Motion planning using synergies : application to anthropomorphic dual-arm robots

Motion planning is a traditional field in robotics, but new problems are nevertheless incessantly appearing, due to continuous advances in the robot developments. In order to solve these new problems, as well as to improve the existing solutions to classical problems, new approaches are being proposed. A paradigmatic case is the humanoid robotics, since the advances done in this field require motion planners not only to look efficiently for an optimal solution in the classic way, i.e. optimizing consumed energy or time in the plan execution, but also looking for human-like solutions, i.e. requiring the robot movements to be similar to those of the human beings. This anthropomorphism in the robot motion is desired not only for aesthetical reasons, but it is also needed to allow a better and safer human-robot collaboration: humans can predict more easily anthropomorphic robot motions thus avoiding collisions and enhancing the collaboration with the robot. Nevertheless, obtaining a satisfactory performance of these anthropomorphic robotic systems requires the automatic planning of the movements, which is still an arduous and non-evident task since the complexity of the planning problem increases exponentially with the number of degrees of freedom of the robotic system. This doctoral thesis tackles the problem of planning the motions of dual-arm anthropomorphic robots (optionally with mobile base). The main objective is twofold: obtaining robot motions both in an efficient and in a human-like fashion at the same time. Trying to mimic the human movements while reducing the complexity of the search space for planning purposes leads to the concept of synergies, which could be conceptually defined as correlations (in the joint configuration space as well as in the joint velocity space) between the degrees of freedom of the system. This work proposes new sampling-based motion-planning procedures that exploit the concept of synergies, both in the configuration and velocity space, coordinating the movements of the arms, the hands and the mobile base of mobile anthropomorphic dual-arm robots.La planificación de movimientos es un campo tradicional de la robótica, sin embargo aparecen incesantemente nuevos problemas debido a los continuos avances en el desarrollo de los robots. Para resolver esos nuevos problemas, así como para mejorar las soluciones existentes a los problemas clásicos, se están proponiendo nuevos enfoques. Un caso paradigmático es la robótica humanoide, ya que los avances realizados en este campo requieren que los algoritmos planificadores de movimientos no sólo encuentren eficientemente una solución óptima en el sentido clásico, es decir, optimizar el consumo de energía o el tiempo de ejecución de la trayectoria; sino que también busquen soluciones con apariencia humana, es decir, que el movimiento del robot sea similar al del ser humano. Este antropomorfismo en el movimiento del robot se busca no sólo por razones estéticas, sino porque también es necesario para permitir una colaboración mejor y más segura entre el robot y el operario: el ser humano puede predecir con mayor facilidad los movimientos del robot si éstos son antropomórficos, evitando así las colisiones y mejorando la colaboración humano robot. Sin embargo, para obtener un desempeño satisfactorio de estos sistemas robóticos antropomórficos se requiere una planificación automática de sus movimientos, lo que sigue siendo una tarea ardua y poco evidente, ya que la complejidad del problema aumenta exponencialmente con el número de grados de libertad del sistema robótico. Esta tesis doctoral aborda el problema de la planificación de movimientos en robots antropomorfos bibrazo (opcionalmente con base móvil). El objetivo aquí es doble: obtener movimientos robóticos de forma eficiente y, a la vez, que tengan apariencia humana. Intentar imitar los movimientos humanos mientras a la vez se reduce la complejidad del espacio de búsqueda conduce al concepto de sinergias, que podrían definirse conceptualmente como correlaciones (tanto en el espacio de configuraciones como en el espacio de velocidades de las articulaciones) entre los distintos grados de libertad del sistema. Este trabajo propone nuevos procedimientos de planificación de movimientos que explotan el concepto de sinergias, tanto en el espacio de configuraciones como en el espacio de velocidades, coordinando así los movimientos de los brazos, las manos y la base móvil de robots móviles, bibrazo y antropomórficos.Postprint (published version

Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

Motion Planning for Manipulation With Heuristic Search

Heuristic searches such as A* search are a popular means of finding least-cost plans due to their generality, strong theoretical guarantees on completeness and optimality, simplicity in implementation, and consistent behavior. In planning for robotic manipulation, however, these techniques are commonly thought of as impractical due to the high-dimensionality of the planning problem. As part of this thesis work, we have developed a heuristic search-based approach to motion planning for manipulation that does deal effectively with the high-dimensionality of the problem. In this thesis, I will present the approach together with its theoretical properties and show how to apply it to single-arm and dual-arm motion planning with upright constraints on a PR2 robot operating in non-trivial cluttered spaces. Then I will explain how we extended our approach to manipulation planning for n-arms with regrasping. In this work, the planner itself makes all of the discrete decisions, including which arm to use for the pickup and putdown, whether handoffs are necessary and how the object should be grasped at each step along the way. An extensive experimental analysis in both simulation and on a physical PR2 shows that, in terms of runtime, our approach is on par with some of the most common sampling-based approaches. This includes benchmarking our planning framework on two domains that we constructed that are common to manufacturing: pick-and-place of fast moving objects and the autonomous assembly of small objects. Between these applications, the planner exhibited fast planning times and the ability to robustly plan paths into and out of tight working environments that are common to assembly. The closing work of this thesis includes an exhaustive study of the natural tradeoff that occurs between planning efficiency versus solution quality for different values of the heuristic inflation factor. A comparison of the solution quality of our planner to paths computed by an asymptotically optimal approach given a great deal of time for path optimization is included as well. Finally, a set of experimental results are included that show that due to our approach\u27s deterministic cost-minimization, similar input tends to lead to similarity in the output. This kind of local consistency is important to the predictability of the robot\u27s motions and contributes to human-robot safety

