Propelling Underwater Vehicles Using Vortex Ring Generation
Ann Marie Polsenberg, Joel Burdick

Abstract. As robots designed to operate underwater become more common, it is useful to look at ways to make them more efficient. Autonomous Underwater Vehicles (AUVs) carry their power source with them, so improving the efficiency of the vehicle will also increase the maximum duration time for missions that the vehicle can perform. One area in which efficiency is very important is the propulsion system. We propose that vortex ring generators may be a viable way to propel these vehicles. This idea stems from looking at aquatic animals, such as squid, which use this mechanism. Our work involves the modeling, design, construction and analysis of synthetic jets. The next step will be to design a small vehicle that uses these thrusters and to begin an investigation into the control of such a vehicle. (full report)

Averaging Methods for Control of Biomimetic Locomotion
Patricio Vela, Joel Burdick

Abstract. Biomimetic control systems are inherently difficult to control because they typically fall within the class of nonlinear control systems known as underactuated systems with drift (a few models do exist without drift). The nature of underactuated systems is such that smooth exponentially stabilizing feedback laws do not exist, and therefore require time-varying or non-smooth (either continuous or discontinuous) feedback strategies. Since many biomimetic systems exhibit time-periodic behavior, it is advantageous to examine the role of time-periodic forcing elements on the underlying equations of motion.

Current work focuses on developing a general averaging theory that works to arbitrary orders of approximation so as to better understand the nonlinear response of dynamical systems. From this, it should be possible to develop feedback control laws that are exponentially stabilizing. Recent work has developed the averaging methods and applied them to various biomimetic systems, such as a carangiform fish, a snakeboard, and a kinematic biped. The nature of the averaging results and subsequent control laws demonstrate that the approach may hold generally, irrespective of the locomotive strategy, i.e., discontinuous legged locomotion, land locomotion, or hydrodynamic locomotion. (full report)

Modeling Swarm-Based, Distributed Robotic Manipulation
William Agassounon, Kjerstin Easton, and Alcherio Martinoli
Collaborators: Joel Burdick, Kristina Lerman, Wulfram Gerstner

Abstract. We developed a macroscopic modeling methodology for swarm-based, distributed robotic manipulation. The methodology is well-suited for nonspatial metrics as it does not take into account robots’ trajectories or the spatial distribution of objects in the environment. The strength of the proposed models is that they have been built up incrementally, with matching between models and embodied simulations (and sometimes, real robot experiments) verified at each step as new complexity was added. Precise heuristic criteria based on geometrical considerations and systematic tests with one or two embodied agents prevent the introduction of free parameters into the model. Two concrete case-studies were considered. The first case-study, referred to as the aggregation experiment, is a non-collaborative manipulation concerned with gathering and clustering small objects initially scattered in an enclosed arena. The other case-study is involves strictly collaborative manipulation and is referred to as the stick-pulling experiment, as the robots’ task is to collaborate to pull sticks out of holes in the arena floor. Results show that the proposed approach delivers quantitatively accurate predictions, in particular for nonspatial metrics related to both the aggregation and stick-pulling processes, and constitutes a computationally efficient tool. The simplicity of the modeling methodology suggests that it is easily applicable to other experiments characterized by different agent capabilities and individual control algorithms. (full report)

Distributed Exploration and Coverage
Nikolaus Correll, Kjerstin Easton, Alcherio Martinoli, and Joel Burdick
Collaborators: Jonathan Witt, Edmond Wong (NASA Glenn Center)

Abstract. The aim of this project is to formulate an efficient exploration and coverage algorithm for a swarm of mobile agents. We present a completely distributed algorithm relying on agents endowed with identical controllers. The controller for the individual agent is realized through a hybrid approach using deliberative planning together with reactive behavior for collision avoidance. To exchange information about task progress the agents exploit a cellular decomposition of the environment. Coverage is performed using a grid-based algorithm (the Spanning Tree Coverage algorithm). Interaction between the agents is constrained to decentralized line-of-sight communication with limited range. The algorithm has been proved regarding completeness and its performance has been systematically investigated using an embodied simulator. (full report)