Browsing by Author "Shahrokhi, Shiva"
Now showing 1 - 5 of 5
- Results Per Page
- Sort Options
Item Controlling a Swarm of Robots Using Global Inputs(2018-08) Shahrokhi, Shiva; Becker, Aaron T.; Kavraki, Lydia; Brankovic, Stanko R.; Mayerich, David; Faghih, Rose T.Microrobotics has the potential to revolutionize many applications, including targeted material delivery, assembly, and surgery. The same properties that promise breakthrough solutions---small size and large populations---present unique challenges for controlling motion. When there are more particles than control inputs, the system is underactuated and requires new control techniques. Rather than focusing on a specific microrobotic system, this dissertation designs control laws and algorithms for steering many particles controlled by global fields. First, we identify key parameters for particle manipulation by using a collection of online games where players steer swarms of up to 500 particles to complete manipulation challenges. Inspired by techniques where human operators performed well, we investigate controllers that only use the mean and variance of the swarm. We next derive automatic controllers for these and a hysteresis-based switching control to regulate the first two moments of the particle distribution. Torque control is also necessary for manipulating objects as well as for aligning sensors, emitters, or redirecting an incoming signal. Second, this dissertation proves that swarm torque control is possible, then presents algorithms to automate the task. Torque control enables us to control the position and orientation of an object. Finally, this dissertation investigates particle control with uniform magnetic gradients (the same force is applied everywhere in the workspace). We provide position control algorithms that only require non-slip wall contact in 2D. The walls of in vivo and artificial environments often have surface roughness such that the particles do not move unless actuation pulls them away from the wall. We assume that particles in contact with the boundaries have zero velocity if the shared control input pushes the particle into the wall. All the results are validated with simulations and hardware implementations.Item Exploiting Nonslip Wall Contacts to Position Two Particles Using the Same Control Input(IEEE Transactions on Robotics, 2/11/2019) Shahrokhi, Shiva; Shi, Jingang; Isichei, Benedict; Becker, Aaron T.Steered particles offer a method for targeted therapy, interventions, and drug delivery in regions inaccessible by large robots. For example, magnetic actuation of particles has the benefits of requiring no tethers, being able to operate from a distance, and in some cases allows imaging for feedback (e.g., MRI). This paper investigates position control of particles using uniform forces (the same force is applied everywhere in the workspace). Given a controllable field that can generate bidirectional forces in three orthogonal directions, steering one particle in three-dimensional (3-D) is trivial. Adding additional particles to steer makes the system underactuated because there are more states than control inputs. However, the walls of in vivo and artificial environments often have surface roughness such that the particles do not move unless actuation pulls them away from the wall. In the previous work, we showed that the individual two-dimensional (2-D) position of two particles is controllable using global inputs in a square workspace with nonslip wall contact [1]. Because in vivo environments are usually not square, this paper extends the previous work to all convex workspaces, and shows how this could be extended to 3-D positioning of neutrally buoyant particles. We investigate analytically an idealized variant of this problem with nonslip boundaries and control inputs that are applied uniformly to all particles in the workspace. This paper also implements the algorithms in 2-D using a hardware setup inspired by the gastrointestinal tract.Item Planar Orientation Control and Torque Maximization Using a Swarm With Global Inputs(IEEE Transactions on Automation Science and Engineering, 7/24/2019) Shahrokhi, Shiva; Lin, Lillian; Becker, Aaron T.This paper studies the torque applied by a large number of particles on a long aspect-ratio rod. The particles are all pushed in the same direction by a global signal. We calculate the force and torque generated by three canonical position distributions of a swarm: uniform, triangular, and normal. The model shows that for a pivoted rod the uniform distribution produces the maximum torque for small swarm standard deviations, but the normal distribution maximizes torque for large standard deviations. In the simulation, we use these results to design proportional-derivative controllers to orient rigid objects. We conclude showing the experiments with up to 97 hardware robots to evaluate our theory in practice.Item Shaping a Swarm Using a Shared Control Input(2018-08) Shahrokhi, Shiva; Becker, Aaron T.; Mayerich, David; Faghih, Rose T.Micro-robots are small enough to move through the passageways of the body, therefore they are suited for targeted drug delivery and micro-scale manufacturing. Due to their small size, a single robot does not have enough force to deliver payloads, and it is prohibitively difficult to have onboard computation. Therefore, these robots are usually controlled by global inputs such as a uniform external magnetic field. This thesis presents controllers and algorithms for steering such an under-actuated swarm. This work first proves that the mean position of the swarm is controllable, and shows how an obstacle can make the variance controllable. Then it derives automatic controllers for these and a hysteresis-based switching control to regulate the first two moments of the swarm distribution. Finally, this work uses friction with boundary walls to break the symmetry caused by the global input and uses it to steer two particles to arbitrary positions.Item Steering a Swarm of Particles Using Global Inputs and Swarm Statistics(IEEE Transactions on Robotics, 12/21/2017) Shahrokhi, Shiva; Lin, Lillian; Ertel, Chris; Wan, Mable; Becker, Aaron T.Microrobotics has the potential to revolutionize many applications including targeted material delivery, assembly, and surgery. The same properties that promise breakthrough solutions-small size and large populations-present unique challenges for controlling motion. Robotic manipulation usually assumes intelligent agents, not particle systems manipulated by a global signal. To identify the key parameters for particle manipulation, we used a collection of online games in which players steer swarms of up to 500 particles to complete manipulation challenges. We recorded statistics from more than 10 000 players. Inspired by techniques in which human operators performed well, we investigate controllers that use only the mean and variance of the swarm. We prove that mean position is controllable and provide conditions under which variance is controllable. We next derive automatic controllers for these and a hysteresis-based switching control to regulate the first two moments of the particle distribution. Finally, we employ these controllers as primitives for an object manipulation task and implement all controllers on 100 kilobots controlled by the direction of a global light source.