Drawing inspiration from Maslow's “hierarchy of needs”, this paper develops a real-time control synthesis framework for robots to address hierarchical, complex objectives, recognizing that their behaviors are inherently driven by underlying needs. Each need is encoded by the zero-superlevel set of a control barrier function (CBF), which can be time-varying, and all the needs at the same level in a hierarchy are composed into a single one through Boolean compositions of the corresponding CBFs. The effectiveness of the proposed framework is demonstrated through a hypothetical interstellar exploration mission using laboratory robots, and novel results on nonsmooth CBF and time-varying CBF are derived.

Environmental monitoring is used to characterize the health and relationship between organisms and their environments. In forest ecosystems, robots can serve as platforms to acquire such data, even in hard-to-reach places where wire-traversing platforms are particularly promising due to their efficient displacement. This paper presents the RaccoonBot, which is a novel autonomous wire-traversing robot for persistent environmental monitoring, featuring a fail-safe mechanical design with a self-locking mechanism in case of electrical shortage. The robot also features energy-aware mobility through a novel Solar tracking algorithm, that allows the robot to find a position on the wire to have direct contact with solar power to increase the energy harvested. Experimental results validate the electro-mechanical features of the RaccoonBot, showing that it is able to handle wire perturbations, different inclinations, and achieving energy autonomy.

Inspired by common features found in collaborative behaviors in nature, we investigate a general collaborative pursuit framework enabling heterogeneous multi-robot systems to adapt to dynamic environments and diverse tasks. A class of augmented Fokker-Planck equations is formulated to characterize dynamic environmental conditions, and the resulting time-varying density functions drive a novel coverage-based controller, with provable stability properties, for the participating robots to perform tasks in real time. The developed framework is decentralized and incorporates heterogeneity among different robots in task suitability, relative performance in a specific task, and safe operating regions. To demonstrate its adaptivity and effectiveness, the framework is implemented across four experimental applications ranging from multi-robot coordination to collaboration, namely forest firefighting, pursuit-evasion, monitoring of various environmental phenomena, and phoretic interactions.

This paper proposes a distributed coverage control strategy for quadcopters equipped with downward-facing cameras that prevents the appearance of unmonitored areas in between the quadcopters' fields of view (FOVs). We derive a necessary and sufficient condition for eliminating any unsurveilled area that may arise in between the FOVs among a trio of quadcopters by utilizing a power diagram, i.e. a weighted Voronoi diagram defined by radii of FOVs. Because this condition can be described as logically combined constraints, we leverage nonsmooth control barrier functions (NCBFs) to prevent the appearance of unmonitored areas among a team's FOV. We then investigate the symmetric properties of the proposed NCBFs to develop a distributed algorithm. The proposed algorithm can support the switching of the NCBFs caused by changes of the quadcopters composing trios. The existence of the control input satisfying NCBF conditions is analyzed by employing the characteristics of the power diagram. The proposed framework is synthesized with a coverage control law that maximizes the monitoring quality while reducing overlaps of FOVs. The proposed method is demonstrated in simulation and experiment.

A coverage-based collaborative control strategy is developed in this paper for a multi-robot system with heterogeneous effective sensing ranges and safe operation zones to simultaneously estimate the states of and follow multiple targets governed by stochastic dynamics. Multiplicatively weighted Voronoi diagrams are exploited to define each robot's dominant region considering its limited sensing radius. The asymptotically stable system dynamics enabling the heterogeneous multi-robot system to (locally) optimally cover the time-varying probability density distributions that characterize the uncertainties of the targets' positions is derived, and minimally perturbed by control barrier functions designed to ensure that each robot moves within its safe operation zone in a collision-free manner. Specific target dynamics and measurement models are chosen in the experiment, whose results demonstrate the effectiveness of the proposed dynamic multi-target tracking approach.

Systematically designing local interaction rules to achieve collective behaviors in robot swarms is a challenging endeavor, especially in micro robots, where size restrictions imply severe sensing, communication, and computation limitations. In such robot swarms, performing useful functions is often preconditioned on the formation of high-density aggregations which can facilitate collective signaling and information sharing. In this article, we present a systematic approach to control aggregation behaviors by leveraging the physical interactions in a swarm of 300 3-mm vibration-driven micro bristle robots that we designed and fabricated. We demonstrate the ability to control the degree of aggregation by varying the motility characteristics of the robots through global vibration frequency and amplitude inputs, after comprehensive characterization, modeling, and simulation of the locomotion dynamics and robot interactions. To quantify the degree of aggregation, we also introduce a new metric, the motility-induced phase separation index index, which unlike many existing methods does not require a scenario-specific tuning of parameters. Our investigations reveal how physics-driven interaction mechanisms can be exploited to achieve desired behaviors in minimally equipped robot swarms and highlight the specific ways in which hardware and software developments aid in the achievement of collision-induced aggregations.

