Scale-free vision-based aerial control of a ground formation with hybrid topology

We present a novel vision-based control method to make a group of ground mobile robots achieve a specified formation shape with unspecified size. Our approach uses multiple aerial control units equipped with downward-facing cameras, each observing a partial subset of the multirobot team. The units compute the control commands from the ground robots' image projections, using neither calibration nor scene scale information, and transmit them to the robots. The control strategy relies on the calculation of image similarity transformations, and we show it to be asymptotically stable if the overlaps between the subsets of controlled robots satisfy certain conditions. The presence of the supervisory units, which coordinate their motions to guarantee a correct control performance, gives rise to a hybrid system topology. All in all, the proposed system provides relevant practical advantages in simplicity and flexibility. Within the problem of controlling a team shape, our contribution lies in addressing several simultaneous challenges: the controller needs only partial information of the robotic group, does not use distance measurements or global reference frames, is designed for unicycle agents, and can accommodate topology changes. We present illustrative simulation results.Comment: This is the accepted version an already published manuscript. See journal reference for detail

Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies

Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation. Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety. In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors. We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions

Data-Driven Methods for Geometric Systems

The tools of geometric mechanics provide a compact representation of locomotion dynamics as ``the reconstruction equation''. We have found this equation yields a convenient form for estimating models directly from observation data. This convenience draws from the method's relatively rare feature of providing high accuracy models with little effort. By little effort, we point to the modeling process's low data requirements and the property that nothing about the implementation changes when substituting robot kinematics, material properties, or environmental conditions, as long as some intuitive baseline features of the dynamics are shared. We have applied data-driven geometric mechanics models toward optimizing robot behaviors both physical and simulated, exploring robots' ability to recover from injury, and efficiently creating libraries of maneuvers to be used as building blocks for higher-level robot tasks. Our methods employed the tools of data-driven Floquet analysis, providing a phase that we used as a means of grouping related measurements, allowing us to estimate a reconstruction equation model as a function of phase in the neighborhood of an observed behavior. This tool allowed us to build models at unanticipated scales of complexity and speed. Our use of a perturbation expansion for the geometric terms led to an improved estimation procedure for highly damped systems containing nontrivial but non-dominating amounts of momentum. Analysis of the role of passivity in dissipative systems led to another extension of the estimation procedure to robots with high degrees of underactuation in their internal shape, such as soft robots. This thesis will cover these findings and results, simulated and physical, and the surprising practicality of data-driven geometric mechanics.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168033/1/babitt_1.pd

PLANNING FOR AUTOMATED OPTICAL MICROMANIPULATION OF BIOLOGICAL CELLS

Optical tweezers (OT) can be viewed as a robot that uses a highly focused laser beam for precise manipulation of biological objects and dielectric beads at micro-scale. Using holographic optical tweezers (HOT) multiple optical traps can be created to allow several operations in parallel. Moreover, due to the non-contact nature of manipulation OT can be potentially integrated with other manipulation techniques (e.g. microfluidics, acoustics, magnetics etc.) to ensure its high throughput. However, biological manipulation using OT suffers from two serious drawbacks: (1) slow manipulation due to manual operation and (2) severe effects on cell viability due to direct exposure of laser. This dissertation explores the problem of autonomous OT based cell manipulation in the light of addressing the two aforementioned limitations. Microfluidic devices are well suited for the study of biological objects because of their high throughput. Integrating microfluidics with OT provides precise position control as well as high throughput. An automated, physics-aware, planning approach is developed for fast transport of cells in OT assisted microfluidic chambers. The heuristic based planner employs a specific cost function for searching over a novel state-action space representation. The effectiveness of the planning algorithm is demonstrated using both simulation and physical experiments in microfluidic-optical tweezers hybrid manipulation setup. An indirect manipulation approach is developed for preventing cells from high intensity laser. Optically trapped inert microspheres are used for manipulating cells indirectly either by gripping or pushing. A novel planning and control approach is devised to automate the indirect manipulation of cells. The planning algorithm takes the motion constraints of the gripper or pushing formation into account to minimize the manipulation time. Two different types of cells (Saccharomyces cerevisiae and Dictyostelium discoideum) are manipulated to demonstrate the effectiveness of the indirect manipulation approach

Manipulability of Leader-Follower Networks with the Rigid-Link Approximation

© Elsevier. This is the author's version of a work that was accepted for publication in Automatica. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Automatica, Vol. 50, Issue 3, pp. 695-706, March 2014, doi: 10.1016/j.automatica.2013.11.041DOI: 10.1016/j.automatica.2013.11.041This paper introduces the notion of manipulability to mobile, multi-agent networks as a tool to analyze the instantaneous effectiveness of injecting control inputs at certain, so-called leader nodes in the network. Effectiveness is interpreted to characterize how the movements of the leader nodes translate into responses among the remaining follower nodes. This notion of effectiveness is a function of the interaction topologies, the agent configurations, and the particular choice of inputs used to influence the network. In fact, classic manipulability is an index used in robotics to analyze the singularity and efficiency of configurations of robot-arm manipulators. To define similar notions for leader-follower networks, we use a rigid-link approximation of the follower dynamics and, under this assumption, we prove that the instantaneous follower velocities can be uniquely determined from that of the leaders’, which allows us to define a meaningful and computable manipulability index for the leader-follower networks. This paper examines the property of the proposed index in simulation and with real mobile robots, and demonstrates how the index can be used to find effective interaction topologies