4,042 research outputs found

    Research and development at ORNL/CESAR towards cooperating robotic systems for hazardous environments

    Get PDF
    One of the frontiers in intelligent machine research is the understanding of how constructive cooperation among multiple autonomous agents can be effected. The effort at the Center for Engineering Systems Advanced Research (CESAR) at the Oak Ridge National Laboratory (ORNL) focuses on two problem areas: (1) cooperation by multiple mobile robots in dynamic, incompletely known environments; and (2) cooperating robotic manipulators. Particular emphasis is placed on experimental evaluation of research and developments using the CESAR robot system testbeds, including three mobile robots, and a seven-axis, kinematically redundant mobile manipulator. This paper summarizes initial results of research addressing the decoupling of position and force control for two manipulators holding a common object, and the path planning for multiple robots in a common workspace

    Achieving Corresponding Effects on Multiple Robotic Platforms: Imitating in Context Using Different Effect Metrics

    Get PDF
    Original paper can be found at: www.aisb.org.uk/publications/proceedings/aisb05/3_Imitation_Final.pdfOne of the fundamental problems in imitation is the correspondence problem, how to map between the actions, states and effects of the model and imitator agents, when the embodiment of the agents is dissimilar. In our approach, the matching is according to different metrics and granularity. This paper presents JABBERWOCKY, a system that uses captured data from a human demonstrator to generate appropriate action commands, addressing the correspondence problem in imitation. Towards a characterization of the space of effect metrics, we are exploring absolute/relative angle and displacement aspects and focus on the overall arrangement and trajectory of manipulated objects. Using as an example a captured demonstration from a human, the system produces a correspondence solution given a selection of effect metrics and starting from dissimilar initial object positions, producing action commands that are then executed by two imitator target platforms (in simulation) to successfully imitate

    Solving the potential field local minimum problem using internal agent states

    Get PDF
    We propose a new, extended artificial potential field method, which uses dynamic internal agent states. The internal states are modelled as a dynamical system of coupled first order differential equations that manipulate the potential field in which the agent is situated. The internal state dynamics are forced by the interaction of the agent with the external environment. Local equilibria in the potential field are then manipulated by the internal states and transformed from stable equilibria to unstable equilibria, allowiong escape from local minima in the potential field. This new methodology successfully solves reactive path planning problems, such as a complex maze with multiple local minima, which cannot be solved using conventional static potential fields

    Lower body design of the ‘iCub’ a human-baby like crawling robot

    Get PDF
    The development of robotic cognition and a greater understanding of human cognition form two of the current greatest challenges of science. Within the RobotCub project the goal is the development of an embodied robotic child (iCub) with the physical and ultimately cognitive abilities of a 2frac12 year old human baby. The ultimate goal of this project is to provide the cognition research community with an open human like platform for understanding of cognitive systems through the study of cognitive development. In this paper the design of the mechanisms adopted for lower body and particularly for the leg and the waist are outlined. This is accompanied by discussion on the actuator group realisation in order to meet the torque requirements while achieving the dimensional and weight specifications. Estimated performance measures of the iCub are presented

    Design and motion control of a 6-UPS fully parallel robot for long bone fracture reduction : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University

    Get PDF
    The incidences of long bone fractures in New Zealand are approximately 1 in 10,000. Long bones such as tibia and femur have complicated anatomic structures, making the realignment of these long bone fractures reliant on the skill of the surgeon. The drawbacks of current practice result in long time exposure to radiation, slow recovery and possible morbidity. A semi-automated long bone fracture reduction system based on a 6-DOF parallel robot platform has been in development since 2004. The developed 6-DOF parallel robot platform comprises of six linear actuators with rotary incremental encoders. To implement a realignment of long bone fractures, a framework for the 6-DOF platform robot has been developed. The inverse kinematics and singularity of the 6-DOF parallel robot has been studied to obtain the actions and Jacobin matrices. In motion control a multiple axis motion controller and amplifiers were used for 6-DOF parallel robot. PID tuning algorithms were developed based on the combination of the general tuning result and the contour control principle. The PID parameters have been validated by a number of experiments. The practical realignment of bone fractures requires a "Pull-Rotate-Push" action implemented by the 6-DOF parallel robot. After calibration, the reduction trajectories were generated accurately. The actual trials on the artificial fractures have shown that the robot developed is capable of performing the required reduction motion

    Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework

    Get PDF
    From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot’s body and its peripersonal space. The internal models (of each robot’s body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms
    • 

    corecore