This paper presents a solution to the problem of executing robotic tasks over time horizons that exceed the robot's total battery capacity. In the presented robotics application, the robot's mission is to satisfy two tasks: environmental exploration and environmental monitoring, both of which need to be executed over long time periods. These tasks need therefore to be persistified. Ensuring the longevity of the system requires to consider a maximum energy consumption at all times. Including a dependency of battery voltage dynamics on the control input introduces a quadratic term in the battery's dynamic equation, making previous persistification approaches no longer directly applicable, as they used control-affine dynamics. In this paper an alternative task persistification approach is formulated. The control strategy used is based on Control Barrier Functions (CBFs). Using which, the generated controller renders the system state variables, position and battery voltage, to remain within the boundaries of their safe sets. This generated safe controller minimally modifies the nominal controller, which commands the robot to satisfy its environmental mission. Once the CBFs are selected, the minimization problem that results is one with a quadratic cost and two nested scalar constraints: one of which is quadratic and the other is linear. This new class of problems is noted as Quadratic Cost Scalar Linear and Quadratically Constrained (QCSLQC) problems. An analytical solution to the general QCSLQC problem is presented.

Applications that require multirobot systems to operate independently for extended periods of time in unknown or unstructured environments face a broad set of challenges, such as hardware degradation, changing weather patterns, or unfamiliar terrain. To operate effectively under these changing conditions, algorithms developed for long-term autonomy applications require a stronger focus on robustness. Consequently, this work considers the ability to satisfy the operation-critical constraints of a disturbed system in a modular fashion, which means compatibility with different system objectives and disturbance representations. Toward this end, this article introduces a controller-synthesis approach to constraint satisfaction for disturbed control-affine dynamical systems by utilizing control barrier functions (CBFs). The aforementioned framework is constructed by modeling the disturbance as a union of convex hulls and leveraging previous work on CBFs for differential inclusions. This method of disturbance modeling grants compatibility with different disturbance-estimation methods. For example, this work demonstrates how a disturbance learned via a Gaussian process may be utilized in the proposed framework. These estimated disturbances are incorporated into the proposed controller-synthesis framework which is then tested on a fleet of robots in different scenarios.

In the context of heterogeneous multirobot teams deployed for executing multiple tasks, this article develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, we opt for a survivability-focused approach. Toward this end, the task prioritization and execution—through which the allocation of tasks to robots is effectively realized—are encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is interpreted as a prioritization of a task over all others by each of the robots. Furthermore, we present a novel framework to represent the heterogeneous capabilities of the robots, by distinguishing between the features available on the robots and the capabilities enabled by these features. By embedding these descriptions within the optimization problem, we make the framework resilient to situations, where environmental conditions make certain features unsuitable to support a capability and when component failures on the robots occur. We demonstrate the efficacy and resilience of the proposed approach in a variety of use-case scenarios, consisting of simulations and real robot experiments.

We propose a framework for resilience in a networked heterogeneous multirobot team subject to resource failures. Each robot in the team is equipped with resources that it shares with its neighbors, which are identified based on the team’s communication graph. Additionally, each robot in the team executes a task, whose performance depends on the resources to which it has access. When a resource on a particular robot becomes unavailable ( e.g., a camera ceases to function), the team optimally reconfigures its communication network so that the robots affected by the failure can continue their tasks. We focus on a monitoring task, where robots individually estimate the state of an exogenous process. We encode the end-to-end effect of a robot’s resource loss on the monitoring performance of the team by defining a new stronger notion of observability—one-hop observability. By abstracting the impact that low-level individual resources have on the task performance through the notion of one-hop observability, our framework leads to the principled reconfiguration of information flow in the team to effectively replace the lost resource on one robot with information from another, as long as certain conditions are met. Network reconfiguration is converted to the problem of selecting edges to be modified in the system’s communication graph after a resource failure has occurred. A controller based on finite-time convergence control barrier functions drives each robot to a spatial location that enables the communication links of the modified graph. We validate the effectiveness of our framework by deploying it on a team of differential-drive robots estimating the position of a group of quadrotors.

