First-order synergies for motion planning of anthropomorphic dual-arm robots

This paper addresses the problem of designing a planning algorithm for anthropomorphic dual-arm robotic systems to find paths that mimics the movements of real human beings by using first-order synergies (correlations between joint velocities). The key idea of the proposal is to convert captured human movements into a vector field of velocities, defined in the configuration space of the robot, and use it to guide the search of a solution path. The motion planning is solved using the proposed algorithm, called FOS-BKPIECE, that is a bidirectional version of the KPIECE planner working with an improved version of the extension procedure of the VF-RRT planner. The obtained robot movements follow the directions of the defined vector field and hence allow the robot to solve the task in a human-like fashion. The paper presents a description of the proposed approach as well as results from conceptual and application examples, the latter using a real anthropomorphic dual-arm robotic system. A thorough comparison with other previous planning algorithms shows that the proposed approach obtains better results.Postprint (published version

Human Operator Tracking System for Safe Industrial Collaborative Robotics

With the advent of the Industry 4.0 paradigm, manufacturing is shifting from mass production towards customisable production lines. While robots excel at reliably executing repeating tasks in a fast and precise manner, they lack the now desired versatility of humans. Human-robot collaboration (HRC) seeks to address this issue by allowing human operators to work together with robots in close proximity, leveraging the strengths of both agents to increase adaptability and productivity. Safety is critical to user acceptance and the success of collaborative robots (cobots) and is thus a focus of research. Typical approaches provide the cobot with information such as operator pose estimates or higher-level motion predictions to facilitate adaptive planning of trajectory or action. Therefore, locating the operator in the shared workspace is a key feature. This dissertation seeks to kickstart the development of a human operator tracking system that provides a three-dimensional pose estimate and, in turn, ensures safety. State-of-the-art methods for human pose estimation in two-dimensional RGB images are tested with a custom dataset and evaluated. The results are then analysed considering real-time capability in the use case of a single operator performing industrial assembly tasks in a collaborative robotic cell equipped with a robotic arm. The resulting observations enable future work like fusion of depth information.With the advent of the Industry 4.0 paradigm, manufacturing is shifting from mass production towards customisable production lines. While robots excel at reliably executing repeating tasks in a fast and precise manner, they lack the now desired versatility of humans. Human-robot collaboration (HRC) seeks to address this issue by allowing human operators to work together with robots in close proximity, leveraging the strengths of both agents to increase adaptability and productivity. Safety is critical to user acceptance and the success of collaborative robots (cobots) and is thus a focus of research. Typical approaches provide the cobot with information such as operator pose estimates or higher-level motion predictions to facilitate adaptive planning of trajectory or action. Therefore, locating the operator in the shared workspace is a key feature. This dissertation seeks to kickstart the development of a human operator tracking system that provides a three-dimensional pose estimate and, in turn, ensures safety. State-of-the-art methods for human pose estimation in two-dimensional RGB images are tested with a custom dataset and evaluated. The results are then analysed considering real-time capability in the use case of a single operator performing industrial assembly tasks in a collaborative robotic cell equipped with a robotic arm. The resulting observations enable future work like fusion of depth information

Planning hand-arm grasping motions with human-like appearance

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFinalista de l’IROS Best Application Paper Award a la 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, ICROS.This paper addresses the problem of obtaining human-like motions on hand-arm robotic systems performing pick-and-place actions. The focus is set on the coordinated movements of the robotic arm and the anthropomorphic mechanical hand, with which the arm is equipped. For this, human movements performing different grasps are captured and mapped to the robot in order to compute the human hand synergies. These synergies are used to reduce the complexity of the planning phase by reducing the dimension of the search space. In addition, the paper proposes a sampling-based planner, which guides the motion planning ollowing the synergies. The introduced approach is tested in an application example and thoroughly compared with other state-of-the-art planning algorithms, obtaining better results.Peer ReviewedAward-winningPostprint (author's final draft

Analysis and Observations from the First Amazon Picking Challenge

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge

Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study

Developing robot agnostic software frameworks involves synthesizing the disparate fields of robotic theory and software engineering while simultaneously accounting for a large variability in hardware designs and control paradigms. As the capabilities of robotic software frameworks increase, the setup difficulty and learning curve for new users also increase. If the entry barriers for configuring and using the software on robots is too high, even the most powerful of frameworks are useless. A growing need exists in robotic software engineering to aid users in getting started with, and customizing, the software framework as necessary for particular robotic applications. In this paper a case study is presented for the best practices found for lowering the barrier of entry in the MoveIt! framework, an open-source tool for mobile manipulation in ROS, that allows users to 1) quickly get basic motion planning functionality with minimal initial setup, 2) automate its configuration and optimization, and 3) easily customize its components. A graphical interface that assists the user in configuring MoveIt! is the cornerstone of our approach, coupled with the use of an existing standardized robot model for input, automatically generated robot-specific configuration files, and a plugin-based architecture for extensibility. These best practices are summarized into a set of barrier to entry design principles applicable to other robotic software. The approaches for lowering the entry barrier are evaluated by usage statistics, a user survey, and compared against our design objectives for their effectiveness to users

Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms

Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-