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The cost, in terms of both time and money, of instantiating a physical testbed can be prohibitive. To help resolve this issue, the Robotarium offers a free, remotely accessible robotics lab to users around the world. Since allowing the general public to use it, hundreds of users have submitted thousands of experiments. The current and accelerating experiment submission rate poses an operational challenge that cannot be handled through manual or human supervised execution without devoting a full time operator to the platform. A solution to this problem is enable the Robotarium to operate autonomously: improving the robustness and reliability of the system while reducing required human intervention to diagnose and recover from failures. In this pursuit, the hardware, software, and algorithms deployed on the Robotarium have undergone numerous developments, including a new differential-drive robot, the use of modern virtualization techniques for the software infrastructure, and the inclusion of robust constraint-satisfaction methods for long-term safe operation. Over the past year of autonomous operation, these advances have resulted in 0.76% of the 3402 submitted remote experiments failing and requiring human intervention to recover from. This paper details these development efforts and best practices that have been learned automating a remote-access testbed to keep up with the experimental demand of a large, active, and growing userbase.

This paper explores the expressive capabilities of a swarm of miniature mobile robots within the context of inter-robot interactions and their mapping to the so-called fundamental emotions. In particular, we investigate how motion and shape descriptors that are psychologically associated with different emotions can be incorporated into different swarm behaviors for the purpose of artistic expositions. Based on these characterizations from social psychology, a set of swarm behaviors is created, where each behavior corresponds to a fundamental emotion. The effectiveness of these behaviors is evaluated in a survey in which the participants are asked to associate different swarm behaviors with the fundamental emotions. The results of the survey show that most of the research participants assigned to each video the emotion intended to be portrayed by design. These results confirm that abstract descriptors associated with the different fundamental emotions in social psychology provide useful motion characterizations that can be effectively transformed into expressive behaviors for a swarm of simple ground mobile robots., 13 pages, 17 figures

Collisions may be harnessed as a way to improve the overall safety and navigational effectiveness of some spacecraft. However, leveraging this capability in autonomous platforms requires the abilit...

Distributed algorithms have been proposed as options to scale control propositions to the massive number of intelligent energy devices, sub-systems, and distributed energy resources being integrated into the electricity grid. Distributed algorithms rely on the communication network for exchanging information. Failures in the communication network can jeopardize distributed decision-making and in the worst-case scenario can lead to system-level stability problems. This paper proposes a communication-failure-resilient architecture for distributed operation and control in smart grids with hybrid producer/consumer (prosumer) agents. We describe the relations between system-wide performance and communication failures and identify topological conditions on the cyber-physical network, under which prosumers can perform key operating tasks, such as distributed frequency regulation through an imperfect communication network.

Distributed control has emerged as a major focus in the systems and controls area, with multiagent robotics playing a prominent role both as a canonical instantiation of a system, where control decisions must be made by individual nodes across an information-exchange network, and as a rich source of applications [1]-[5]. These applications include environmental monitoring [6], collective materials handling [7], construction [8], disaster response [9], and precision agriculture [10].

Many applications involving complex multi-task problems such as disaster relief, logistics and manufacturing necessitate the deployment and coordination of heterogeneous multi-agent systems due to the sheer number of tasks that must be executed simultaneously. A fundamental requirement for the successful coordination of such systems is leveraging the specialization of each agent within the team. This work presents a Receding Horizon Planning (RHP) framework aimed at scheduling tasks for heterogeneous multi-agent teams in a robust manner. In order to allow for the modular addition and removal of different types of agents to the team, the proposed framework accounts for the capabilities that each agent exhibits (e.g. quadrotors are agile and agnostic to rough terrain but are not suited to transport heavy payloads). An instantiation of the proposed RHP is developed and tested for a search and rescue scenario. Moreover, we present an abstracted search and rescue simulation environment, where a heterogeneous team of agents is deployed to simultaneously explore the environment, find and rescue trapped victims, and extinguish spreading fires as quickly as possible. We validate the effectiveness of our approach through extensive simulations comparing the presented framework with various planning horizons to a greedy task allocation scheme., Accepted to IFAC World Congress 2020