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.

In this paper, we investigate how heterogeneous multi-robot systems with different sensing capabilities can observe a domain with an a priori unknown density function. Common coverage control techniques are targeted towards homogeneous teams of robots and do not consider what happens when the sensing capabilities of the robots are vastly different. This work proposes an extension to Lloyd’s algorithm that fuses coverage information from heterogeneous robots with differing sensing capabilities to effectively observe a domain. Namely, we study a bimodal team of robots consisting of aerial and ground agents. In our problem formulation we use aerial robots with coarse domain sensors to approximate the number of ground robots needed within their sensing region to effectively cover it. This information is relayed to ground robots, who perform an extension to Lloyd’s algorithm that balances a locally focused coverage controller with a globally focused distribution controller. The stability of the Lloyd’s algorithm extension is proven and its performance is evaluated through simulation and experiments using the Robotarium, a remotely-accessible, multi-robot testbed.

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 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, 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 simulated scenario of 20 fixed wing aircraft where the vehicles are able to maintain safe distances from each other even though the vehicles are subject to limited range sensing.

A formulation of the area defense and surveillance problem for one intruder and one defense and surveillance robot and its corresponding solution using control barrier functions is presented. The defense robot must follow the intruder as it moves through a rectangular region in the plane, ensuring that the position of the intruder is also within a rectangular region attached to the surveillance robot. The proposed reactive and closed-form control laws depend on the positions of the robots, their maximum speeds, and the size of the rectangular regions. We show the application and effectiveness of our results in experiments with real robots.

When organizing robots into formations, the interplay between the underlying network topology of the team and the sensing and communication modalities available to the individual robots has a fundamental effect on what types of formations are possible. The purpose of this article is to characterize the available motions of formations in which relative angles between robots equipped with bearing-only sensors are maintained. First, infinitesimal shape-similarity, a property of frameworks for which maintaining certain angles between robots ensures that the formation is invariant to translation, rotation, and uniform scaling, is examined; the shape-similarity matrix is redeveloped, and results on its nullspace are presented. Second, triangulations, a class of frameworks, are shown to be infinitesimally shape-similar. Finally, the coupling between network topology and robot capabilities is examined through the design of a decentralized heterogeneous formation-control strategy for a class of triangulations in which all robots are equipped with bearing-only sensors and a single robot can measure distances; the formation-control strategy is demonstrated on a team of differential-drive robots.

A number of coordinated behaviors are proposed for achieving specific tasks for multirobot systems. However, since most applications require more than one such behavior, one needs to be able to compose together sequences of behaviors while respecting local information flow constraints. Specifically, when the interagent communication depends on interrobot distances, these constraints translate into particular configurations that must be reached in finite time in order for the system to be able to transition between the behaviors. To this end, we develop a distributed framework based on finite-time convergence control barrier functions that enables a team of robots to adjust its configuration in order to meet the communication requirements for the different tasks. In order to demonstrate the significance of the proposed framework, we implemented a full-scale scenario where a team of eight planar robots explore an urban environment in order to localize and rescue a subject.

In this paper, we propose a graph-based search method to optimally allocate tasks to a team of robots given a global task specification. In particular, we define these agents as discrete transition systems. In order to allocate tasks to the team of robots, we decompose finite linear temporal logic (LTL) specifications and consider agent specific cost functions. We propose to use the stochastic optimization technique, cross entropy, to optimize over this cost function. The multi-agent task allocation cross-entropy (MTAC-E) algorithm is developed to determine both when it is optimal to switch to a new agent to complete a task and minimize the costs associated with individual agent trajectories. The proposed algorithm is verified in simulation and experimental results are included.

We present an algorithm for multi-robot coverage of an initially unknown spatial scalar field characterized by a density function, whereby a team of robots simultaneously estimates and optimizes its coverage of the density function over the domain. The proposed algorithm borrows powerful concepts from Bayesian Optimization with Gaussian Processes that, when combined with control laws to achieve centroidal Voronoi tessellation, give rise to an adaptive sequential sampling method to explore and cover the domain. The crux of the approach is to apply a control law using a surrogate function of the true density function, which is then successively refined as robots gather more samples for estimation. The performance of the algorithm is justified theoretically under slightly idealized assumptions, by demonstrating asymptotic no-regret with respect to the coverage obtained with a known density function. The performance is also evaluated in simulation and on the Robotarium with small teams of robots, confirming the good performance suggested by the theoretical analysis.

