Last Updated: Oct 14, 2004.
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Low Kian Hsiang.


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PROJECTS >
Distributed Layered Architecture for Self-Organization of Mobile Sensor Networks
This work presents a layered approach that integrates high-level swarm-based task allocation with low-level reactive motion control for distributed mobile sensor network coverage in complex, dynamic environments.

Continuous-Spaced Action Selection for Single- and Multi-Robot Tasks with Cooperative Extended Kohonen Maps
This work implements an action selection mechanism that operates in continuous state and action spaces via the cooperation and competition of Extended Kohonen Maps to achieve more complex single- and multi-robot motion tasks.

Integrated Robot Planning and Control with Extended Kohonen Maps
This work integrates high-level deliberative planning with low-level reactive control for goal-directed, collision-free robot motion in complex, unpredictable environments. By enhancing the reactive capabilities with Extended Kohonen Maps, the workload of the planner can be eased.
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DISTRIBUTED LAYERED ARCHITECTURE FOR SELF-ORGANIZATION OF MOBILE SENSOR NETWORKS
PROBLEM STATEMENT

One of the fundamental issues that arises in a sensor network is coverage. Traditionally, network coverage is maximized by determining the optimal placement of static sensors in a centralized manner, which can be related to the class of art gallery problems. However, recent investigations in sensor network mobility reveal that mobile sensors can self-organize to provide better coverage than static placement. Existing applications have only utilized uninformed mobility (i.e., random motion or patrol). In contrast, our work here focuses on informed, intelligent mobility to further improve coverage. Our network coverage problem is motivated by the following constraints that discourage static sensor placement or uninformed mobility: a) no prior information about the exact target locations, population densities or motion pattern, b) limited sensory range, and c) very large area to be observed. All these conditions may cause the sensors to be unable to cover the entire region of interest. Hence, fixed sensor locations or uninformed mobility will not be adequate in general. Rather, the sensors have to move dynamically in response to the motion and distribution of targets and other sensors to maximize coverage. Inspired by robotics, the above problem may be regarded as that of low-level motion control to coordinate the sensors' target tracking movements in the continuous workspace. Alternatively, it can be cast as a high-level task allocation problem by segmenting the workspace into discrete regions such that each region is assigned a group or coalition of sensors to track the targets within.


PROPOSED METHOD

This work presents a reactive layered multi-robot architecture for distributed mobile sensor network coverage in complex, dynamic environments. At the lower layer, each robot uses a reactive motion control strategy known as Cooperative Extended Kohonen Maps to coordinate their target tracking within a region without the need of communication. This strategy is also responsible for obstacle avoidance, robot separation to minimize task interference, and navigation between regions via beacons or checkpoints plotted by a motion planner. At the higher layer, the robots use a dynamic ant-based task allocation scheme to cooperatively self-organize their coalitions in a decentralized manner according to the target distributions across the regions. This scheme addresses the following issues, which distinguish it from the other task allocation mechanisms:
Task Allocation for Multi-Robot Tasks: Existing algorithms (e.g., auction-and behavior-based) assume a multi-robot task can be partitioned into single-robot tasks. But this may not be always possible or the multi-robot task can be more efficiently performed by coalitions of robots.
Coalition Formation for Minimalist Robots: Existing coalition formation schemes require complex planning, explicit negotiation, and precise estimation of coalitional cost. Hence, they do not perform well in dynamic, real-time scenarios.
Cooperation of Resource-Limited Robots: Robots with limited communication and sensing capabilities (i.e., partial observability) can only obtain local, uncertain information of the dynamic environment. With limited computational power, their cooperative strategies cannot involve complex planning or negotiations.



VIDEOS

Coverage of 30 targets (green) with 15 ant robots (white)
  1. Self-organization of swarm coalitions to unknown, time-varying target distribution after

  2. Robot switching to region of higher task demand (i.e., targets to robots ratio) @refer to the above publications for information on the region numbers


PUBLICATIONS
  1. Task Allocation via Self-Organizing Swarm Coalitions in Distributed Mobile Sensor Network.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the 19th National Conference on Artificial Intelligence (AAAI-04), pages 28-33, San Jose, CA, Jul 25-29, 2004.
    [ ]   [ ]   [ 26.7% Acceptance Rate ]

  2. Reactive, Distributed Layered Architecture for Resource-Bounded Multi-Robot Cooperation: Application to Mobile Sensor Network Coverage.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'04), pages 3747-3752, New Orleans, LA, Apr 26 - May 1, 2004.
    [ ]   [ ]

CONTINUOUS-SPACED ACTION SELECTION FOR SINGLE- AND MULTI-ROBOT TASKS WITH
COOPERATIVE EXTENDED KOHONEN MAPS
PROBLEM STATEMENT

A central issue in the design of behavior-based control architectures for autonomous mobile robots is the formulation of effective mechanisms to coordinate the behaviors. These mechanisms determine the policy of conflict resolution between behaviors, which involves behavioral cooperation and competition to select the most appropriate action. The actions are selected so as to optimize the achievement of the goals or behavioral objectives. Developing such an action selection methodology is non-trivial due to realistic constraints such as environmental complexity and unpredictability, and resource limitations, which include computational and cognitive capabilities of the robot, incomplete knowledge of the environment, and time constraints. As a result, action selection can never be absolutely optimal. Given these constraints, the action selection scheme should be able to choose actions that are good enough to satisfy multiple concurrent, possibly conflicting, behavioral objectives.


