on a remote computer connected with the KUKA Sunrise controller via Transmission Control Protocol/Internet Protocol (TCP/IP). The potentialities of the KST are demonstrated in nine application examples.

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A new mathematical formulation of manipulator motion that simplifies dynamics and kinematics

May 28, 2018 08:08 ,

Labbé, M. & Michaud, F., Comprehensive theory of differential kinematics and dynamics towards extensive motion optimization framework, The International Journal of Robotics Research First Published May 20, 2018 DOI: 10.1177/0278364918772893.

This paper presents a novel unified theoretical framework for differential kinematics and dynamics for the optimization of complex robot motion. By introducing an 18×18 comprehensive motion transformation matrix, the forward differential kinematics and dynamics, including velocity and acceleration, can be written in a simple chain product similar to an ordinary rotational matrix. This formulation enables the analytical computation of derivatives of various physical quantities (e.g. link velocities, link accelerations, or joint torques) with respect to joint coordinates, velocities and accelerations for a robot trajectory in an efficient manner (O(NJ), where NJ is the number of the robot’s degree of freedom), which is useful for motion optimization. Practical implementation of gradient computation is demonstrated together with simulation results of robot motion optimization to validate the effectiveness of the proposed framework.

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Extending STRIPS-like symbolic planners with metrical/physical constraints for the domain of robotic manipulation

January 31, 2018 08:37 ,

Caelan Reed Garrett, Tomás Lozano-Pérez, and Leslie Pack Kaelbling, FFRob: Leveraging symbolic planning for efficient task and motion planning, The International Journal of Robotics Research Vol 37, Issue 1, pp. 104 – 136, DOI: 10.1177/0278364917739114
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Mobile manipulation problems involving many objects are challenging to solve due to the high dimensionality and multi-modality of their hybrid configuration spaces. Planners that perform a purely geometric search are prohibitively slow for solving these problems because they are unable to factor the configuration space. Symbolic task planners can efficiently construct plans involving many variables but cannot represent the geometric and kinematic constraints required in manipulation. We present the FFRob algorithm for solving task and motion planning problems. First, we introduce extended action specification (EAS) as a general purpose planning representation that supports arbitrary predicates as conditions. We adapt existing heuristic search ideas for solving strips planning problems, particularly delete-relaxations, to solve EAS problem instances. We then apply the EAS representation and planners to manipulation problems resulting in FFRob. FFRob iteratively discretizes task and motion planning problems using batch sampling of manipulation primitives and a multi-query roadmap structure that can be conditionalized to evaluate reachability under different placements of movable objects. This structure enables the EAS planner to efficiently compute heuristics that incorporate geometric and kinematic planning constraints to give a tight estimate of the distance to the goal. Additionally, we show FFRob is probabilistically complete and has a finite expected runtime. Finally, we empirically demonstrate FFRob’s effectiveness on complex and diverse task and motion planning tasks including rearrangement planning and navigation among movable objects.

Posted in: Industrial robots, Robot task planning , Tagged: ,

Calibrating a robotic manipulator through photogrammetry, and a nice state-of-the-art in the issue of robot calibration

September 20, 2017 08:03 ,

Alexandre Filion, Ahmed Joubair, Antoine S. Tahan, Ilian A. Bonev, Robot calibration using a portable photogrammetry system, Robotics and Computer-Integrated Manufacturing, Volume 49, 2018, Pages 77-87, DOI: 10.1016/j.rcim.2017.05.004.

This work investigates the potential use of a commercially-available portablephotogrammetry system (the MaxSHOT 3D) in industrial robot calibration. To demonstrate the effectiveness of this system, we take the approach of comparing the device with a laser tracker (the FARO laser tracker) by calibrating an industrial robot, with each device in turn, then comparing the obtained robot position accuracy after calibration. As the use of a portablephotogrammetry system in robot calibration is uncommon, this paper presents how to proceed. It will cover the theory of robot calibration: the robot’s forward and inverse kinematics, the elasto-geometrical model of the robot, the generation and ultimate selection of robot configurations to be measured, and the parameter identification. Furthermore, an experimental comparison of the laser tracker and the MaxSHOT3D is described. The obtained results show that the FARO laser trackerION performs slightly better: The absolute positional accuracy obtained with the laser tracker is 0.365mm and 0.147mm for the maximum and the mean position errors, respectively. Nevertheless, the results obtained by using the MaxSHOT3D are almost as good as those obtained by using the laser tracker: 0.469mm and 0.197mm for the maximum and the mean position errors, respectively. Performances in distance accuracy, after calibration (i.e. maximum errors), are respectively 0.329mm and 0.352mm, for the laser tracker and the MaxSHOT 3D. However, as the validation measurements were acquired with the laser tracker, bias favors this device. Thus, we may conclude that the calibration performances of the two measurement devices are very similar.

Posted in: Industrial robots, Robot models , Tagged: ,

Integrating humans and robots in the factories

April 20, 2016 16:30 ,

Andrea Cherubini, Robin Passama, André Crosnier, Antoine Lasnier, Philippe Fraisse, Collaborative manufacturing with physical human–robot interaction, Robotics and Computer-Integrated Manufacturing, Volume 40, August 2016, Pages 1-13, ISSN 0736-5845, DOI: 10.1016/j.rcim.2015.12.007.

Although the concept of industrial cobots dates back to 1999, most present day hybrid human–machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human–robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.

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