The interplay between network topology and the interaction modalities of a multi-robot team fundamentally impact the types of formations that can be achieved. To explore the trade-offs between network structure and the sensing and communication capabilities of individual robots, this paper applies controller synthesis to formation control of infinitesimally shape-similar frameworks, for which maintaining the relative angles between robots ensures invariance of the framework to translation, rotation, and uniform scaling. Beginning with the development of a controller for the sole purpose of maintaining the formation, the controller-synthesis approach is introduced as a mechanism for incorporating user- designated objectives while ensuring that the formation is maintained. Both centralized and decentralized formulations of the synthesized controller are presented, the resulting sensing and communication requirements are discussed, and the method is demonstrated on a team of differential-drive robots.

For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions. This paper presents an adaptive task allocation and task execution framework which allows individual robots to prioritize among tasks while explicitly taking into account their efficacy at performing the tasks---the parameters of which might be unknown before deployment and/or might vary over time. Such a \emph{specialization} parameter---encoding the effectiveness of a given robot towards a task---is updated on-the-fly, allowing our algorithm to reassign tasks among robots with the aim of executing them. The developed framework requires no explicit model of the changing environment or of the unknown robot capabilities---it only takes into account the progress made by the robots at completing the tasks. Simulations and experiments demonstrate the efficacy of the proposed approach during variations in environmental conditions and when robot capabilities are unknown before deployment.

This paper presents a coverage control algorithm for teams of quadcopters with downward facing visual sensors that prevents the appearance of coverage holes in-between the monitored areas while maximizing the coverage quality as much as possible. We derive necessary and sufficient conditions for preventing the appearance of holes in-between the fields of views among trios of robots. Because this condition can be expressed as logically combined constraints, control nonsmooth barrier functions are implemented to enforce it. An algorithm which extends control nonsmooth barrier functions to hybrid systems is implemented to manage the switching among barrier functions caused by the changes of the robots composing trio. The performance and validity of the proposed algorithm are evaluated in simulation as well as on a team of quadcopters.

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.

The sensing modalities available to individual agents in a multi-robot team have a significant effect on what the team can accomplish. Previous work on infinitesimal shape-similarity has shown that maintaining relative angles between robots equipped with bearing-only sensors can render a formation of these robots invariant to translation, rotation, and uniform scaling; however, previous work has not proposed decentralized control strategies for exploiting this invariance. To address this deficiency, this paper proposes a decentralized formation control strategy for assembled triangulations, a class of infinitesimally shape-similar formations. Heterogeneous in terms of sensing and control, a decentralized formation control strategy is developed in which one robot sets the position of the formation, a robot capable of measuring bearings and distances controls the scale and heading, and the remaining robots maintain the assembled triangulation. The asymptotic controllers that compose the formation control strategy of this work are implemented on a team of differential-drive robots.

This paper presents a coverage control strategy for teams of quadcopters that ensures that no area is left unsurveyed in between the fields of view of the visual sensors mounted on the quadcopters. We present a locational cost that quantifies the team's coverage performance according to the sensors' performance function. Moreover, the cost function penalizes overlaps between the fields of view of the different sensors, with the objective of increasing the area covered by the team. A distributed control law is derived for the quadcopters so that they adjust their position and zoom according to the direction of ascent of the cost. Control barrier functions are implemented to ensure that, while executing the gradient ascent control law, no holes appear in between the fields of view of neighboring robots. The performance of the algorithm is evaluated in simulated experiments.

In densely-packed robot swarms operating in confined regions, spatial interference-which manifests itself as a competition for physical space-forces robots to spend more time navigating around each other rather than performing the primary task. This paper develops a decentralized algorithm that enables individual robots to decide whether to stay in the region and contribute to the overall mission, or vacate the region so as to reduce the negative effects that interference has on the overall efficiency of the swarm. We develop this algorithm in the context of a distributed collection task, where a team of robots collect and deposit objects from one set of locations to another in a given region. Robots do not communicate and use only binary information regarding the presence of other robots around them to make the decision to stay or retreat. We illustrate the efficacy of the algorithm with experiments on a team of real robots.

