Person:

Egerstedt, Magnus B.

Permanent Link

https://hdl.handle.net/1853/72204

Associated Organization(s)

Organizational Unit

School of Electrical and Computer Engineering

Full item page

Publication Search Results

Now showing 1 - 10 of 12

Power-aware hybrid-dynamical approach to coverage control in multi-robot systems

(Georgia Institute of Technology, 2018-04-24) Olsen, Mark Ryan

This thesis develops an algorithm which allows robots in a multi-robot team to optimize for battery power while performing coverage control so as to maximize the mission life of the multi-robot team. We envision a scenario where robots with limited battery supply are executing the well known Lloyd's algorithm in order to effectively cover a certain region. We perform a trade-off between the distance of a robot from the centroid of its Voronoi cell, and the energy required to traverse that distance. In order to execute this trade-off two different strategies are presented -- in one case, the reduction in cost due to coverage is compared against the energy required to traverse the distance to the centroid, and using a user-defined threshold, the decision is made. Then, a more sophisticated algorithm is used to perform the trade-off where the robots solves a switch-time optimization problem to decide whether it should move or it should stay.
Assignment and pursuit in temporally heterogeneous robotic teams

(Georgia Institute of Technology, 2016-08-01) Oei, Marius Florian Bruno

Traditionally, robotic systems are built to move as fast as possible. In contrast to this, we investigate slowness and its effects on heterogeneous robotic teams inspired by biological systems. An assignment problem for static targets and a team pursuit problem for heterogeneous evaders are addressed. The value of slowness in solving these problems optimally is examined. We further assemble the optimal teams for given problems by finding a compromise between performance and energy consumption or monetary cost. The results are validated in simulation and implemented on a robotic testbed.
Safe open-loop strategies for handling intermittent communications in multi-robot systems

(Georgia Institute of Technology, 2016-05-03) Mayya, Siddharth

The objective of this thesis is to develop a strategy that allows robots to safely execute open-loop motion patterns for pre-computed time durations when facing interruptions in communication. By computing the time horizon in which collisions with other robots are impossible, this method allows the robots to move safely despite having no updated information about the environment. As the complexity of multi-robot systems increase, communication failures in the form of packet losses, saturated network channels and hardware failures are inevitable. This thesis is motivated by the need to increase the robustness of operation in the face of such failures. The advantage of this strategy is that it prevents the jerky and unpredictable motion behaviour which often plague robotic systems experiencing communication issues. To compute the safe time horizon, the first step involves constructing reachable sets around the robots to determine the set of all positions that can be reached by the robot in a given amount of time. In order to avoid complications arising from the non-convexity of these reachable sets, analytical expressions for minimum area ellipses enclosing the reachable sets are obtained. By using a fast gradient descent based technique, intersections are computed between a robot’s trajectory and the reachable sets of other robots. This information is then used to compute the safe time horizon for each robot in real time. To this end, provable safety guarantees are formulated to ensure collision avoidance. This strategy has been verified in simulation as well as on a team of two-wheeled differential drive robots on a multi-robot testbed.
Musical abstractions for multi-robot coordination

(Georgia Institute of Technology, 2016-04-29) Santos Fernandez, Maria Teresa

This work presents a new approach to human-swarm interactions, a discipline which addresses the problem of how a human operator can influence the behavior of large groups of robots, providing high-level information understandable by the team. While there exist potential advantages of introducing a human in the control loop of a robot swarm, how the human must be incorporated is not a simple problem. For the intervention of a human operator to be favorable to the performance of the team, the means and form of the information between the human and the robot swarm must be adequately defined: we need to design which device will be provided to the operator to interact with the swarm and how the information will be shaped so that both the human and the robot team understand it. Coordination of multi-robot systems involves the generation of involved motion patterns for the individual agents that result in an overall organized movement. We introduce in this thesis a new human-swarm interaction modality based on music theory, a discipline studied for centuries and capable of creating complex sound structures. In particular, we have focused on understanding how we can apply rules and structures from music theory to an operator's input so that each command both specifies the goal location to be visited and the geometry to be adopted by the swarm. We interpret the sequence of locations to be visited by the swarm as a musical melody, identifying each note with a certain location in the robots' workspace. Once the objective path is defined in the form of a melody, we can apply rules from harmony, a discipline of music theory, to create chords that harmonize the input melody. The interest in using these chords lies fundamentally in that they are structured combinations of pitches, heard simultaneously. These inherent structures will be used to determine the geometry that should be displayed by the team. The developed multi-robot control is applied to a team of differential drive mobile robots through an electronic piano.
Optimal behavior composition for robotics

(Georgia Institute of Technology, 2014-04-02) Bartholomew, Paul D.

The development of a humanoid robot that mimics human motion requires extensive programming as well as understanding the motion limitations of the robot. Programming the countless possibilities for a robot’s response to observed human motion can be time consuming. To simplify this process, this thesis presents a new approach for mimicking captured human motion data through the development of a composition routine. This routine is built upon a behavior-based framework and is coupled with optimization by calculus to determine the appropriate weightings of predetermined motion behaviors. The completion of this thesis helps to fill a void in human/robot interactions involving mimicry and behavior-based design. Technological advancements in the way computers and robots identify human motion and determine for themselves how to approximate that motion have helped make possible the mimicry of observed human subjects. In fact, many researchers have developed humanoid systems that are capable of mimicking human motion data; however, these systems do not use behavior-based design. This thesis will explain the framework and theory behind our optimal behavior composition algorithm and the selection of sinusoidal motion primitives that make up a behavior library. This algorithm breaks captured motion data into various time intervals, then optimally weights the defined behaviors to best approximate the captured data. Since this routine does not reference previous or following motion sequences, discontinuities may exist between time intervals. To address this issue, the addition of a PI controller to regulate and smooth out the transitions between time intervals will be shown. The effectiveness of using the optimal behavior composition algorithm to create an approximated motion that mimics capture motion data will be demonstrated through an example configuration of hardware and a humanoid robot platform. An example of arm motion mimicry will be presented and includes various image sequences from the mimicry as well as trajectories containing the joint positions for both the human and the robot.
Electric vehicle-intelligent energy management system for frequency regulation application using a distributed, prosumer-based grid control architecture

