Self-organized robotic system design and autonomous odor localization.

摘要

This thesis presents a methodology for designing self-organized autonomous robotic systems and demonstrates how this process can be applied to the problem of finding the source of an airborne odor plume. The design methodology is applicable to other task domains and the resulting odor localization system extends the state of the art.; The design procedure centers on the ability to define a specific task performance metric, systematically evaluate performance in a realistic environment, Once such relationships have been experimentally validated in a test environment, they can be used to guide the design of a deployable system. Because this process relies heavily on evaluative feedback, this work emphasizes the development of tools that allow the collection of accurate performance data. It presents a reliable multiple robot test-bed and some task-enabling sensory hardware, as well as validation of the sensory and kinematic models used in simulation. Also, a reinforcement learning methodology is described that provides consistent optimization performance while minimizing the amount of required evaluation.; The design methodology is applied to the task of odor localization. Specifically, this thesis analyzes a basic collective search task and derives the optimal group size and expected performance bounds for random and coordinated search. It also investigates a set of biologically inspired behaviors that permit an agent to traverse an odor plume to its source and describes the common characteristics of successful algorithms. One of these algorithms is implemented on the real test-bed and in simulation to verify that plume traversal is taking place and that the use of multiple collaborating robots can expand the reachable performance space. Collective search and plume traversal are then combined (along with egocentric source declaration) into the full odor localization task which is optimized in simulation. Then, following the design methodology, a model is presented which can aid in the prediction of performance and choice of algorithm parameters in more complex environments. Finally, a flocking behavior is designed, and the addition of this flocking behavior to the plume tracing algorithm is shown to produce a more capable system.