This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique. We use the learned model in combination with control barrier certificates which constrain policies (feedback controllers) in order to maintain safety, which refers to avoiding particular undesirable regions of the state space. Under certain conditions, recovery of safety in the sense of Lyapunov stability after violations of safety due to the nonstationarity is guaranteed. In addition, we reformulate an action-value function approximation to make any kernel-based nonlinear function estimation method applicable to our adaptive learning framework. Lastly, solutions to the barrier-certified policy optimization are guaranteed to be globally optimal, ensuring the greedy policy improvement under mild conditions. The resulting framework is validated via simulations of a quadrotor, which has previously been used under stationarity assumptions in the safe learnings literature, and is then tested on a real robot, the brushbot, whose dynamics is unknown, highly complex and nonstationary., \copyright 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

In this paper we discuss how to use a barrier function that is subject to kinematic constraints and limited sensing in order to guarantee that fixed wing unmanned aerial vehicles (UAVs) will maintain safe distances from each other at all times despite being subject to limited range sensing constraints. Prior work has shown that a barrier function can be used to guarantee safe system operation when the state can be sensed at all times. However, in this paper we show that this construction does not guarantee safety when the UAVs are subject to limited range sensing. To resolve this issue, we introduce a method for constructing a new barrier function that accommodates limited sensing range from a previously existing barrier function that may not necessarily accommodate limited range sensing. We show that, under appropriate conditions, the newly constructed barrier function ensures system safety even in the presence of limited range sensing. We demonstrate the contribution of this paper in a scenario of 20 fixed wing aircraft, where because of the proposed algorithm, the vehicles are able to maintain safe distances from each other even though the vehicles are subject to limited range sensing., \c{opyright} 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

In this paper, we present a robotic painting system whereby a team of mobile robots equipped with different color paints create pictorial compositions by leaving trails of color as they move throughout a canvas. We envision this system to be used by an external user who can control the concentration of different colors over the painting by specifying density maps associated with the desired colors over the painting domain, which may vary over time. The robots distribute themselves according to such color densities by means of a heterogeneous distributed coverage control paradigm, whereby only those robots equipped with the appropriate paint will track the corresponding color density function. The painting composition therefore arises as the integration of the motion trajectories of the robots, which lay paint as they move throughout the canvas tracking the color density functions. The proposed interactive painting system is evaluated on a team of mobile robots with different experimental setups in terms of paints given to the robot team to highlight the effects and benefits of considering heterogeneous teams when there is a limited availability of painting resources.

In this paper, we consider a two-player racing game, where an autonomous ego vehicle has to be controlled to race against an opponent vehicle, which is either autonomous or human-driven. The approach to control the ego vehicle is based on a Sensitivity-ENhanced NAsh equilibrium seeking (SENNA) method, which uses an iterated best response algorithm in order to optimize for a trajectory in a two-car racing game. This method exploits the interactions between the ego and the opponent vehicle that take place through a collision avoidance constraint. This game-theoretic control method hinges on the ego vehicle having an accurate model and correct knowledge of the state of the opponent vehicle. However, when an accurate model for the opponent vehicle is not available, or the estimation of its state is corrupted by noise, the performance of the approach might be compromised. For this reason, we augment the SENNA algorithm by enforcing Permissive RObust SafeTy (PROST) conditions using control barrier functions. The objective is to successfully overtake or to remain in the front of the opponent vehicle, even when the information about the latter is not fully available. The successful synergy between SENNA and PROST—antithetical to the notable rivalry between the two namesake Formula 1 drivers—is demonstrated through extensive simulated experiments.

