Thomas Apker

Optimizing a reconfigurable robotic microphone array

Relative positioning of microphones in an array has a significant impact on how well the array can localize sound sources. Two- or three- dimensional localization accuracy of a randomly distributed array will vary widely with respect to the relative position of the sound source. Where a statically located array would be forced to work with its initial configuration, however, a reconfigurable robotic array does not have to remain in its starting position. Given a known region of interest, robots can autonomously optimize their relative configuration to improve localization accuracy where it matters the most. In this work, we propose 2 separate strategies for optimizing such a robotic array. Evaluations are completed first in simulation, and then deployed to a team of real robots.

LTL Templates for Play-Calling Supervisory Control

A Playbook allows operators to design sets of tasks for a team of vehicles to perform in an abstract way before the mission begins, and then call the plays much like a coach of a human sports team during the mission. We extend this Playbook concept to include a set of assertions that must be true on each vehicle to ensure successful completion of a play, provide a means of specifying contingency plans, and then translate this extended Playbook into a linear temporal logic specification that can be used to synthesize a correctby-construction controller. We provide a demonstration of this concept and discuss its implications for autonomous systems safety and reliability.

Physicomimetic Motion Control of Physically Constrained Agents

Physicomimetics is a simple and scalable means of controlling multiple agents, provided the agents can perform the maneuvers required by the forces applied to them. For most physical agents, such as wheeled vehicles and fixed-wing aircraft, physical constraints such as motor power and stall speed limit the ability of the agents to respond to physicomimetic inputs. We identified four factors, maximum turn rate, controller time resolution, maximum speed and minimum speed, that must be accounted for in the design of the agent model in order to allow good swarming behavior. To address them, we developed an extended body agent model consisting of two particles, one in front of the vehicle’s rotation center and one behind. This allowed us to explicitly determine the agent’s direction of motion and, combined with nonlinear checks to avoid unachievable commands, allowed us to develop agent models whose behavior was still intuitively controllable and analyzable but which respected the constraints of our physical robots. We also defined a dynamic friction term that penalized speed in cluttered environments and excessive or unstable oscillations to address the fact that under asynchronous distributed control no single set of friction parameters worked in all cases.

Application of Grazing-Inspired Guidance Laws to Autonomous Information Gathering

Domestic grazing animals follow simple, scalable rules to assign themselves trajectories to cover a pasture. We explain how to adapt these rules for an information gathering system based on a realistic robot motion model and Kalman-filter based evidence grid that accounts for both bandwidth and sensor limitations. Our results show that this algorithm can meet or exceed the performance of state of the art field robotics systems, particularly when scalability and robustness to failure are required.

A Physics Inspired Finite State Machine Controller for Mobile Acoustic Arrays

Applying coherent array processing to sound source localization when individual sensors are attached to heterogeneous platforms is a multi-faceted challenge for both perception and mobility. Recent technical advances in robot localization have made such mobile acoustic arrays possible, but the multi-robot coordination problem remains incomplete. How can a team of robots coordinate in cluttered environments, both with each other and static mounted sensors to effectively localize sound sources? This work proposes and implements a physicomimetics based robot control system with solid, liquid, and gas phase finite states. Applying these different phases appropriately enables efficient navigation through clutter and localization of both exposed and buried or hidden sound sources by teams of mobile robots.

Atmospheric Turbulence Measurements in Dynamical Links

Atmospheric turbulence measurements on a dynamical link are essential for sensing and communication applications. Monostatic systems represent a challenge for dynamical configurations. We present experimental data which evaluate such configurations.

Learning to Estimate: A Case-Based Approach to Task Execution Prediction

A system that controls a team of autonomous vehicles should be able to accurately predict the expected outcomes of various subtasks. For example, this may involve estimating how well a vehicle will perform when searching a designated area. We present CBE, a case-based estimation algorithm, and apply it to the task of predicting the performance of autonomous vehicles using simulators of varying fidelity and past performance. Since there are costs to evaluating the performance in simulators (i.e., higher fidelity simulators are more computationally expensive) and in deployment (i.e., potential human injury and deployment expenses), CBE uses a variant of local linear regression to estimate values that cannot be directly evaluated, and incrementally revises its case base. We empirically evaluate CBE on Humanitarian Assistance/Disaster Relief (HA/DR) scenarios and show it to be more accurate than several baselines and more efficient than using a low fidelity simulator.