Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills. Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives. By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives. The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation. By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment. We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform. The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously

ENERGY MODELLING AND SIMULATION FOR INDUSTRIAL ROBOTS

openThis thesis explores energy modelling and simulation techniques tailored for industrial robots, with a primary objective of advancing energy efficiency. Focusing on the ABB-IRB-140 robot, the study utilizes MATLAB to develop comprehensive energy models for three distinct motions. The research unfolds through various objectives, including formulating kinematics, developing motion planning algorithms, conducting simulations, and constructing energy consumption models for individual robot joints. A pivotal aspect of this research lies in the development of a robust motion planning algorithm, recognized as a fundamental pillar that underpins the entire endeavour. This algorithm serves as a critical mechanism for optimizing energy efficiency and seamlessly integrating energy modelling techniques into real-world industrial applications. While MATLAB customization caters to specific robot characteristics, the developed algorithm boasts versatility, enabling its adaptation across a spectrum of industrial contexts and robot configurations. By elucidating the intricate relationship between motion planning and energy consumption in industrial robots, this research contributes to a deeper understanding of energy dynamics within the industrial landscape. Moreover, the insights gleaned hold the promise of significant advancements in energy-efficient robotics, fostering sustainable practices and mitigating the environmental impact associated with industrial operations. Ultimately, this thesis represents a crucial step forward in the quest for energy optimization, highlighting the transformative potential of interdisciplinary research at the nexus of engineering and sustainability

Automation of product packaging for industrial applications

[EN] This work presents a robotic-based solution devised to automate the product packaging in industrial environments. Although the proposed approach is illustrated for the case of the shoe industry, it applies to many other products requiring similar packaging processes. The main advantage obtained with the automated task is that productivity could be significantly increased. The key algorithms for the developed robot system are: object detection using a computer vision system; object grasping; trajectory planning with collision avoidance; and operator interaction using a force/torque sensor. All these algorithms have been experimentally tested in the laboratory to show the effectiveness and applicability of the proposed approach.This work has been partly supported by Ministerio de Economia y Competitividad of the Spanish Government [Grant No. RTC201654086 and PRI-AIBDE-2011-1219], by the Deutscher Akademischer Austauschdienst (DAAD) of the German Government (Projekt-ID 54368155) and by ROBOFOOT project [Grant No. 260159] of the European Commission.Perez-Vidal, C.; Gracia, L.; De Paco, J.; Wirkus, M.; Azorin, J.; De Gea, J. (2018). Automation of product packaging for industrial applications. International Journal of Computer Integrated Manufacturing. 31(2):129-137. https://doi.org/10.1080/0951192X.2017.1369165S12913731

Optimized joint motion planning for redundant industrial robots

The paper presents a model and solution method for optimized robot joint motion planning of redundant industrial robots that execute a set of tasks in a complex work environment, in face of various technological and geometric constraints. The approach aims at directly exploiting redundancy to optimize a given performance measure, e.g., cycle time. Alternative configurations along the path are captured in a graph model, whereas bi-directional transition between task and configuration spaces facilitates generating relevant, collision-free configurations only. Re-parametrization of the trajectory warrants compliance with the robot's kinematic constraints. Successful application of the method is demonstrated in remote laser welding. (C) 2016 CIRP

Point trajectory planning of flexible redundant robot manipulators using genetic algorithms

The paper focuses on the problem of point-to-point trajectory planning for flexible redundant robot manipulators (FRM) in joint space. Compared with irredundant flexible manipulators, a FRM possesses additional possibilities during point-to-point trajectory planning due to its kinematics redundancy. A trajectory planning method to minimize vibration and/or executing time of a point-to-point motion is presented for FRMs based on Genetic Algorithms (GAs). Kinematics redundancy is integrated into the presented method as planning variables. Quadrinomial and quintic polynomial are used to describe the segments that connect the initial, intermediate, and final points in joint space. The trajectory planning of FRM is formulated as a problem of optimization with constraints. A planar FRM with three flexible links is used in simulation. Case studies show that the method is applicable