We present an optimization framework for solving multiagent convex programs subject to inequality constraints while keeping the agents’ state trajectories private. Each agent has an objective function depending only upon its own state and the agents are collectively subject to global constraints. The agents do not directly communicate with each other but instead route messages through a trusted cloud computer. The cloud adds noise to data being sent to the agents in accordance with the framework of differential privacy and, thus, keeps each agent's state trajectory private from all other agents and any eavesdroppers. This private problem can be viewed as a stochastic variational inequality, and it is solved using a projection-based method for solving variational inequalities that resemble a noisy primal-dual gradient algorithm. Convergence of the optimization algorithm in the presence of noise is proven, and a quantifiable tradeoff between privacy and convergence is extracted from this proof. Simulation results are provided that demonstrate numerical convergence for both $\epsilon$ -differential privacy and $(\epsilon, \delta)$ -differential privacy.

We present a framework for asynchronously solving convex optimization problems over networks of agents which are augmented by the presence of a centralized cloud computer. This framework uses a Tikhonov-regularized primal-dual approach in which the agents update the system's primal variables and the cloud updates its dual variables. To minimize coordination requirements placed upon the system, the times of communications and computations among the agents are allowed to be arbitrary, provided they satisfy mild conditions. Communications from the agents to the cloud are likewise carried out without any coordination in their timing. However, we require that the cloud keeps the dual variable's value synchronized across the agents, and a counterexample is provided that demonstrates that this level of synchrony is indeed necessary for convergence. Convergence rate estimates are provided in both the primal and dual spaces, and simulation results are presented that demonstrate the operation and convergence of the proposed algorithm.

This paper discusses the problem of making multiple robots coordinate their movements subject to energy constraints imposed by their available battery levels. This problem is instantiated in the context of energy-aware rendezvous, whereby a collection of mobile robots must decide where and when to meet, with the objective of doing so in the least amount of time, given varying available initial battery levels. As the movements of the individual robots directly affect their energy consumption, the robots must coordinate their movements in such a way that they do not run out of energy prior to the completion of the task. This problem is formulated as a constrained optimization problem, where the constraints themselves result from a lower-level optimal control problem that ensures that all of the robots have nonnegative battery levels when they meet. The energy-constrained coordination problem is solved in a distributed manner and is implemented on a team of differential-drive mobile robots.

This paper presents safety barrier certificates that ensure scalable and provably collision-free behaviors in multirobot systems by modifying the nominal controllers to formally satisfy safety constraints. This is achieved by minimizing the difference between the actual and the nominal controllers subject to safety constraints. The resulting computation of the safety controllers is done through a quadratic programming problem that can be solved in real-time and in this paper, we describe a series of problems of increasing complexity. Starting with a centralized formulation, where the safety controller is computed across all agents simultaneously, we show how one can achieve a natural decentralization whereby individual robots only have to remain safe relative to nearby robots. Conservativeness and existence of solutions as well as deadlock-avoidance are then addressed using a mixture of relaxed control barrier functions, hybrid braking controllers, and consistent perturbations. The resulting control strategy is verified experimentally on a collection of wheeled mobile robots whose nominal controllers are explicitly designed to make the robots collide.

Multi-robot task allocation is a ubiquitous problem in robotics due to its applicability in a variety of scenarios. Adaptive task-allocation algorithms account for unknown disturbances and unpredicted phenomena in the environment where robots are deployed to execute tasks. However, this adaptivity typically comes at the cost of requiring precise knowledge of robot models in order to evaluate the allocation effectiveness and to adjust the task assignment online. As such, environmental disturbances can significantly degrade the accuracy of the models which in turn negatively affects the quality of the task allocation. In this paper, we leverage Gaussian processes, differential inclusions, and robust control barrier functions to learn environmental disturbances in order to guarantee robust task execution. We show the implementation and the effectiveness of the proposed framework on a real multi-robot system., Comment: Submitted to the 2021 IEEE International Conference on Robotics and Automation (ICRA)

