Published Papers

Self-Assembly
• A System for In-Space Assembly (2004)
• Complete Process for Target Form Self-Replication from Homogeneous Self-Reconfigurable Robot Modules (2006)

Sensor Tasking
• Visual Surveillance Coverage: Strategies and Metrics (2005)
• Task-Selection and Task-Merging for Directional and Vision-Based Sensors (2005)

Sensor Understanding
• Distributed Biologically Based Real Time Tracking in the Absence of Prior Target Information (2005)
• Single-Sensor Probabilistic Localization on the SeReS Self-Reconfigurable Robot (2006)

Self-Reconfigurable Control
• Multimode Locomotion for Reconfigurable Robots (2006)
• Multimode Locomotion via Superbot Robots (2006)
• Transformation of Control in Congruent Self-Reconfigurable Robot Topologies (2006)
• Comparative Study of Locomotion Methods for Modular and Self-Reconfigurable Robots (2005 - Unpublished)

Title:

A System for In-Space Assembly

Authors:

Everist, J., Mogharei, K., Suri, H., Ranasinghe, N., Khoshnevis, B., Will, P., Shen, W.-M.

Presented:

International Conference on Intelligent and Robotic Systems, Sendai, Japan, 2004

Abstract:

This paper presents an experimental system for assembly in space. A weightless and frictionless environment is approximated using an air-hockey table where robots and structural components can float on the surface. The robots use fan propulsion to dock with components and assemble them together to make 2Dstructures. This system is designed to implement three key technologies for space self-assembly: 1) intelligent components with universal connectors, 2) a set of serf-reconfigurable robots that fetch and assemble components, and 3)a distributed method for controlling the robotic-assembly process. An overview of the system's design and experimental results is presented.

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Title:

Complete Process for Target Form Self-Replication from Homogeneous Self-Reconfigurable Robot Modules

Authors:

J. Everist, W.-M. Shen

Presented:

ALife X, Workshop on Machine Self-Replication, Bloomington, IN, 2006

Abstract:

We describe a complete system for the autonomous self- replication of multiple instances of an user-defined target robot form using self-reconfigurable robots. We describe the tools that are required, the steps of the assembly process, and the work required to realize a working system. We show how this system is practical for a real self-reconfigurable robot system. We give some experimental results that show the feasibility of the system and describe future work required to give a complete demonstration.

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Title:

Visual Surveillance Coverage: Strategies and Metrics

Authors:

J. Everist, T.N. Mundhenk, C. Landauer, K. Bellman

Presented:

Proceedings of SPIE Conference on Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, Optics East, Boston, USA, 2005

Abstract:

Many sensor systems such as security cameras and satellite photography are faced with the problem of where they should point their sensors at any given time. With directional control of the sensor, the amount of space available to cover far exceeds the field-of-view of the sensor. Given a task domain and a set of constraints, we seek coverage strategies that achieve effective area coverage of the environment. We develop metrics that measure the quality of the strategies and give a basis for comparison. In addition, we explore what it means for an area to be "covered" and how that is affected by the domain, the sensor constraints, and the algorithms. We built a testbed in which we implement and run various sensor coverage strategies and take measurements on their performance. We modeled the domain of a camera mounted on pan and tilt servos with appropriate constraints and time delays on movement. Next, we built several coverage strategies for selecting where the camera should look at any given time based on concepts such as force-mass systems, scripted movements, and the time since an area was last viewed.

Finally, we describe several metrics with which we can compare the effectiveness of different coverage strategies. These metrics are based on such things as how well the whole space is covered, how relevant the covered areas are to the domain, how much time is spent acquiring data, how much time is wasted while moving the servos, and how well the strategies detect new objects moving through space.

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Title:

Task-Selection and Task-Merging for Directional and Vision-Based Sensors

Authors:

J. Everist, T.N. Mundhenk, C. Landauer, K. Bellman

Presented:

Proceedings of SPIE Conference on Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, Optics East, Boston, USA, 2005

Abstract:

Many vision research projects involve a sensor or camera doing one thing and doing it well. Fewer research projects have been done involving a sensor trying to satisfy simultaneous and conflicting tasks. Satisfying a task involves pointing the sensor in the direction demanded by the task. We seek ways to mitigate and select between competing tasks and also, if possible, merge the tasks together to be simultaneously achieved by the sensor. This would make a simple pan-tilt camera a very powerful instrument. These two approaches are task selection and task merging respectively.

We built a simple testbed to implement our task selection and task merging schemes. We use a digital camera as our sensor attached to pan and tilt servos capable of pointing the sensor in different directions.

We use three different types of tasks for our research: target tracking, surveillance coverage, and initiative. Target tracking is the task of following a target with a known set of features. Surveillance coverage is the task of ensuring that all areas of the space are routinely scanned by the sensor. Initiative is the task of focusing on new things of potential interest should they appear in the course of other activities.

Given these heterogeneous task descriptions, we achieve task selection by assigning priority functions to each task and letting the camera select among the tasks to service. To achieve task merging, we introduce a concept called "task maps" that represent the regions of space the tasks wish to attend with the sensor. We then merge the task maps and select a region to attend that will satisfy multiple tasks at the same time if possible.

