Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

Permanent Link

https://hdl.handle.net/1853/70657

Includes Organization(s)

Organizational Unit

Biorobotics and Human Modeling Lab

Organizational Unit

Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 10 of 18

Information-based Reduced Landmark SLAM

(Georgia Institute of Technology, 2015-05) Choudhary, Siddharth ; Indelman, Vadim ; Christensen, Henrik I. ; Dellaert, Frank

In this paper, we present an information-based approach to select a reduced number of landmarks and poses for a robot to localize itself and simultaneously build an accurate map. We develop an information theoretic algorithm to efficiently reduce the number of landmarks and poses in a SLAM estimate without compromising the accuracy of the estimated trajectory. We also propose an incremental version of the reduction algorithm which can be used in SLAM framework resulting in information based reduced landmark SLAM. The results of reduced landmark based SLAM algorithm are shown on Victoria park dataset and a Synthetic dataset and are compared with standard graph SLAM (SAM [6]) algorithm. We demonstrate a reduction of 40-50% in the number of landmarks and around 55% in the number of poses with minimal estimation error as compared to standard SLAM algorithm.
Trust Modeling in Multi-Robot Patrolling

(Georgia Institute of Technology, 2014-06) Pippin, Charles ; Christensen, Henrik I.

On typical multi-robot teams, there is an implicit assumption that robots can be trusted to effectively perform assigned tasks. The multi-robot patrolling task is an example of a domain that is particularly sensitive to reliability and performance of robots. Yet reliable performance of team members may not always be a valid assumption even within homogeneous teams. For instance, a robot’s performance may deteriorate over time or a robot may not estimate tasks correctly. Robots that can identify poorly performing team members as performance deteriorates, can dynamically adjust the task assignment strategy. This paper investigates the use of an observation based trust model for detecting unreliable robot team members. Robots can reason over this model to perform dynamic task reassignment to trusted team members. Experiments were performed in simulation and using a team of indoor robots in a patrolling task to demonstrate both centralized and decentralized approaches to task reassignment. The results clearly demonstrate that the use of a trust model can improve performance in the multi-robot patrolling task.
Performance based task assignment in multi- robot patrolling

(Georgia Institute of Technology, 2013) Pippin, Charles, E. ; Christensen, Henrik I. ; Weiss, Lora G.

This article applies a performance metric to the multi-robot patrolling task to more efficiently distribute patrol areas among robot team members. The multi-robot patrolling task employs multiple robots to perform frequent visits to known areas in an environment, while minimizing the time between node visits. Conventional strategies for performing this task assume that the robots will perform as expected and do not address situations in which some team members patrol inefficiently. However, reliable performance of team members may not always be a valid assumption. This paper considers an approach for monitoring robot performance in a patrolling task and dynamically reassigning tasks from those team members that perform poorly. Experimental results from simulation and on a team of indoor robots demonstrate that in using this approach, tasks can be dynamically and more efficiently distributed in a multi-robot patrolling application.
3D Textureless Object Detection and Tracking: An Edge-based Approach

(Georgia Institute of Technology, 2012-10) Choi, Changhyun ; Christensen, Henrik I.

This paper presents an approach to textureless object detection and tracking of the 3D pose. Our detection and tracking schemes are coherently integrated in a particle filtering framework on the special Euclidean group, SE(3), in which the visual tracking problem is tackled by maintaining multiple hypotheses of the object pose. For textureless object detection, an efficient chamfer matching is employed so that a set of coarse pose hypotheses is estimated from the matching between 2D edge templates of an object and a query image. Particles are then initialized from the coarse pose hypotheses by randomly drawing based on costs of the matching. To ensure the initialized particles are at or close to the global optimum, an annealing process is performed after the initialization. While a standard edge-based tracking is employed after the annealed initialization, we employ a refinement process to establish improved correspondences between projected edge points from the object model and edge points from an input image. Comparative results for several image sequences with clutter are shown to validate the effectiveness of our approach.
3D Pose Estimation of Daily Objects Using an RGB-D Camera

(Georgia Institute of Technology, 2012-10) Choi, Changhyun ; Christensen, Henrik I.

In this paper, we present an object pose estimation algorithm exploiting both depth and color information. While many approaches assume that a target region is cleanly segmented from background, our approach does not rely on that assumption, and thus it can estimate pose of a target object in heavy clutter. Recently, an oriented point pair feature was introduced as a low dimensional description of object surfaces. The feature has been employed in a voting scheme to find a set of possible 3D rigid transformations between object model and test scene features. While several approaches using the pair features require an accurate 3D CAD model as training data, our approach only relies on several scanned views of a target object, and hence it is straightforward to learn new objects. In addition, we argue that exploiting color information significantly enhances the performance of the voting process in terms of both time and accuracy. To exploit the color information, we define a color point pair feature, which is employed in a voting scheme for more effective pose estimation. We show extensive quantitative results of comparative experiments between our approach and a state-of-the-art.
Performance based monitoring using statistical control charts on multi-robot teams

(Georgia Institute of Technology, 2012-07) Pippin, Charles, E. ; Christensen, Henrik I.

On typical multi-robot teams, there is an implicit assumption that robots can be trusted to effectively perform assigned tasks. However, reliable performance of team members may not always be a valid assumption. For instance, a robot’s performance may deteriorate over time or a robot may not estimate tasks correctly. Traditional health monitoring techniques call attention to an operator or assume a binary classification of either success or failure. Robots that can identify poorly performing team members, as performance deteriorates, can adjust the task assignment process dynamically. This paper investigates the use of statistical process control charts from operations research as a tool for monitoring team member performance as part of a multi-robot task assignment framework.
Linguistic Composition of Semantic Maps and Hybrid Controllers. Experimental Robotics

(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.
Coordination Strategies for Multi-robot Exploration and Mapping

(Georgia Institute of Technology, 2012-06) Rogers, John G. ; Nieto-Granda, Carlos ; Christensen, Henrik I.

Situational awareness in rescue operations can be provided by teams of autonomous mobile robots. Human operators are required to teleoperate the current generation of mobile robots for this application; however, teleoperation is increasingly difficult as the number of robots is expanded. As the number of robots is increased, each robot may interfere with one another and eventually decrease mapping performance. Through careful consideration of robot team coordination and exploration strategy, large numbers of mobile robots be allocated to accomplish the mapping task more quickly and accurately.
Learning task performance in market-based task allocation

(Georgia Institute of Technology, 2012-06) Pippin, Charles, E. ; Christensen, Henrik I.

Auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents can be trusted to effectively perform assigned tasks. However, reliable performance of team members may not always be a valid assumption. An approach to learning team member performance is presented, which enables more efficient task assignment. A policy gradient reinforcement learning algorithm is used to learn a cost factor that can be applied individually to auction bids. Experimental results demonstrate that agents that model team member performance using this approach can more efficiently distribute tasks in multi-agent auctions.
Cooperation based Dynamic Team Formation in Multi-Agent Auctions

(Georgia Institute of Technology, 2012-05) Pippin, Charles, E. ; Christensen, Henrik I.

Auction based methods are often used to perform distributed task allocation on multi-agent teams. Many existing approaches to auctions assume fully cooperative team members. On in-situ and dynamically formed teams, reciprocal collaboration may not always be a valid assumption. This paper presents an approach for dynamically selecting auction partners based on observed team member performance and shared reputation. In addition, we present the use of a shared reputation authority mechanism. Finally, experiments are performed in simulation on multiple UAV platforms to highlight situations in which it is better to enforce cooperation in auctions using this approach.