Organizational Unit:
Humanoid Robotics Laboratory

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End date: 2012 × Start date: 2011 × search.filters.applied.f.resourcesubtype: Post-print ×

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Now showing 1 - 8 of 8
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    Robust and Efficient Communication for Real-Time Multi-Process Robot Software
    (Georgia Institute of Technology, 2012-11) Dantam, Neil ; Stilman, Mike
    We present a new Interprocess Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating drivers, controllers, and algorithms in complex robotic systems such as humanoid robots. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems, and we discuss the implementation on our humanoid robot Golem Krang. Finally, the source code for Ach is available under an Open Source permissive license.
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    Deterministic Motion Planning for Redundant Robots along End-Effector Paths
    (Georgia Institute of Technology, 2012-11) Quispe, Ana Huamán ; Stilman, Mike
    In this paper we propose a deterministic approach to solve the Motion Planning along End-Effector Paths problem (MPEP) for redundant manipulators. Most of the existing approaches are based on local optimization techniques, hence they do not offer global guarantees of finding a path if it exists. Our proposed method is resolution complete. This feature is achieved by discretizing the Jacobian nullspace at each waypoint and selecting the next configuration according to a given heuristic function. To escape from possible local minima, our algorithm implements a backtracking strategy that allows our planner to recover from erroneous previous configuration choices by performing a breadth-first backwards search procedure. We present the results of simulated experiments performed with diverse manipulators and a humanoid robot.
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    Linguistic Transfer of Human Assembly Tasks to Robots
    (Georgia Institute of Technology, 2012-10) Dantam, Neil ; Essa, Irfan ; Stilman, Mike
    We demonstrate the automatic transfer of an assembly task from human to robot. This work extends efforts showing the utility of linguistic models in verifiable robot control policies by now performing real visual analysis of human demonstrations to automatically extract a policy for the task. This method tokenizes each human demonstration into a sequence of object connection symbols, then transforms the set of sequences from all demonstrations into an automaton, which represents the task-language for assembling a desired object. Finally, we combine this assembly automaton with a kinematic model of a robot arm to reproduce the demonstrated task.
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    Multi-Robot Multi-Object Rearrangement in Assignment Space
    (Georgia Institute of Technology, 2012-10) Levihn, Martin ; Igarashi, Takeo ; Stilman, Mike
    We present Assignment Space Planning, a new efficient robot multi-agent coordination algorithm for the PSPACE- hard problem of multi-robot multi-object push rearrangement. In both simulated and real robot experiments, we demonstrate that our method produces optimal solutions for simple problems and exhibits novel emergent behaviors for complex scenarios. Assignment Space takes advantage of the domain structure by splitting the planning up into three stages, effectively reducing the search space size and enabling the planner to produce optimized plans in seconds. Our algorithm finds solutions of comparable quality to complete configuration space search while reducing the computing time to seconds, which allows our approach to be applied in practical scenarios in real-time.
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    Manipulation Planning with Soft Task Constraints
    (Georgia Institute of Technology, 2012-10) Kunz, Tobias ; Stilman, Mike
    We present a randomized configuration space planner that enforces soft workspace task constraints. A soft task constraint allows an interval of feasible values while favoring a given exact value. Previous work only allows for enforcing an exact value or an interval without a specific preference. Soft task constraints are a useful concept in everyday life. For example when carrying a container of liquid we want to keep it as close to the upright position as possible but want to be able to tilt it slightly in order to avoid obstacles. This paper introduces the necessary algorithms for handling such constraints, including projection methods and useful representations of everyday constraints. Our algorithms are evaluated on a series of simulated benchmark problems and shown to yield significant improvement in constraint satisfaction.
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    Linguistic Composition of Semantic Maps and Hybrid Controllers
    (Georgia Institute of Technology, 2012-06) Dantam, Neil ; Nieto-Granda, Carlos ; Christensen, Henrik I. ; Stilman, Mike
    This work combines semantic maps with hybrid control models, generating a direct link between action and environment models to produce a control policy for mobile manipulation in unstructured environments. First, we generate a semantic map for our environment and design a base model of robot action. Then, we combine this map and action model using the Motion Grammar Calculus to produce a combined robot-environment model. Using this combined model, we apply supervisory control to produce a policy for the manipulation task. We demonstrate this approach on a Segway RMP-200 mobile platform.
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    Push Planning for Object Placement on Cluttered Table Surfaces
    (Georgia Institute of Technology, 2011-09) Cosgun, Akansel ; Hermans, Tucker ; Emeli, Victor ; Stilman, Mike
    We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence of manipulation actions that create space for the object. When no continuous space is large enough for direct placement, the planner leverages means-end analysis and dynamic simulation to find a sequence of linear pushes that clears the necessary space. Our heuristic for determining candidate placement poses for the target object is used to guide the manipulation search. We show successful results for our algorithm in simulation.
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    Sampling Heuristics for Optimal Motion Planning in High Dimensions
    (Georgia Institute of Technology, 2011-09) Akgun, Baris ; Stilman, Mike
    We present a sampling-based motion planner that improves the performance of the probabilistically optimal RRT* planning algorithm. Experiments demonstrate that our planner finds a fast initial path and decreases the cost of this path iteratively. We identify and address the limitations of RRT* in high-dimensional configuration spaces. We introduce a sampling bias to facilitate and accelerate cost decrease in these spaces and a simple node-rejection criteria to increase efficiency. Finally, we incorporate an existing bi-directional approach to search which decreases the time to find an initial path. We analyze our planner on a simple 2D navigation problem in detail to show its properties and test it on a difficult 7D manipulation problem to show its effectiveness. Our results consistently demonstrate improved performance over RRT*.