Robotic systems often need to consider multiple tasks concurrently. This challenge calls for controller synthesis algorithms that fulfill multiple control specifications while maintaining the stability of the overall system. In this paper, we decompose multi-objective tasks into subtasks, where individual subtask controllers are designed independently and then combined to generate the overall control policy. In particular, we adopt Riemannian Motion Policies (RMPs), a recently proposed controller structure in robotics, and, RMPflow, its associated computational framework for combining RMP controllers. We re-establish and extend the stability results of RMPflow through a rigorous Control Lyapunov Function (CLF) treatment. We then show that RMPflow can stably combine individually designed subtask controllers that satisfy certain CLF constraints. This new insight leads to an efficient CLF-based computational framework to generate stable controllers that consider all the subtasks simultaneously. Compared with the original usage of RMPflow, our framework provides users the flexibility to incorporate design heuristics through nominal controllers for the subtasks. We validate the proposed computational framework through numerical simulation and robotic implementation., The 58th IEEE Conference on Decision and Control (CDC), 2019

Due to the inherently distributed and heterogeneous nature of the microgrids, distributed control can be a promising approach to improve the stability, reliability, and scalability of the microgrids compared with centralized control strategies. This paper studies the distributed reactive power sharing problem for a microgrid with connected ac inverters. Under the standard decoupling approximation for bus angle differences, the reactive power flow of each inverter is dependent on the voltage amplitudes of its neighboring inverters connected by electrical power lines. Using the Lyapunov approach, a novel distributed voltage controller with nonlinear state feedback is proposed for reactive power sharing of the microgrid. It is proved that the inverters can achieve accurate reactive power sharing under the proposed controller if the communication network of inverters is connected. Then, by introducing the sampling and holding scheme, we extend the proposed controller to the nonlinear state feedback control with event-triggered communication among inverters. The new event-triggered control approach can dramatically reduce the amount of communication of the microgrid, and significantly relax the requirement for precise real-time information transmission among the inverters. Both the proposed controllers are validated by simulations on a group of inverters with time-varying loads.

The Robotarium, a remotely accessible swarm-robotics testbed, has provided free, open access to robotics and controls research for hundreds of users in thousands of experiments. This high level of usage requires autonomy in the system, which mainly corresponds to constraint satisfaction in the context of users' submissions. In other words, in case that the users' inputs to the robots may lead to collisions, these inputs must be altered to avoid these collisions automatically. However, these alterations must be minimal so as to preserve the users' objective in the experiment. Toward this end, the system has utilized barrier functions, which admit a minimally invasive controller-synthesis procedure. However, barrier functions are yet to be robustified with respect to unmodeled disturbances (e.g., wheel slip or packet loss) in a manner conducive to real-time synthesis. As such, this paper formulates robust barrier functions for a general class of disturbed control-affine systems that, in turn, is key for the Robotarium to operate fully autonomously (i.e., without human supervision). Experimental results showcase the effectiveness of this robust formulation in a long-term experiment in the Robotarium., Comment: Submitted and accepted for the 58th IEEE Conference on Decision and Control (CDC)

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Cyber-Physical Systems on 17/04/2014, available online:http://dx.doi.org/10.1080/23335777.2015.1056234

This paper presents an algorithm for learning the switching policy and the boundaries conditions between primitive controllers that maximize the translational movements of a complex locomoting system. The algorithm learns an optimal action for each boundary condition instead of one for each discretized state-action pair of the system, as is typically done in machine learning. The system is modeled as a hybrid system because it contains both discrete and continuous dynamics. With this hybridification of the system and with this abstraction of learning boundary-action pairs, the “curse of dimensionality” is mitigated. The effectiveness of this learning algorithm is demonstrated on both a simulated system and on a physical robotic system. In both cases, the algorithm is able to learn the hybrid control strategy that maximizes the forward translational movement of the system without the need for human involvement.

