Organizational Unit:

Humanoid Robotics Laboratory

Permanent Link

https://hdl.handle.net/1853/70718

Parent Organization

Organizational Unit

Institute for Robotics and Intelligent Machines (IRIM)

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Publication Search Results

Now showing 1 - 10 of 11

Push Planning for Object Placement in Clutter Using the PR-2

(Georgia Institute of Technology, 2011-09) Emeli, Victor ; Kemp, Charles C. ; Stilman, Mike

The goal of this project is to investigate the implementation of a planning algorithm for the problem of placing objects on a cluttered surface with a PR-2 mobile manipulator. The original push planning algorithm [1] was initially developed as a simulation. We modified the simulator for execution in real-world cluttered environments. This paper discusses the challenges of implementation and presents empirical results that determine how well the simulator models the real world as clutter is pushed and collides with other objects.
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.
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*.
Make Your Robot Talk Correctly: Deriving Models of Hybrid System

(Georgia Institute of Technology, 2011-07) Dantam, Neil ; Stilman, Mike ; Egerstedt, Magnus B.

Using both formal language and differential equations to model a robotic system, we introduce a calculus of transformation rules for the symbolic derivation of hybrid controllers. With a Context-Free Motion Grammar, we show how to test reachability between different regions of state-space and give several symbolic transformations to modify the set of event strings the system may generate. This approach lets one modify the language of the hybrid system, providing a way to change system behavior so that it satisfies linguistic constraints on correct operation.
The Motion Grammar: Linguistic Perception, Planning, and Control

(Georgia Institute of Technology, 2011-06) Dantam, Neil ; Stilman, Mike

We present and analyze the Motion Grammar: a novel unified representation for task decomposition, perception, planning, and control that provides both fast online control of robots in uncertain environments and the ability to guarantee completeness and correctness. The grammar represents a policy for the task which is parsed in real-time based on perceptual input. Branches of the syntax tree form the levels of a hierarchical decomposition, and the individual robot sensor readings are given by tokens. We implement this approach in the interactive game of Yamakuzushi on a physical robot resulting in a system that repeatably competes with a human opponent in sustained gameplay for the roughly six minute duration of each match.
Dynamic Chess: Strategic Planning for Robot Motion

(Georgia Institute of Technology, 2011-05) Kunz, Tobias ; Kingston, Peter ; Stilman, Mike ; Egerstedt, Magnus B.

We introduce and experimentally validate a novel algorithmic model for physical human-robot interaction with hybrid dynamics. Our computational solutions are complementary to passive and compliant hardware. We focus on the case where human motion can be predicted. In these cases, the robot can select optimal motions in response to human actions and maximize safety. By representing the domain as a Markov Game, we enable the robot to not only react to the human but also to construct an infinite horizon optimal policy of actions and responses. Experimentally, we apply our model to simulated robot sword defense. Our approach enables a simulated 7-DOF robot arm to block known attacks in any sequence. We generate optimized blocks and apply game theoretic tools to choose the best action for the defender in the presence of an intelligent adversary.
Ach: IPC for Real-Time Robot Control

(Georgia Institute of Technology, 2011) Dantam, Neil ; Stilman, Mike

We present a new Inter-Process Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating perception, control drivers, and algorithms in real-time systems that sample data from physical processes. 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. Finally, the source code for Ach is available under an Open Source BSD-style license.
Time-Optimal Path Following with Bounded Joint Accelerations and Velocities

(Georgia Institute of Technology, 2011) Kunz, Tobias ; Stilman, Mike

This paper presents a method to generate the time-optimal trajectroy that exactly follows a given differentiable joint-space path within given bounds on joint accelerations and velocities. We also present a path preprocessing method to make nondifferentiable paths differentiable by adding circular blends.
Design and Development of a Dynamically-Balancing Holonomic Robot

(Georgia Institute of Technology, 2011) Reynolds-Haertle, Saul ; Stilman, Mike

This paper describes the design, control, and construction of Golem Wing, the first vehicle which both balances dynamically and has entirely holonomic ground movement. A nonstandard linear arrangement of mecanum wheels gives it the load-lifting, performance, and manipulation benefits of a dynamically-balancing platform without the maneuvering difficulties exhibited by previous balancing platforms. We show that the arrangement is capable of holonomic motion, describe a controller that maintains dynamic balance during holonomic motion, and show an implementation of the system in hardware that validate our assertions.
Turning Paths Into Trajectories Using Parabolic Blends

(Georgia Institute of Technology, 2011) Kunz, Tobias ; Stilman, Mike

We present an approach for converting a path of multiple continuous linear segments into a trajectory that satisfies velocity and acceleration constraints and closely follows the given path without coming to a complete stop at every waypoint. Our method applies parabolic blends around waypoints to improve speed. In contrast to established methods that smooth trajectories with parabolic blends, our method does not require the timing of waypoints or durations of blend phases. This makes our approach particularly useful for robots that must follow kinematic paths that are not explicitly parametrized by time. Our method chooses timing automatically to achieve high performance while satisfying the velocity and acceleration constraints of a given robot.