10,812 research outputs found
Comparative study of algorithms for the motion control of a robot arm
This paper describes and complements a poster presentation where different algorithms are compared from experimental results. Interest is focused on the robust behavior of simple algorithms used for the motion control of joints in robots.Peer ReviewedPostprint (published version
User evaluation of an interactive learning framework for single-arm and dual-arm robots
The final publication is available at link.springer.comSocial robots are expected to adapt to their users and, like their human counterparts, learn from the interaction. In our previous work, we proposed an interactive learning framework that enables a user to intervene and modify a segment of the robot arm trajectory. The framework uses gesture teleoperation and reinforcement learning to learn new motions. In the current work, we compared the user experience with the proposed framework implemented on the single-arm and dual-arm Barrett’s 7-DOF WAM robots equipped with a Microsoft Kinect camera for user tracking and gesture recognition. User performance and workload were measured in a series of trials with two groups of 6 participants using two robot settings in different order for counterbalancing. The experimental results showed that, for the same task, users required less time and produced shorter robot trajectories with the single-arm robot than with the dual-arm robot. The results also showed that the users who performed the task with the single-arm robot first experienced considerably less workload in performing the task with the dual-arm robot while achieving a higher task success rate in a shorter time.Peer ReviewedPostprint (author's final draft
Faster Motion on Cartesian Paths Exploiting Robot Redundancy at the Acceleration Level
The problem of minimizing the transfer time along a given Cartesian path for redundant robots can be approached in two steps, by separating the generation of a joint path associated to the Cartesian path from the exact minimization of motion time under kinematic/dynamic bounds along the obtained parameterized joint path. In this framework, multiple suboptimal solutions can be found, depending on how redundancy is locally resolved in the joint space within the first step. We propose a solution method that works at the acceleration level, by using weighted pseudoinversion, optimizing an inertia-related criterion, and including null-space damping. Several numerical results obtained on different robot systems demonstrate consistently good behaviors and definitely faster motion times in comparison with related methods proposed in the literature. The motion time obtained with our method is reasonably close to the global time-optimal solution along same Cartesian path. Experimental results on a KUKA LWR IV are also reported, showing the tracking control performance on the executed motions
Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
To enable safe and efficient human-robot collaboration in shared workspaces
it is important for the robot to predict how a human will move when performing
a task. While predicting human motion for tasks not known a priori is very
challenging, we argue that single-arm reaching motions for known tasks in
collaborative settings (which are especially relevant for manufacturing) are
indeed predictable. Two hypotheses underlie our approach for predicting such
motions: First, that the trajectory the human performs is optimal with respect
to an unknown cost function, and second, that human adaptation to their
partner's motion can be captured well through iterative re-planning with the
above cost function. The key to our approach is thus to learn a cost function
which "explains" the motion of the human. To do this, we gather example
trajectories from pairs of participants performing a collaborative assembly
task using motion capture. We then use Inverse Optimal Control to learn a cost
function from these trajectories. Finally, we predict reaching motions from the
human's current configuration to a task-space goal region by iteratively
re-planning a trajectory using the learned cost function. Our planning
algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF
human kinematic model and accounts for the presence of a moving collaborator
and obstacles in the environment. Our results suggest that in most cases, our
method outperforms baseline methods when predicting motions. We also show that
our method outperforms baselines for predicting human motion when a human and a
robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201
Adaptive Parallel Iterative Deepening Search
Many of the artificial intelligence techniques developed to date rely on
heuristic search through large spaces. Unfortunately, the size of these spaces
and the corresponding computational effort reduce the applicability of
otherwise novel and effective algorithms. A number of parallel and distributed
approaches to search have considerably improved the performance of the search
process. Our goal is to develop an architecture that automatically selects
parallel search strategies for optimal performance on a variety of search
problems. In this paper we describe one such architecture realized in the
Eureka system, which combines the benefits of many different approaches to
parallel heuristic search. Through empirical and theoretical analyses we
observe that features of the problem space directly affect the choice of
optimal parallel search strategy. We then employ machine learning techniques to
select the optimal parallel search strategy for a given problem space. When a
new search task is input to the system, Eureka uses features describing the
search space and the chosen architecture to automatically select the
appropriate search strategy. Eureka has been tested on a MIMD parallel
processor, a distributed network of workstations, and a single workstation
using multithreading. Results generated from fifteen puzzle problems, robot arm
motion problems, artificial search spaces, and planning problems indicate that
Eureka outperforms any of the tested strategies used exclusively for all
problem instances and is able to greatly reduce the search time for these
applications
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
The Anthropomorphic Hand Assessment Protocol (AHAP)
The progress in the development of anthropomorphic hands for robotic and prosthetic applications has not been followed by a parallel development of objective methods to evaluate their performance. The need for benchmarking in grasping research has been recognized by the robotics community as an important topic. In this study we present the Anthropomorphic Hand Assessment Protocol (AHAP) to address this need by providing a measure for quantifying the grasping ability of artificial hands and comparing hand designs. To this end, the AHAP uses 25 objects from the publicly available Yale-CMU-Berkeley Object and Model Set thereby enabling replicability. It is composed of 26 postures/tasks involving grasping with the eight most relevant human grasp types and two non-grasping postures. The AHAP allows to quantify the anthropomorphism and functionality of artificial hands through a numerical Grasping Ability Score (GAS). The AHAP was tested with different hands, the first version of the hand of the humanoid robot ARMAR-6 with three different configurations resulting from attachment of pads to fingertips and palm as well as the two versions of the KIT Prosthetic Hand. The benchmark was used to demonstrate the improvements of these hands in aspects like the grasping surface, the grasp force and the finger kinematics. The reliability, consistency and responsiveness of the benchmark have been statistically analyzed, indicating that the AHAP is a powerful tool for evaluating and comparing different artificial hand designs
RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning
Motivated by what is required for real-time path planning, the paper starts
out by presenting sRMPD, a new recursive "local" planner founded on the key
notion that, unless made necessary by an obstacle, there must be no deviation
from the shortest path between any two points, which would normally be a
straight line path in the configuration space. Subsequently, we increase the
power of sRMPD by using it as a "connect" subroutine call in a higher-level
sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence,
mRMPD spawns a larger number of space exploring trees in regions of the
configuration space that are characterized by a higher density of obstacles.
The overall effect is a hybrid tree growing strategy with a trade-off between
random exploration as made possible by multi-RRT based logic and immediate
exploitation of opportunities to connect two states as made possible by sRMPD.
The mRMPD planner can be biased with regard to this trade-off for solving
different kinds of planning problems efficiently. Based on the test cases we
have run, our experiments show that mRMPD can reduce planning time by up to 80%
compared to basic RRT
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