Multi-agent robotics involves the coordination of large numbers of robots, which leads to significant challenges in terms of collision avoidance. This paper generates provably collision free swarm behaviours by constructing swarm safety control barrier certificates. The safety barrier, implemented via an optimization-based controller, serves as a low level safety controller formally ensuring the forward invariance of the safe operating set. In addition, the proposed method naturally combines the goals of collision avoidance and interference with the coordination laws in a unified and computationally efficient manner. The centralized version of safety barrier certificate is designed on double integrator dynamic model, and then a decentralized formulation is proposed as a less computationally intensive and more scalable solution. The safety barrier certificate is validated in simulation and implemented experimentally on multiple mobile robots; the proposed optimization-based controller successfully generates collision free control commands with minimal overall impact on the coordination control laws.

This paper presents experimental results on novel robot testbeds that allow the evaluation of the simultaneous implementation of adaptive speed regulation and lane keeping in a safe, education-centric, and inexpensive manner. The underlying algorithms are based on a control Lyapunov function for performance, a control barrier function for safety, and a realtime quadratic program for mediating the conflicting demands of performance and safety. The Robotarium used for this work allows students, as well as researchers less experienced with hardware, to experiment with advanced control concepts in a safe and standardized environment.

Safety Barrier Certificates that ensure collision-free maneuvers for teams of differential flatness-based quadrotors are presented in this paper. Synthesized with control barrier functions, the certificates are used to modify the nominal trajectory in a minimally invasive way to avoid collisions. The proposed collision avoidance strategy complements existing flight control and planning algorithms by providing trajectory modifications with provable safety guarantees. The effectiveness of this strategy is supported both by the theoretical results and experimental validation on a team of five quadrotors., To appear in ICRA 2017, 8 page version

In this work, we incorporate trust into the interaction dynamics for multi-agent systems in order to analyze the effects of trust on human-robot teams. We extend previous work by moving away from a 'self-centered' trust model and introduce a so-called 'team-oriented' trust model in which each agent increases its trust for its neighbors only if they are collectively contributing to the team's overall goal. The coupling of the trust dynamics and the agents' state dynamics is shown to give rise to an intricate relationship that has the potential to make the team's performance deteriorate under certain circumstances. We derive conditions under which the multi-agent system is guaranteed to achieve a collective objective and provide simulations to corroborate the theoretical findings.

In this paper, we propose a distributed frequency regulation framework for prosumer-based electric energy systems, where a prosumer (producer-consumer) is defined as an intelligent agent which can produce, consume, and/or store electricity. Despite the frequency regulators being distributed, stability can be ensured while avoiding inter-area oscillations using a limited control effort. To achieve this, a fully distributed one-step model-predictive control protocol is proposed and analyzed, whereby each prosumer communicates solely with its neighbors in the network. The efficacy of the proposed frequency regulation framework is shown through simulations on two real-world electric energy systems of different scale and complexity. We show that prosumers can indeed bring frequency and power deviations to their desired values after small perturbations.

In this paper, we explore the relationship between dual decomposition and the consensus-based method for distributed optimisation. The relationship is developed by examining the similarities between the two approaches and their relationship to gradient-based constrained optimisation. By formulating each algorithm in continuous-time, it is seen that both approaches use a gradient method for optimisation with one using a proportional control term and the other using an integral control term to drive the system to the constraint set. Therefore, a significant contribution of this paper is to combine these methods to develop a continuous-time proportional-integral distributed optimisation method. Furthermore, we establish convergence using Lyapunov stability techniques and utilising properties from the network structure of the multi-agent system.

This 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.

During the spring of 2013, I taught a massive open online course (MOOC), Control of Mobile Robots, to over 40,000 students worldwide. Online resources for higher education have received significant attention during the last year, but upper-level engineering classes have been virtually absent from all the major MOOC content providers(Udacity, Coursera, edX).