(Georgia Institute of Technology, 2013-04-12) Sandoval, Marcelo

The world faces the unprecedented challenge of the need change to a new energy era. The introduction of distributed renewable energy and storage together with transportation electrification and deployment of electric and hybrid vehicles, allows traditional consumers to not only consume, but also to produce, or store energy. The active participation of these so called "prosumers", and their interactions may have a significant impact on the operations of the emerging smart grid. However, how these capabilities should be integrated with the overall system operation is unclear. Intelligent energy management systems give users the insight they need to make informed decisions about energy consumption. Properly implemented, intelligent energy management systems can help cut energy use, spending, and emissions. This thesis aims to develop a consumer point of view, user-friendly, intelligent energy management system that enables vehicle drivers to plan their trips, manage their battery pack and under specific circumstances, inject electricity from their plug-in vehicles to power the grid, contributing to frequency regulation.
Multi-robot platooning in hostile environments

(Georgia Institute of Technology, 2012-04-09) Shively, Jeremy

The purpose of this thesis is to develop a testing environment for mobile robot experiments, to examine methods for multi-robot platooning through hostile environments, and test these algorithms on mobile robots. Such a system will allow us to rapidly address and test problems that arise concerning robot swarms and consequent interactions. In order to create this hardware simulation environment a test bed will be created using ROS or Robot Operating System. This platform is highly modular and extensible for future development. Trajectory generation for the robots will use smoothing splines, B-splines, and A* search. Each method has distinct properties which will be analyzed and rated with respect to its effectiveness with regards to robotic platooning. A few issues to be considered include: Is the optimal path taken with respect to distance and threats? Is the formation of the robots maintained or compromised during traversal of the path? And finally, what sorts of compromises or additions are needed to make each method effective? This work will be helpful for choosing route planning methods in future work and will provide a large code base for rapid prototyping.
Generation and use of a discrete robotic controls alphabet for high-level tasks

(Georgia Institute of Technology, 2012-04-06) Gargas , Eugene Frank, III

The objective of this thesis is to generate a discrete alphabet of low-level robotic controllers rich enough to mimic the actions of high-level users using the robot for a specific task. This alphabet will be built through the analysis of various user data sets in a modified version of the motion description language, MDLe. It can then be used to mimic the actions of a future user attempting to perform the task by calling scaled versions of the controls in the alphabet, potentially reducing the amount of data required to be transmitted to the robot, with minimal error. In this thesis, theory is developed that will allow the construction of such an alphabet, as well as its use to mimic new actions. A MATLAB algorithm is then built to implement the theory. This is followed by an experiment in which various users drive a Khepera robot through different courses with a joystick. The thesis concludes by presenting results which suggest that a relatively small group of users can generate an alphabet capable of mimicking the actions of other users, while drastically reducing bandwidth.
3D reconfiguration using graph grammars for modular robotics

(Georgia Institute of Technology, 2011-12-16) Pickem, Daniel

The objective of this thesis is to develop a method for the reconfiguration of three-dimensional modular robots. A modular robot is composed of simple individual building blocks or modules. Each of these modules needs to be controlled and actuated individually in order to make the robot perform useful tasks. The presented method allows us to reconfigure arbitrary initial configurations of modules into any pre-specified target configuration by using graph grammar rules that rely on local information only. Local in a sense that each module needs just information from neighboring modules in order to decide its next reconfiguration step. The advantage of this approach is that the modules do not need global knowledge about the whole configuration. We propose a two stage reconfiguration process composed of a centralized planning stage and a decentralized, rule-based reconfiguration stage. In the first stage, paths are planned for each module and then rewritten into a ruleset, also called a graph grammar. Global knowledge about the configuration is available to the planner. In stage two, these rules are applied in a decentralized fashion by each node individually and with local knowledge only. Each module can check the ruleset for applicable rules in parallel. This approach has been implemented in Matlab and currently, we are able to generate rulesets for arbitrary homogeneous input configurations.
Multi-robot assignment and formation control

(Georgia Institute of Technology, 2011-07-08) Macdonald, Edward A.

Our research focuses on one of the more fundamental issues in multi-agent, mobile robotics: the formation control problem. The idea is to create controllers that cause robots to move into a predefined formation shape. This is a well studied problem for the scenario in which the robots know in advance to which point in the formation they are assigned. In our case, we assume this information is not given in advance, but must be determined dynamically. This thesis presents an algorithm that can be used by a network of mobile robots to simultaneously determine efficient robot assignments and formation pose for rotationally and translationally invariant formations. This allows simultaneous role assignment and formation sysnthesis without the need for additional control laws. The thesis begins by introducing some general concepts regarding multi-agent robotics. Next, previous work and background information specific to the formation control and assignment problems are reviewed. Then the proposed assignment al- gorithm for role assignment and formation control is introduced and its theoretical properties are examined. This is followed by a discussion of simulation results. Lastly, experimental results are presented based on the implementation of the assignment al- gorithm on actual robots.