O. Burchan Bayazit

Roadmap-based flocking for complex environments

Flocking behavior is very common in nature, and there have been ongoing research efforts to simulate such behavior in computer animations and robotics applications. Generally, such work considers behaviors that can be determined independently by each flock member solely by observing its local environment, e.g., the speed and direction of its neighboring flock members. Since flock members are not assumed to have global information about the environment, only very simple navigation and planning techniques have been considered for such flocks. In this paper, we investigate how the addition of global information in the form of a roadmap of the environment enables more sophisticated flocking behaviors. In particular, we study and propose new techniques for three distinct group behaviors: homing, exploring and shepherding. These behaviors exploit global knowledge of the environment and utilize knowledge gathered by all flock members. This knowledge is communicated by allowing individual flock members to dynamically update the shared roadmap to reflect (un)desirable routes or regions. We present experimental results showing how the judicious use of simple roadmaps of the environment enables more complex behaviors to be obtained at minimal cost.

Mobile Wireless Sensor Network Connectivity Repair with K-Redundancy

Connectivity is an important requirement for wireless sensor networks especially in real-time monitoring and data transfer applications. However, node movements and failures change the topology of the initial deployed network, which can result in partitioning of the communication graph. In this paper, we present a method for maintaining and repairing the communication network of a dynamic mobile wireless sensor network. We assume that we cannot control the motion of wireless sensor nodes, but there are robots whose motion can be controlled by the wireless sensor nodes to maintain and repair the connectivity of the network. At the heart of our method lies a novel graph property, k-redundancy, which is a measure of the importance of a node to the connectivity of a network.We first show that this property can be used to estimate repair time of a dynamic network. Then, we present a dynamic repair algorithm that minimizes expected repair time. Finally, we show the effectiveness of our method with extensive simulations and its feasibility with experiments on real robots and motes.

Automated motion synthesis for dancing characters

In this paper, we present a technique to automatically synthesize dancing motions for arbitrary songs with dance beats. Our technique is based on analysing a musical tune (can be a song or melody) and synthesizing a motion for the virtual character where the characters movement synchronizes to the musical beats. In order to analyse beats of the tune, we developed a fast algorithm. Our motion synthesis algorithm analyses library of stock motions and generates new sequences of movements that were not described in the library. We show that our motion synthesis algorithm is better than previous dance generation techniques. We also present two algorithms to synchronize dance moves and musical beats: a fast greedy algorithm, and a genetic algorithm. Our experimental results show that we can generate new sequences of dance figures in which the dancer reacts to music and dances in synchronization with the music. Copyright

Emergent Task Allocation for Mobile Robots

Multi-robot systems require efficient and accurate planning in order to perform mission-critical tasks. However, algorithms that find the optimal solution are usually computationally expensive and may require a large number of messages between the robots as the robots need to be aware of the global spatiotemporal information. In this paper, we introduce an emergent task allocation approach for mobile robots. Each robot uses only the information obtained from its immediate neighbors in its decision. Our technique is general enough to be applicable to any task allocation scheme as long as a utilization criteria is given. We demonstrate that our approach performs similar to the integer linear programming technique which finds the global optimal solution at the fraction of its cost. The tasks we are interested in are detecting and controlling multiple regions of interest in an unknown environment in the presence of obstacles and intrinsic constraints. The objective function contains four basic requirements of a multi-robot system serving this purpose: control regions of interest, provide communication between robots, control maximum area and detect regions of interest. Our solution determines optimal locations of the robots to maximize the objective function for small problem instances while efficiently satisfying some constraints such as avoiding obstacles and staying within the speed capabilities of the robots, and finds an approximation to global optimal solution by correlating solutions of small problems.

Roadmap query for sensor network assisted navigation in dynamic environments

Mobile entity navigation in dynamic environments is an essential part of many mission critical applications like search and rescue and fire fighting. The dynamism of the environment necessitates the mobile entity to constantly maintain a high degree of awareness of the changing environment. This criteria makes it difficult to achieve good navigation performance by using just on-board sensors and existing navigation methods and motivates the use of wireless sensor networks (WSNs) to aid navigation. In this paper, we present a novel approach that integrates a roadmap based navigation algorithm with a novel WSN query protocol called Roadmap Query (RQ). RQ enables collection of frequent, up-to-date information about the surrounding environment, thus allowing the mobile entity to make good navigation decisions. Simulation results under realistic fire scenarios show that in highly dynamic environments RQ outperforms existing approaches in both navigation performance and communication cost. We also present a mobile agent based implementation of RQ along with preliminary experimental results, on Mica2 motes.

