Control of Distributed Systems and Swarm Robotics

 

 

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Control of a large number of distributed systems provides several technical challenges which includes scalability of control and information processing algorithms, stability, and robustness to not only sensor/actuator noises but also to broken/delayed communication. The research conducted in CDS Lab is at the intersection of a number of multidisciplinary aspects (as shown in the figure) including:

  1.  Systems and controls: Development of decentralized control algorithms, optimization methods, and novel mathematical tools to analyze the stability of system and its robustness to noise and uncertainties.
  2. Probability and Statistics: Development of tools to represent uncertainties and for their analysis, specifically in a networked environment.
  3. Self-organizing behaviors: Drawing inspirations from systems seen in nature at microscopic levels such cellular behaviors to that seen at macroscopic levels such flock of birds, swarm of ants, and schools of fish.

Researchers at the CDS lab have been extensively involved in various aspects of research on multi-robot cooperative control including work on aggregation/segregation of heterogeneous units in robotic swarms, development of control laws for a robotic swarm emulating ant foraging behavior, noise induced adaptive emergent behaviors in swarm robotic systems, and performance driven decentralized control of robotic agents. Some of current focus of research in CDS Lab include: study of statistical-mechanical concepts such random graphs, random geometric graphs, and mean-field theory to model and analyze system behaviors; and study of the effect of noise on robust self-organization of robotic swarms.

 

 

 

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Swarming Of Heterogeneous Robots

It is often difficult to obtain precise information about the states of large-scale systems due to non-linearities involved, complex interactions, and uncertainties. It may, however, be possible to analytically obtain average quantities that would provide crude representations of complex behaviors. For example, in a study on segregation of heterogeneous units in a swarm of robotics agents, average distances between agents of similar and dis-similar types were used to obtain analytical results on segregation of heterogeneous agents under very simple control laws based on differential potential.

 

 

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Spatiotemporal chemotactic model for ant foraging

A significant challenge in swarm robotics is the design and control of a robotic swarm capable of adaptive behavior dictated by local communication in uncertain environments. Given their remarkable propensity to adapt to rapidly changing environments, the dynamics of biological systems, such as an ant colony system, provide fundamental insights in this context. This research focused on development of a new mathematical model, represented by coupled Partial Differential Equations inspired by Keller Segel model of bacterial chemotaxis, for ant foraging that accounts for different behaviors exhibited by foragers in search of food and food carrying ants. The model essentially shows the evolution of i) food searching (foraging) ants; ii) food carrying ants; and iii) the pheromone distribution in the space. Food search is governed by an environmental potential, the pheromone gradient and is also characterized by inherent randomness that allows for a comprehensive search of the entire domain for food sources. Moreover, a fraction of the foraging ants change character to food carrying ants within a certain neighborhood of the food sources and the reverse happens within a neighborhood of the nest.

 

 

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Cyclic Pursuit by Multiple UAVs for Monitoring

This project focused on developing cooperative control laws for cyclic pursuit of robotic agents to track a closed perimeter. In this work, a linear interaction law is proposed for pursuit with UAV. Using principles of linear control theory, stability conditions were obtained and it was shown that, within specified stability regions, the system was robust to addition or deletion of agents. Similarly, cooperative control algorithms for wildfire monitoring and fighting using non-linear interaction potential is also developed.

 

 

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Role of Noise in Robust Self-Organizing Behaviors

On a research studying the effect of randomness on robust flocking behaviors in multi-robotic agent systems, we were able to show that single-cluster flock would asymptotically form if designed randomness is introduced in the system. It was concluded via analysis as well as extensive simulations that randomness provided a necessary mechanism for robust flocking behavior.