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Title:

Distributed Biologically Based Real Time Tracking in the Absence of Prior Target Information

Authors:

T.N. Mundhenk, J. Everist, C. Landauer, K. Bellman

Presented:

Proceedings of SPIE Conference on Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, Optics East, Boston, USA, 2005

Abstract:

We are developing a distributed system for the tracking of people and objects in complex scenes and environments using biologically based algorithms. An important component of such a system is its ability to track targets from multiple cameras at multiple viewpoints. As such, our system must be able to extract and analyze the features of targets in a manner that is sufficiently invariant of viewpoints, so that they can share information about targets, for purposes such as tracking. Since biological organisms are able to describe targets to one another from very different visual perspectives, by discovering the mechanisms by which they understand objects, it is hoped such abilities can be imparted on a system of distributed agents with many camera viewpoints. Our current methodology draws from work on saliency and center surround competition among visual components that allows for real time location of targets without the need for prior information about the targets visual features. For instance, gestalt principles of color opponencies, continuity and motion form a basis to locate targets in a logical manner. From this, targets can be located and tracked relatively reliably for short periods. Features can then be extracted from salient targets allowing for a signature to be stored which describes the basic visual features of a target. This signature can then be used to share target information with other cameras, at other viewpoints, or may be used to create the prior information needed for other types of trackers. Here we discuss such a system, which, without the need for prior target feature information, extracts salient features from a scene, binds them and uses the bound features as a set for understanding trackable objects.

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Title:

Single-Sensor Probabilistic Localization on the SeReS Self-Reconfigurable Robot

Authors:

K. Payne, J. Everist, F. Hou, W.-M. Shen

Presented:

Proceedings on International Conference on Intelligent Autonomous Systems, Tokyo, Japan, 2006

Abstract:

This paper proposes a novel method for localizing a stationary infrared source of unknown orientation relative to a static docking sensor. This method uses elliptical approximations of likely positions of the infrared source and computes the intersections to find the most probable locations. It takes only a few samples to localize, is easily computed with inexpensive microcontrollers, and is robust to sensor noise. We then compare our approach with two other methods. The first uses a Bayesian filter across a map of discrete locations in the robot's operational workspace to determine the suspected source position. The second also uses a probability distribution map but uses the method described by Elfes in his paper on probabilistic sonar-based mapping and navigation [1]. We show that our approach localizes quickly with a single sensor and is no more computationally demanding than other methods.

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Title:

Multimode Locomotion for Reconfigurable Robots

Authors:

M .Krivokon, C.H. Chiu, J.Everist, M. Rubenstein, J. Venkatesh, W.-M. Shen

Presented:

Autonomous Robots, 20(2):165-177, 2006.

Abstract:

One of the most challenging issues for a self- sustaining robotic system is how to use its limited resources to accomplish a large variety of tasks. The scope of such tasks could include transportation, exploration, construc- tion, inspection, maintenance, in-situ resource utilization, and support for astronauts. This paper proposes a modular and reconfigurable solution for this challenge by allowing a robot to support multiple modes of locomotion and select the appropriate mode for the task at hand. This solution re- lies on robots that are made of reconfigurable modules. Each locomotion mode consists of a set of characteristics for the environment type, speed, turning-ability, energy-efficiency, and recoverability from failures. This paper demonstrates a solution using the SuperBot robot that combines advantages from M-TRAN, CONRO, ATRON, and other chain-based and lattice-based robots. At the present, a single real Super- Bot module can move, turn, sidewind, maneuver, and travel on batteries up to 500 m on carpet in an office environment. In physics-based simulation, SuperBot modules can perform multimodal locomotions such as snake, caterpillar, insect, spider, rolling track, H-walker, etc. It can move at speeds of up to 1.0 m/s on flat terrain using less than 6 W per module, and climb slopes of no less 40 degrees.

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Title:

Multimode Locomotion via Superbot Robots

Authors:

W.-M. Shen, M. Krivokon, H. Chiu, J. Everist, M. Rubenstein, J. Venkatesh

Presented:

Proceedings of the International Conference on Robotics and Automation, Orlando, Florida, 2006

Abstract:

This paper presents a modular and reconfigurable robot for multiple locomotion modes based on reconfigurable modules. Each mode consists of characteristics for the environment type, speed, turning-ability, energy-efficiency, and recoverability from failures. The paper demonstrates this solution by the Superbot robot that combines advantages from MTRAN, CONRO and others. Experimental results, both in real robots and in simulation, have shown the validity of the approach and demonstrated the movements of forward, backward, turn, sidewinder, maneuver, and travel on batteries up to 500 meters on a flat terrain. In physics-based simulation, Superbot can perform as snake, caterpillar, insect, spider, rolling track, H- walker, etc., and move 1.0 meter/second on flat terrain with less than 6W/module, and climb slopes of no less 40 degrees.

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Title:

Transformation of Control in Congruent Self-Reconfigurable Robot Topologies

Authors:

J. Everist, F. Hou, W.-M. Shen

Presented:

Proceedings of the International Conference on Intelligent Robots and Systems, Beijing, China, 2006.

Abstract:

Much work on self-reconfigurable robotics has been focused on motion planning and physical reconfiguration of the robot. Using the Superbot self-reconfigurable robot, we focus on the details of realizing locomotion gaits given that a single robot topology can be realized in a large number of different ways. That is, each module in the robot topology has 4 symmetric orientations that are functional and shape equivalent. Once a role is selected for each module, such as through the use of hormone-inspired control, each module's role is supplied with a gait template which then must be transformed to suit the local configurations of each module with respect to the global topology. We provide a theoretical framework for which this can be accomplished.

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Title:

Comparative Study of Locomotion Methods for Modular and Self-Reconfigurable Robots

Authors:

J. Everist, W.-M. Shen

Presented:

(2005 - Unpublished)

Abstract:

Modular and self-reconfigurable robots can take any form by connecting modules together in different configurations. Each module has its own set of motors and due to the high amount of redundancy and distributed processing, many different methods have been developed to control them. We extract some basic low-level methods that are common to all locomotion strategies developed by researchers. We compare and contrast them to see their advantages and disadvantages.

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