In this paper we consider a constrained coverage control problem for a team of mobile robots. The robots are asked to provide sensor coverage over a two-dimensional domain, while being constrained to only move on a curve. The unconstrained coverage problem can be effectively solved by defining a locational cost to be minimized by the robots, in a decentralized fashion, using gradient descent. However, a direct projection of the solution to the unconstrained problem onto the curve may result in a very poor spatial allocation of the team within the two-dimensional domain. Therefore, we propose a modification to the locational cost, which incorporates the constraints, and a convex relaxation that allows us to efficiently minimize a convex approximation of the cost using a decentralized strategy. The resulting algorithm is implemented on a team of mobile robots.

In this paper, we study the problem of navigating quadcopters through a sequence of hoops. The specification may be given directly or indirectly via a linear temporal logic (LTL) formula. We approach this problem in three phases. First, we introduce a planner that generates a path through a given sequence of hoops. Second, we augment our planner to leverage a given specification in linear temporal logic (LTL) and generate a sequence that satisfies this specification. Third, we implement cross-entropy optimization on this planner to enhance trajectory performance where quadcopter trajectories are modified within the solution space to optimize over a cost function. We implement this planner as a novel interaction modality between users and quadcopters on the Robotarium. Simulation and experimental results are provided.

In robot swarms operating under highly restrictive sensing and communication constraints, individuals may need to use direct physical proximity to facilitate information exchange. However, in certain task-related scenarios, this requirement might conflict with the need for robots to spread out in the environment, e.g., for distributed sensing or surveillance applications. This paper demonstrates how a swarm of minimally-equipped robots can form high-density robot aggregates that coexist with lower robot densities in space. We envision a scenario where a swarm of vibration-driven robots-which sit atop bristles and achieve directed motion by vibrating them-move randomly in an environment while colliding with each other. Theoretical techniques from the study of far-from-equilibrium collectives and statistical mechanics clarify the mechanisms underlying the formation of these high and low density regions. Specifically, we capitalize on a transformation that connects the collective properties of a system of self-propelled particles with that of a well-studied molecular fluid system, thereby inheriting the rich theory of equilibrium thermodynamics. Real robot experiments as well as simulations illustrate how inter-robot collisions can precipitate the formation of non-uniform robot densities in a closed and bounded region.

In this paper we present a study of a specific class of vibration-driven robots: the brushbots. In a bottom-up fashion, we start by deriving dynamic models of the brushes and we discuss the conditions under which these models can be employed to describe the motion of brushbots. Then, we present two designs of brushbots: a fully-actuated platform and a differential-drive-like one. The former is employed to experimentally validate both the developed theoretical models and the devised motion control algorithms. Finally, a coordinated-control algorithm is implemented on a swarm of differential-drive-like brushbots in order to demonstrate the design simplicity and robustness that can be achieved by employing a vibration-based locomotion strategy.

As the size of robots decreases in multirobot systems, collisions cease to be catastrophic events that need to be avoided at all costs. This implies that less conservative, coordinated control strategies can be employed, where collisions are not only tolerated, but can potentially be harnessed as an information source. In this paper, we follow this line of inquiry by employing collisions as a sensing modality that provides information about the robots' surroundings. We envision a collection of robots moving around with no sensors other than binary, tactile sensors that can determine if a collision occurred, and let the robots use this information to determine their locations. We apply a probabilistic localization technique based on mean-field approximations that allows each robot to maintain and update a probability distribution over all possible locations. Simulations and real multirobot experiments illustrate the feasibility of the proposed approach.

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 that 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.

When designing control strategies for differential-drive mobile robots, one standard tool is the consideration of a point at a fixed distance along a line orthogonal to the wheel axis instead of the full pose of the vehicle. This abstraction supports replacing the non-holonomic, three-state unicycle model with a much simpler two-state single-integrator model (i.e., a velocity-controlled point). Yet this transformation comes at a performance cost, through the robot's precision and maneuverability. This work contains derivations for expressions of these precision and maneuverability costs in terms of the transformation's parameters. Furthermore, these costs show that only selecting the parameter once over the course of an application may cause an undue loss of precision. Model Predictive Control (MPC) represents one such method to ameliorate this condition. However, MPC typically realizes a control signal, rather than a parameter, so this work also proposes a Parametric Model Predictive Control (PMPC) method for parameter and sampling horizon optimization. Experimental results are presented that demonstrate the effects of the parameterization on the deployment of algorithms developed for the single-integrator model on actual differential-drive mobile robots.