Better Group Behaviors Using Rule-Based Roadmaps

While techniques exist for simulating group behaviors, these methods usually only provide simplistic navigation and planning capabilities. In this work, we explore the benefits of integrating roadmap-based path planning methods with flocking techniques. We show how group behaviors such as exploring can be facilitated by using dynamic roadmaps (e.g., modifying edge weights) as an implicit means of communication between flock members. Extending ideas from cognitive modeling, we embed behavior rules in individual flock members and in the roadmap. These behavior rules enable the flock members to modify their actions based on their current location and state. We propose new techniques for three distinct group behaviors: homing, exploring (covering and goal searching) and passing through narrow areas. Animations of these behaviors can be viewed at http://parasol.tamu.edu/dsmft.

A motion planning processor on reconfigurable hardware

Motion planning algorithms enable us to find feasible paths for moving objects. These algorithms utilize feasibility checks to differentiate valid paths from invalid ones. Unfortunately, the computationally expensive nature of such checks reduces the effectiveness of motion planning algorithms. However, by using hardware acceleration to speed up the feasibility checks, we can greatly enhance the performance of the motion planning algorithms. Of course, such acceleration is not limited to feasibility checks; other components of motion planning algorithms can also be accelerated using specially designed hardware. A field programmable gate array (FPGA) is a great platform to support such an acceleration. An FPGA is a collection of digital gates which can be reprogrammed at run time, i.e., it can be used as a CPU that reconfigures itself for a given task. In this paper, we study the feasibility of an FPGA based motion planning processor and evaluate its performance. In order to leverage its highly parallel nature and its modular structure, our processor utilizes the probabilistic roadmap method at its core. The modularity enables us to replace the feasibility criteria with other ones. The reconfigurability lets us run our processor in different roles, such as a motion planning co-processor, an autonomous motion planning processor or dedicated collision detection chip. Our experiments show that such a processor is not only feasible but also can greatly increase the performance of current algorithms

Automated motion synthesis for dancing characters: Motion Capture and Retrieval

Not everything is perceived as it is provided by the environment. Depending on focus and attention perception can vary and therefore also the knowledge about the world. Virtual humans are sensing the virtual world, storing knowledge and using it to perform tasks. This paper describes our approach to model perceiving, storing and forgetting knowledge as the main regulation of tasks. We use different forms and levels of knowledge which can be independently adapted to different personalities and situations by combining computer graphics methods with psychological models. Copyright

Automated Motion Synthesis for Virtual Choreography

In this paper, we present a technique to automatically synthesize dancing moves for arbitrary songs. Our current implementation is for virtual characters, but it is easy to use the same algorithms for entertainer robots, such as robotic dancers. Our technique is based on analyzing a musical tune (can be a song or melody) and synthesizing a motion for the virtual character where the characters movement synchronizes to the musical beats. In order to analyze beats of the tune, we developed a fast and novel algorithm. Our motion synthesis algorithm analyze library of stock motions and generates new sequences of movements that were not described in the library. We present two algorithms to synchronize dance moves and musical beats: a fast greedy algorithm, and a genetic algorithm. Our experimental results show that we can generate new sequences of dance figures in which the dancer reacts to music and dances in synchronization with the music. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Automated Motion Synthesis for Virtual Choreography

Intelligent distributed architecture (IDA) for mobile sensor data fusion

In this paper, we provide a new two-layer scalable architecture, Intelligent Distributed Architecture (IDA). The first layer of IDA, the application layer, is responsible for the mission specific tasks. The second layer, the network layer, is responsible for relaying the information quickly to reduce latency. In order to increase efficiency, we propose an intelligent network layer that evaluates the network traffic through application provided policies. The evaluation assesses improved accuracy in bandwidth limited scenarios. IDA policies permit the application to drop, merge or modify the packets in real time from different sources. We demonstrate how this architecture can be applied to an example application, distributed target tracking. We also propose some new algorithms that can be used in conjunction with IDA for target tracking. Our experiments on this application show that IDA improves system performance when the bandwidth is limited.