Model-based autonomous system for performing dexterous, human-level manipulation tasks

This article presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation Software (ARM-S) program. Performing human-level manipulation tasks is achieved through a novel combination of perception in uncertain environments, precise tool use, forceful dual-arm planning and control, persistent environmental tracking, and task level verification. Deliberate interaction with the environment is incorporated into planning and control strategies, which, when coupled with world estimation, allows for refinement of models and precise manipulation. The system takes advantage of sensory feedback immediately with little open-loop execution, attempting true autonomous reasoning and multi-step sequencing that adapts in the face of changing and uncertain environments. A tire change scenario utilizing human tools, discussed throughout the article, is used to described the system approach. A second scenario of cutting a wire is also presented, and is used to illustrate system component reuse and generality.United States. Defense Advanced Research Projects Agency. Autonomous Robotic Manipulation Progra

A Hierarchical Architecture for Flexible Human-Robot Collaboration

This thesis is devoted to design a software architecture for Human- Robot Collaboration (HRC), to enhance the robots\u2019 abilities for working alongside humans. We propose FlexHRC, a hierarchical and flexible human-robot cooperation architecture specifically designed to provide collaborative robots with an extended degree of autonomy when supporting human operators in tasks with high-variability. Along with FlexHRC, we have introduced novel techniques appropriate for three interleaved levels, namely perception, representation, and action, each one aimed at addressing specific traits of humanrobot cooperation tasks. The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative robots could bring to the whole production process. In this context, a yet unreached enabling technology is the design of robots able to deal at all levels with humans\u2019 intrinsic variability, which is not only a necessary element to a comfortable working experience for humans but also a precious capability for efficiently dealing with unexpected events. Moreover, a flexible assembly of semi-finished products is one of the expected features of next-generation shop-floor lines. Currently, such flexibility is placed on the shoulders of human operators, who are responsible for product variability, and therefore they are subject to potentially high stress levels and cognitive load when dealing with complex operations. At the same time, operations in the shop-floor are still very structured and well-defined. Collaborative robots have been designed to allow for a transition of such burden from human operators to robots that are flexible enough to support them in high-variability tasks while they unfold. As mentioned before, FlexHRC architecture encompasses three perception, action, and representation levels. The perception level relies on wearable sensors for human action recognition and point cloud data for perceiving the object in the scene. The action level embraces four components, the robot execution manager for decoupling action planning from robot motion planning and mapping the symbolic actions to the robot controller command interface, a task Priority framework to control the robot, a differential equation solver to simulate and evaluate the robot behaviour on-the-fly, and finally a random-based method for the robot path planning. The representation level depends on AND/OR graphs for the representation of and the reasoning upon human-robot cooperation models online, a task manager to plan, adapt, and make decision for the robot behaviors, and a knowledge base in order to store the cooperation and workspace information. We evaluated the FlexHRC functionalities according to the application desired objectives. This evaluation is accompanied with several experiments, namely collaborative screwing task, coordinated transportation of the objects in cluttered environment, collaborative table assembly task, and object positioning tasks. The main contributions of this work are: (i) design and implementation of FlexHRC which enables the functional requirements necessary for the shop-floor assembly application such as task and team level flexibility, scalability, adaptability, and safety just a few to name, (ii) development of the task representation, which integrates a hierarchical AND/OR graph whose online behaviour is formally specified using First Order Logic, (iii) an in-the-loop simulation-based decision making process for the operations of collaborative robots coping with the variability of human operator actions, (iv) the robot adaptation to the human on-the-fly decisions and actions via human action recognition, and (v) the predictable robot behavior to the human user thanks to the task priority based control frame, the introduced path planner, and the natural and intuitive communication of the robot with the human

Tool Exchangeable Grasp/Assembly Planner

This paper proposes a novel assembly planner for a manipulator which can simultaneously plan assembly sequence, robot motion, grasping configuration, and exchange of grippers. Our assembly planner assumes multiple grippers and can automatically selects a feasible one to assemble a part. For a given AND/OR graph of an assembly task, we consider generating the assembly graph from which assembly motion of a robot can be planned. The edges of the assembly graph are composed of three kinds of paths, i.e., transfer/assembly paths, transit paths and tool exchange paths. In this paper, we first explain the proposed method for planning assembly motion sequence including the function of gripper exchange. Finally, the effectiveness of the proposed method is confirmed through some numerical examples and a physical experiment.Comment: This is to appear Int. Conf. on Intelligent Autonomous System