This paper presents a new method for injecting human inputs into mixed-initiative interactions between humans and robots. The method is based on a model-predictive control (MPC) formulation, which inevitably involves predicting the system (robot dynamics as well as human input) into the future. These predictions are complicated by the fact that the human is interacting with the robot, causing the prediction method itself to have an effect on future human inputs. We investigate and develop different prediction schemes, including fixed and variable horizon MPCs and human input estimators of different orders. Through a search-and-rescue-inspired human operator study, we arrive at the conclusion that the simplest prediction methods outperform the more complex ones, i.e., in this particular case, less is indeed more.

This paper investigates the problem of how to form coalitions in teams of heterogeneous vehicles. In particular, a coordination strategy is designed for unmanned vehicles to autonomously carry out the suppression of enemy air defenses (SEAD) mission, which would benefit from a heterogeneous network of unmanned vehicles to search and destroy threats in an unexplored area. Inspiration for this work is drawn from natural systems, and we consider the alliance-forming behavior of bottlenose dolphins as a guiding example. A two-phased approach is taken to develop the bioinspired strategy. First, in the context of multi-agent systems, a mathematical model is produced that expressively captures the alliance-forming behavior. Next, this model is tailored to the suppression of enemy air defenses mission: the target application. Advantages of using this bioinspired approach are discussed and simulations are provided to demonstrate its operation.

We study bipartite, first-order networks where the nodes take on leader or follower roles. Specifically, we let the leaders' positions be static and assume that leaders and followers communicate via an undirected switching graph topology. This assumption is inspired by the swarming behavior of silkworm moths, where female moths intermittently release pheromones to be detected by the males. The main result presented here states that if the followers execute the linear agreement protocol, they will converge to the convex hull spanned by the leaders' positions as long as the time-varying undirected graph defining the communication among all agents is jointly connected. The novelty of this research is that we use LaSalle's Invariance Principle for switched systems, and additionally, the result is shown to hold for arbitrary state dimensions.

The design of power-aware lifetime maximization algorithms for sensor networks is an active area of research. However, the standard assumption is that the performance of the sensors remains the same throughout the network's lifetime, which is not always true. In this paper, we study the effects of power decay on the performance of individual sensors as well as of the entire network. In particular, we examine networks with decaying footprints, akin to those of RF or radar-based sensors and relate the performance of a sensor to its available power. Moreover, we propose probabilistic scheduling controllers that compensate for the effects of the decrease in power while maintaining an adequate probability of event detection under two sensing models; Boolean and non-Boolean. We simulate the performance of the proposed controllers to establish that the desired performance levels are indeed maintained throughout the lifetime of the network.

In this paper, we present a novel approach for heterogeneous self-reconfiguration of a modular robot comprised of heterogeneous cubic modules. We allow an arbitrary number of modules and module classes and show that the proposed self-reconfiguration algorithm can guarantee completion of heterogeneous self-reconfigurations by avoiding so-called hole obstructions. We introduce a hole-detection algorithm to avoid creating holes in connected sets of modules (furthermore called configuration) and an assignment resolution algorithm that prevents deadlocks. Using these algorithms, we show that this approach yields provably successful reconfiguration sequences from any heterogeneous initial configuration to any heterogeneous target configuration as long as the initial and the target configuration are hole and enclosure-free.

A new approach for controlling a system of multiple agents by choosing a time-varying density function is presented, employing optimal coverage ideas. In this approach, we specify a time-varying density function that represents where it is that want the agents to monitor, and how important it is for each point to be covered. A new algorithm is presented under which the agents track the time-varying density function while providing optimal coverage of the density function. Results from robot implementation show that the proposed algorithm guides the agents well over the chosen density functions, and that the effectiveness of the coverage is higher than other comparable algorithms.

