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In this video, we present theoretical and practical methods for achieving arbitrary reconfiguration of a set of objects, based on the use of external forces, such as a magnetic field or gravity: Upon actuation, each object is pushed in the same direction. This concept can be used for a wide range of applications in which particles do not have their own energy supply or in which they are subject to the same global control commands. A crucial challenge for achieving any desired target configuration is breaking global symmetry in a controlled fashion. Previous work (some of which was presented during SoCG 2015) made use of specifically placed barriers; however, introducing precisely located obstacles into the workspace is impractical for many scenarios. In this paper, we present a different, less intrusive method: making use of the interplay between static friction with a boundary and the external force to achieve arbitrary reconfiguration. Our key contributions are theoretical characterizations of the critical coefficient of friction that is sufficient for rearranging two particles in triangles, convex polygons, and regular polygons; a method for reconfiguring multiple particles in rectangular workspaces, and deriving practical algorithms for these rearrangements. Hardware experiments show the efficacy of these procedures, demonstrating the usefulness of this novel approach. 

It is now possible to deploy swarms of drones with populations in the thousands. There is growing interest in using such swarms for defense, and it has been natural to program them with bio-mimetic motion models such as flocking or swarming. However, these motion models evolved to survive against predators, not enemies with modern firearms. This paper presents experimental data that compares the survivability of several motion models for large numbers of drones. This project tests drone swarms in Virtual Reality (VR), because it is prohibitively expensive, technically complex, and potentially dangerous to fly a large swarm of drones in a testing environment. We model the behavior of drone swarms flying along parametric paths in both tight and scattered formations. We add random motion to the general motion plan to confound path prediction and targeting. We describe an implementation of these flight paths as game levels in a VR environment. We then allow players to shoot at the drones and evaluate the difference between flocking and swarming behavior on drone survivability. 

This paper investigates motion planning for one or more robot(s) that attempt to harvest agents from a moving swarm. Generating motion paths that maximize the number of agents harvested differs from many traditional coverage problems because the agents move. This movement allows previously cleared areas to become recontaminated. We assume that the swarm agents prefer certain regions over others, and that we can represent the swarm by a Markov Process that encodes the agents' preferred regions and their speed of motion. We exploit this model to design and simulate robotic coverage paths that maximize the number of agents harvested by a fleet of robots in a given time budget. 

Small-scale robots have great potential in medicine, micro-assembly and many other areas. For example, robots containing iron can be steered using the magnetic gradient generated by MRI scanners. Since the gradient is approximately the same everywhere inside the scanner, each robot receives the same input and therefore they all are subjected to the same force. A similar technique can be used with rotating magnetic fields. Each robot receives the same inputs, making motion planning challenging. This paper uses a Rapidly Exploring Random Tree (RRT) to plan paths that deliver multiple robots to goal positions by using obstacles to break the actuation symmetry. 

This paper investigates strategies for 3D multiagent position control using a shared control input and selfpropelled agents. The only control inputs allowed are rotation commands that rotate all agents by the same rotation matrix. In the 2D case, only two degrees-of-freedom (DOF) in position are controllable. We review controllability results in 2D, and then show that interesting things happen in 3D. We provide control laws for steering up to nine DOF in position, which can be mapped in various ways, including to control the x, y, z position of three agents, make four agents meet, or reduce the spread of n agents.