PROPOSED METHOD

Our motivation of the action selection mechanism is to develop a motion control strategy for autonomous non-holonomic mobile robots that can perform distributed multi-robot surveillance in unknown, dynamic, complex, and unpredictable environments. By implementing the action selection framework using an assemblage of self-organizing neural networks, it induces the following key features that significantly enhance the agent's action selection capability: self-organization of continuous state and action spaces to provide smooth, efficient and fine motion control, and action selection via the cooperation and competition of Extended Kohonen Maps to achieve more complex motion tasks: (1) negotiation of unforeseen concave and narrowly spaced obstacles, and (2) cooperative tracking of multiple mobile targets by a team of robots. Qualitative and quantitative comparisons for single- and multi-robot tasks show that our framework can provide better action selection than do potential fields method.


VIDEOS
  1. Cooperative tracking of moving targets by robots using cooperative Extended Kohonen Maps (145KB)


PUBLICATIONS
  1. Continuous-Spaced Action Selection for Single- and Multi-Robot Tasks Using Cooperative Extended Kohonen Maps.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the IEEE International Conference on Networking, Sensing and Control (ICNSC'04) (Invited Paper to Special Session on Visual Surveillance), pages 198-203, Taipei, Taiwan, Mar 21-23, 2004.
    [ ]   [ ]

  2. Action Selection for Single- and Multi-Robot Tasks Using Cooperative Extended Kohonen Maps.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03), pages 1505-1506, Acapulco, Mexico, Aug 9-15, 2003.
    [ ]   [ ]   [ 27.6% Acceptance Rate ]

  3. Action Selection in Continuous State and Action Spaces by Cooperation and Competition of Extended Kohonen Maps.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the 2nd International Joint Conference on Autonomous Agents & MultiAgent Systems (AAMAS-03), pages 1056-1057, Melbourne, Australia, Jul 14-18, 2003.
    [ ]   [ ]

INTEGRATED ROBOT PLANNING AND CONTROL WITH EXTENDED KOHONEN MAPS
PROBLEM STATEMENT

Robot motion research has proceeded along two separate directions: high-level deliberative planning and low-level reactive control. Deliberative planning uses a world model to generate an optimal sequence of collision-free actions that can achieve a globally specified goal in a complex static environment. However, in a dynamic environment, unforeseen obstacles may obstruct the action sequence, and replanning to react to these situations can be too computationally expensive. On the other hand, reactive control directly couples sensed data to appropriate actions. It allows the robot to respond robustly and timely to unexpected obstacles and environmental changes but may be trapped by them.


PROPOSED METHOD

The problem of goal-directed, collision-free motion in a complex, unpredictable environment can be solved by tightly integrating high-level deliberative planning with low-level reactive control. This work presents two such architectures for a nonholonomic mobile robot. To achieve real-time performance, reactive control capabilities have to be fully realized so that the deliberative planner can be simplified. These architectures are enriched with reactive target reaching and obstacle avoidance modules. Their target reaching modules use indirect-mapping Extended Kohonen Map to provide finer and smoother motion control than direct-mapping methods. While one architecture fuses these modules indirectly via command fusion, the other one couples them directly using cooperative Extended Kohonen Maps, enabling the robot to negotiate unforeseen concave obstacles. The planner for both architectures use a slippery cells technique to decompose the free workspace into fewer cells, thus reducing search time. Any two points in the cell can still be traversed by reactive motion.


VIDEOS
  1. Robot motion in an environment with unforeseen stationary obstacle using command fusion (1.21MB)

  2. Robot motion in an environment with unforeseen moving obstacle using command fusion (1.79MB)

  3. Robot motion in an environment that changes using command fusion (1.06MB)

  4. Robot motion in an environment with unforeseen stationary concave and narrowly spaced convex obstacles using cooperative Extended Kohonen Maps (1.11MB)

  5. Robot motion in an environment with unforeseen moving obstacles using cooperative Extended Kohonen Maps (0.88MB)


PUBLICATIONS
  1. Enhancing the Reactive Capabilities of Integrated Planning and Control with Cooperative Extended Kohonen Maps.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'03), pages 3428-3433, Taipei, Taiwan, May 12-17, 2003.
    [ ]   [ ]

  2. A Hybrid Mobile Robot Architecture with Integrated Planning and Control.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the 1st International Joint Conference on Autonomous Agents & MultiAgent Systems (AAMAS-02), pages 219-226, Bologna, Italy, Jul 15-19, 2002.
    [ ]   [ ]   [ 26% Acceptance Rate ]

  3. Integrated Planning and Control of Mobile Robot with Self-Organizing Neural Network.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'02), pages 3870-3875, Washington, DC, May 11-15, 2002.
    [ ]   [ ]

  4. Integrated Robot Planning and Control with Extended Kohonen Maps.
    Kian Hsiang Low.
    Master's Thesis, Department of Computer Science, School of Computing, National University of Singapore, July, 2002 (Best M.Sc. Thesis Award 2002-2003).
    [ ]   [ ]

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