In this work, we study the problem of keeping the objective functions of individual agents ∊-differentially private in cloud-based distributed optimization, where agents are subject to global constraints and seek to minimize local objective functions. The communication architecture between agents is cloud-based - instead of communicating directly with each other, they coordinate by sharing states through a trusted cloud computer. In this problem, the difficulty is twofold: the objective functions are used repeatedly in every iteration, and the influence of perturbing them extends to other agents and lasts over time. To solve the problem, we analyze the propagation of perturbations on objective functions over time, and derive an upper bound on them. With the upper bound, we design a noise-adding mechanism that randomizes the cloud-based distributed optimization algorithm to keep the individual objective functions ∊-differentially private. In addition, we study the trade-off between the privacy of objective functions and the performance of the new cloud-based distributed optimization algorithm with noise. We present simulation results to numerically verify the theoretical results presented.

This paper describes the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-robot research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium and discusses the considerations one must take when making complex hardware remotely accessible. In particular, safety must be built into the system already at the design phase without overly constraining what coordinated control programs users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees., 8 pages, 5 figures, 21 references. arXiv admin note: text overlap with arXiv:1604.00640

This paper proposes a resilient architecture for distributed frequency regulation (DFR) during communication failures. The DFR algorithm relies on the communication network for exchanging information between prosumers and obtaining stabilizing control actions, which can bring overall frequency deviations to nominal. Failures in the communication network can jeopardize the functionality of DFR and lead to frequency stability problems. This paper explores the connection between system-wide frequency stability and communication failures and identifies certain topological conditions on the cyber-physical network itself, under which DFR can restore frequency stability through a stressed communication network. The proposed architecture is simulated on a practical power grid and the results indicate that it is indeed resilient under the single communication failure scenario.

Due to requirements of renewable and distributed energy integration,sustainability and energy security, the existing power grid is undergoing radical changes.The lines between producers and consumers are becoming blurred. In this paper, we provide a constrained formulation of the power allocation problem, which is emerging in such producer-consumer hybrid environments, and obtain decentralized protocols for its solution. We show that the information required by the individual prosumers to solve the problem varies depending on the choice of constraints in the optimization problem. As a consequence of this, we demonstrate that there exist fundamental trade-offs between the information required and the convergence rates associated with the power allocation problem.

The Federal Aviation Administration’s NextGen program aims to increase the capacity of the national airspace, while ensuring the safety of aircraft. This paper provides a distributed merging and spacing algorithm that maximizes the throughput at the terminal phase of flight, using information communicated between neighboring aircraft through the ADS-B framework. Aircraft belonging to a mixed fleet negotiate with each other and use dual decomposition to reach an agreement on optimal merging times, with respect to a pairwise cost, while ensuring proper interaircraft spacing for the respective aircraft types. A set of sufficient conditions on the geometry and operating conditions of merging forks is provided to identify when proper interaircraft spacing can always be achieved using the proposed algorithm for any combination of merging aircraft. Also, optimal decentralized controllers are derived for merging air traffic when operating under such conditions. The performance of the presented algorithm is verified through computer simulations.

In this paper, we consider the problem of routing multiple robots to service spatially distributed requests at specified time instants. As a motivating example, we present the Robot Music Wall , a musically instrumented surface where planar positions correspond to distinct notes of an instrument. Multiple robots with the ability to traverse the wall can effectively “play” a piece of music by reaching positions on the wall that correspond to the musical notes in the piece, at specified time instants. We show that the multi-robot routing problem for servicing such spatio-temporal requests can be formulated as a pure assignment problem with the resulting reduction in complexity. Moreover, we derive the minimum number of robots required to service such requests.

In this paper, we consider the problem of going from high-level specifications of complex control tasks for cyber-physical systems to their actual implementation and execution on physical devices. This transition between abstraction levels inevitably results in a specification-to-execution gap, and we discuss two sources for this gap; namely model based and constraint based. For both of these two types of sources, we show how hybrid control techniques provide the tools needed to compile high-level control programs in such a way that the specification-to-execution gap is removed. The solutions involve introducing new control modes into nominal strings of control modes as well as adjusting the control modes themselves.

