3,790 research outputs found
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
Modular Relative Jacobian for Dual-Arms and the Wrench Transformation Matrix
A modular relative Jacobian is recently derived and is expressed in terms of the individual Jacobians of stand-alone manipulators. It includes a wrench transformation matrix, which was not shown in earlier expressions. This paper is an experimental extension of that recent work, which showed that at higher angular end-effector velocities the contribution of the wrench transformation matrix cannot be ignored. In this work, we investigate the dual-arm force control performance, without necessarily driving the end-effectors at higher angular velocities. We compare experimental results for two cases: modular relative Jacobian with and without the wrench transformation matrix. The experimental setup is a dual-arm system consisting of two KUKA LWR robots. Two experimental tasks are used: relative end-effector motion and coordinated independent tasks, where a force controller is implemented in both tasks. Furthermore, we show in an experimental design that the use of a relative Jacobian affords less accurate task specifications for a highly complicated task requirement for both end-effectors of the dual-arm. Experimental results on the force control performance are compared and analyzed
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
Planar Manipulation via Learning Regrasping
Regrasping is important for robots to reorient objects in planar manipulation
tasks. Different placements of objects can provide robots with alternative
grasp configurations, which are used in complex planar manipulation tasks that
require multiple pick-rotate-and-place steps due to the constraints of the
environment and robot kinematics. In this work, our goal is to generate diverse
placements of objects on the plane using deep neural networks. We propose a
pipeline with the stages of orientation generation, position refinement, and
placement discrimination to obtain accurate and diverse stable placements based
on the perception of point clouds. A large-scale dataset is created for
training, including simulated placements and contact information between
objects and the plane. The simulation results show that our pipeline
outperforms the start-of-the-art, achieving an accuracy rate of 90.4% and a
diversity rate of 81.3% in simulation on generated placements. Our pipeline is
also validated in real-robot experiments. With the generated placements,
sequential pick-rotate-and-place steps are calculated for the robot to reorient
objects to goal poses that are not reachable within one step. Videos and
dataset are available at https://sites.google.com/view/pmvlr2022/
Motion Primitives and Planning for Robots with Closed Chain Systems and Changing Topologies
When operating in human environments, a robot should use predictable motions that allow humans to trust and anticipate its behavior. Heuristic search-based planning offers predictable motions and guarantees on completeness and sub-optimality of solutions. While search-based planning on motion primitive-based (lattice-based) graphs has been used extensively in navigation, application to high-dimensional state-spaces has, until recently, been thought impractical. This dissertation presents methods we have developed for applying these graphs to mobile manipulation, specifically for systems which contain closed chains. The formation of closed chains in tasks that involve contacts with the environment may reduce the number of available degrees-of-freedom but adds complexity in terms of constraints in the high-dimensional state-space. We exploit the dimensionality reduction inherent in closed kinematic chains to get efficient search-based planning.
Our planner handles changing topologies (switching between open and closed-chains) in a single plan, including what transitions to include and when to include them. Thus, we can leverage existing results for search-based planning for open chains, combining open and closed chain manipulation planning into one framework. Proofs regarding the framework are introduced for the application to graph-search and its theoretical guarantees of optimality. The dimensionality-reduction is done in a manner that enables finding optimal solutions to low-dimensional problems which map to correspondingly optimal full-dimensional solutions. We apply this framework to planning for opening and navigating through non-spring and spring-loaded doors using a Willow Garage PR2. The framework motivates our approaches to the Atlas humanoid robot from Boston Dynamics for both stationary manipulation and quasi-static walking, as a closed chain is formed when both feet are on the ground
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