Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments

In both industrial and service domains, a central benefit of the use of robots is their ability to quickly and reliably execute repetitive tasks. However, even relatively simple peg-in-hole tasks are typically subject to stochastic variations, requiring search motions to find relevant features such as holes. While search improves robustness, it comes at the cost of increased runtime: More exhaustive search will maximize the probability of successfully executing a given task, but will significantly delay any downstream tasks. This trade-off is typically resolved by human experts according to simple heuristics, which are rarely optimal. This paper introduces an automatic, data-driven and heuristic-free approach to optimize robot search strategies. By training a neural model of the search strategy on a large set of simulated stochastic environments, conditioning it on few real-world examples and inverting the model, we can infer search strategies which adapt to the time-variant characteristics of the underlying probability distributions, while requiring very few real-world measurements. We evaluate our approach on two different industrial robots in the context of spiral and probe search for THT electronics assembly.Comment: 7 pages, 5 figures, accepted to the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan For code and data, see https://github.com/benjaminalt/dps

Neural Network Learning Algorithms for High-Precision Position Control and Drift Attenuation in Robotic Manipulators

In this paper, different learning methods based on Artificial Neural Networks (ANNs) are examined to replace the default speed controller for high-precision position control and drift attenuation in robotic manipulators. ANN learning methods including Levenberg–Marquardt and Bayesian Regression are implemented and compared using a UR5 robot with six degrees of freedom to improve trajectory tracking and minimize position error. Extensive simulation and experimental tests on the identification and control of the robot by means of the neural network controllers yield comparable results with respect to the classical controller, showing the feasibility of the proposed approach

Task scheduling based on genetic algorithm for robotic system in 5G manufacturing industry

With the development of 5G technology, the robotic system has been bought into industrials. Even manufacturers plan the task flow by using project management. An error may occur and make the tasks overlap because they use the traditional scheduling method. It may waste much time between the tasks, and robots will get into standby mode to wait for the next tasks if the scheduling is failed. An algorithm with flexible scheduling is needed to arrange the tasks accordingly with the shortest total completion time. Genetic Algorithm (GA) is applied to task scheduling, and it provides a better solution from previous results or arrangements due to iteration. In this study, an analysis involves multi robots to complete various industrial operations, consisting of multi-tasks. To save time during processing and costs in production, GA may help it have the optimal value about total complete time to avoid any wastage. In short, the manufacturer will have higher productivity and better performance among the robots when applied a suitable Task Scheduling in the industry or workplace

Generation of Optimized Robotic Assembly Sequence using Soft Computing Methods

The assembly process is one of the most time consuming and expensive manufacturing activities. The cost of assembly on an average is 10-30% of the manufacturing cost of a commercial product. The ratio between cost and performance of assembly has gradually increased with respect to the other phases of the manufacturing process and in recent years, this fact has caused a growing interest by industry in this area. Robotic assembly system which comes under the automated assembly system ncorporates the use of robots for performing the necessary assembly tasks. This is one of the most flexible assembly systems to assemble various parts into desired assembly (usable end-product). Robotic assembly systems are the programmable and have the flexibility to handle a wide range of styles and products, to assemble the same products in different ways, and to recover from errors. Robotic assembly has the advantage of greater process capability and scalability. It is faster, more efficient and precise than any conventional process. A variety of optimization tools are available for application to the problem. It is difficult to model the present problem as an n-p problem. Finding the best sequence generation involves the conventional or soft-computing methods by following the procedures of search algorithms

Mobile robotics in agricultural operations: A narrative review on planning aspects

The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.</jats:p

Few-shot Quality-Diversity Optimization

In the past few years, a considerable amount of research has been dedicated to the exploitation of previous learning experiences and the design of Few-shot and Meta Learning approaches, in problem domains ranging from Computer Vision to Reinforcement Learning based control. A notable exception, where to the best of our knowledge, little to no effort has been made in this direction is Quality-Diversity (QD) optimization. QD methods have been shown to be effective tools in dealing with deceptive minima and sparse rewards in Reinforcement Learning. However, they remain costly due to their reliance on inherently sample inefficient evolutionary processes. We show that, given examples from a task distribution, information about the paths taken by optimization in parameter space can be leveraged to build a prior population, which when used to initialize QD methods in unseen environments, allows for few-shot adaptation. Our proposed method does not require backpropagation. It is simple to implement and scale, and furthermore, it is agnostic to the underlying models that are being trained. Experiments carried in both sparse and dense reward settings using robotic manipulation and navigation benchmarks show that it considerably reduces the number of generations that are required for QD optimization in these environments.Comment: Accepted for publication in the IEEE Robotics and Automation Letters (RA-L) journa

Genetski bazirana strategija re-planiranja modela crvotočine korištenjem neuronski naučenog vibracijskog ponašanja u robotskoj montaži

This paper investigates the genetic based re-planning search strategy, using neural learned vibration behavior for achieving tolerance compensation of uncertainties in robotic assembly. The vibration behavior was created from complex robot assembly of cogged tube over multistage planetary speed. Complex extensive experimental investigations were conducted for the purpose of finding the optimum vibration solution for each planetary stage reducer in order to complete the assembly process in defined real-time. However, tuning those parameters through experimental discovering for improved performance is a time consuming process. Neural network based learning was used to generate wider scope of parameters in order to improve the robot behavior during each state of the assembly process. As a novel modelling formalism of reactive hybrid automata, we propose the Wormhole Model with both learning and re-planning capacities (WOMOLERE). For our application, the states of hybrid automaton include amplitudes and frequencies of robot vibration module. The transition action is a function of minimal distance and uncertainty effects due to jamming during the assembly process. The results suggest that the methodology is adequate and could be recognized as an idea for designing of robot surgery assistance methods, especially in soft-robotics.Ovaj članak prezentira strategiju re-planiranja kretanja, baziranu na genetskom algoritmu, korištenjem neuronski naučenog vibracijskog ponašanja u cilju postizanja kompenzacije tolerancije neizvjesnosti u procesu robotske montaže. Vibracijsko ponašanje je kreirano iz kompleksne robotske montaže nazubljene cjevčice preko višestupanjskog planetarnog reduktora brzine motora. Provedeni su brojni eksperimenti s ciljem odre.ivanja optimalnih amplituda i frekvencija vibracijskog modula robota za svaki stupanj reduktora s ciljem završetka procesa montaže u definiranom realnom vremenu. Me.utim, podešavanje ovih parametara kroz eksperimente u cilju unaprje.enja perfomansi je vremenski zahtjevan proces. Učenje bazirano na neuronskim mrežama je korišteno za generiranje šireg opsega parametara stanja modela u cilju unaprje.enja robotskog ponašanja tijekom svake faze procesa montiranja. Kao novi formalizam modeliranja reaktivnog hibridnog automata, predložili smo model crvotočine, sa sposobnostima i učenja i re-planiranja (WOMOLERE). Za našu aplikaciju, stanja hibridnog automata sadrže amplitude i frekvencije robotskog vibracijskog modula. Akcija tranzicije je funkcija minimalne distance i učinaka neizvjesnosti uslijed zaglavljenja pri procesu montiranja. Rezultati pokazuju da je metodologija prikladna i kao ideja se može koristiti u dizajniranju metoda robotske asistencije pri operacijama, osobito u soft robotici