This technical note investigates how to produce control programs for complex systems in a systematic manner. In particular, we present an abstraction-based approach to the specification and optimization of motion programs for controlling robot marionettes. The resulting programs are based on the concatenation of motion primitives and are further improved upon using recent results in optimal switch-time control. Simulations as well as experimental results illustrate the operation of the proposed method.

Discusses the purpose, scope and articles that will appear in the IEEE Transactions on Control of Network Systems.

In this paper, we study the problem of controlling a group of unmanned aerial vehicles (UAVs) to provide convoy protection to a group of ground vehicles. The UAVs are modeled as Dubins vehicles flying at a constant altitude with bounded turning radius. We first present time-optimal paths to provide convoy protection to stationary ground vehicles. Then, we propose a control strategy to provide convoy protection to ground vehicles moving in straight lines. The minimum number of UAVs required to provide perpetual convoy protection, in both cases, are derived.

Many large-scale multi-agent missions consist of a sequence of subtasks, each of which can be accomplished separately by having agents execute appropriate decentralized controllers. However, many decentralized controllers have network topological prerequisites that must be satisfied in order to achieve the desired effect on a system. Therefore, one cannot always hope to accomplish the original mission by having agents naively switch through executing the controllers for each subtask. This paper extends the Graph Process Specification (GPS) framework, which was presented in previous work as a way to script decentralized control sequences for agents, while ensuring that network topological requirements are satisfied when each controller in the sequence is executed. Atoms, the fundamental building blocks in GPS, each explicitly state a network topological transition. Moreover, they specify the means to make that transition occur by providing a multi-agent controller, as well as a way to locally detect the transition. Scripting a control sequence in GPS therefore reduces to selecting a sequence of atoms from a library to satisfy network topological requirements, and specifying interrupt conditions for switching. As an example of how to construct an atom library, the optimal decentralization algorithm is used to generate atoms for agents to track desired multi-agent motions with when the network topology is static. The paper concludes with a simulation of agents performing a drumline-inspired dance using decentralized controllers generated by optimal decentralization and scripted using GPS.

One challenge facing coordination and deployment of unmanned aerial vehicles (UAVs) today is the amount of human involvement needed to carry out a successful mission. Currently, control and coordination of UAVs typically involves multiple operators to control a single agent. The aim of this article is to invert this relationship, enabling a single pilot to control and coordinate a group of UAVs. Furthermore, decision support is provided to the pilot to facilitate effective control of the UAV team. In the scenario envisioned in this article, the human operator (the pilot) is operating along-side a team of UAVs. The pilot communicates with the UAV team remotely and controls the UAV team to execute a surveillance mission.

In this paper, we present feedback control laws for an autonomous vehicle with rigidly mounted range sensors to track a desired curve. In particular, we consider a vehicle that has a group of rays around two center rays that are perpendicular to the velocity of the vehicle. Under such a sensor configuration, singularities are bound to occur in the curve tracking feedback control law when tracking concave curves. To overcome this singularity, we derive a hybrid strategy of switching between control laws when the vehicle gets close to singularities. Rigorous proof and extensive simulation results verify the validity of the proposed feedback control law.

We present an optimal timing control formulation for the problem of controlling autonomous puppets. In particular, by appropriately timing the different movements, entire plays can be performed. Such plays are produced by concatenating sequences of motion primitives and a compiler optimizes these sequences, using recent results in optimal switch-time control. Additionally, we apply saddle-point techniques to approach the problem of timing constraints among interconnected puppets. Experimental results illustrate the operation of the proposed methods.

Social behavior of animals can offer solution models for missions involving a large number of heterogeneous vehicles, such as light combat ships, unmanned aerial vehicles, and unmanned underwater vehicles. We draw inspiration from the foraging techniques of bottlenose dolphins to address the problem of heterogeneous multi-agent herding. We produce a hybrid automaton model of the entire foraging method - search, detect, and capture - where agents are modeled as first-order systems in which interactions are defined through spatial proximity. Finally, simulations are provided to illustrate that our model is expressive enough to capture this complex biological phenomenon.