In this paper we consider the coverage control problem for a team of wire-traversing robots. The two-dimensional motion of robots moving in a planar environment has to be projected to one-dimensional manifolds representing the wires. Starting from Lloyd's descent algorithm for coverage control, a solution that generates continuous motion of the robots on the wires is proposed. This is realized by means of a Continuous Onto Wires (COW) map: the robots' workspace is mapped onto the wires on which the motion of the robots is constrained to be. A final projection step is introduced to ensure that the configuration of the robots on the wires is a local minimizer of the constrained locational cost. An algorithm for the continuous constrained coverage control problem is proposed and it is tested both in simulation and on a team of mobile robots.

To effectively control complex dynamical systems, accurate nonlinear models are typically needed. However, these models are not always known. In this paper, we present a data-driven approach based on Gaussian processes that learns models of quadrotors operating in partially unknown environments. What makes this challenging is that if the learning process is not carefully controlled, the system will go unstable, i.e., the quadcopter will crash. To this end, barrier certificates are employed for safe learning. The barrier certificates establish a non-conservative forward invariant safe region, in which high probability safety guarantees are provided based on the statistics of the Gaussian Process. A learning controller is designed to efficiently explore those uncertain states and expand the barrier certified safe region based on an adaptive sampling scheme. Simulation results are provided to demonstrate the effectiveness of the proposed approach.

This paper presents a coverage algorithm for multi-robot systems where the robots are equipped with qualitatively different sensing modalities. Unlike previous approaches to the problem of coverage for teams with heterogeneous sensing capabilities, in this paper the robots have access to information about their neighbors' specific sensor modalities. This knowledge affords the ability of ensuring that no robot is tasked with covering features in a region without the required sensing modalities. With this information, a robot can determine which of its neighbors it should coordinate with to cover the environmental features in a region while ignoring robots that are not equipped with that particular sensory capability. We derive a distributed control algorithm that allows the robots to move in a direction of descent relative to a novel locational cost, in order to minimize it. The performance of the algorithm is evaluated on a real robotic platform.

Formation control strategies are fundamentally impacted by the sensing modalities present in the multi-robot team. Infinitesimal shape-similarity describes frameworks for which maintaining the relative angles between robots in formation also maintains the shape up to translation, rotation, and uniform scaling; however, ensuring invariance of the formation to these motions requires that the robots measure a sufficient number of angles, which means that the topology of the frame-work must be carefully designed. In this paper, we investigate the self-assembly of a class of infinitesimally shape-similar frameworks by robots equipped with bearing-only sensors. To accomplish self-assembly, we introduce a rank condition on the shape-similarity matrix for analyzing frameworks; we then use this rank condition to show that triangulations are infinitesimally shape-similar. A graph grammar is presented to assemble triangulations, and a controller is designed to achieve self-assembly of a team of differential-drive robots.

In multi-robot systems, although the idea of behaviors allows for an efficient solution to low-level tasks, high-level missions can rarely be achieved by the execution of a single behavior. In contrast to this, a sequence of behaviors would provide the requisite expressiveness, but there are no a priori guarantees that the sequence is composable in the sense that the robots can actually execute it. In order to guarantee a provably correct composition of behaviors, Finite-Time Convergence Control Barrier Functions are introduced in this paper to guarantee the terminal configuration of one behavior is a valid initial configuration for the following one. Nominal control inputs prescribed by the behaviors are modified in a minimally invasive fashion, in order to establish the information-exchange network required by the following behavior. The effectiveness of the proposed composition strategy is validated on a team of mobile robots.

This paper concerns optimal mode-scheduling in autonomous switched-mode hybrid dynamical systems, where the objective is to minimize a cost-performance functional defined on the state trajectory as a function of the schedule of modes. The controlled variable, namely the modes’ schedule, consists of the sequence of modes and the switchover times between them. We propose a gradient-descent algorithm that adjusts a given mode-schedule by changing multiple modes over time-sets of positive Lebesgue measures, thereby avoiding the inefficiencies inherent in existing techniques that change the modes one at a time. The algorithm is based on steepest descent with Armijo step sizes along Gâteaux differentials of the performance functional with respect to schedule-variations, which yields effective descent at each iteration. Since the space of mode-schedules is infinite dimensional and incomplete, the algorithm’s convergence is proved in the sense of Polak’s framework of optimality functions and minimizing sequences. Simulation results are presented, and possible extensions to problems with dwell-time lower-bound constraints are discussed.

In multi-robot systems where a central decision maker is specifying the movement of each individual robot, a communication failure can severely impair the performance of the system. This paper develops a motion strategy that allows robots to safely handle critical communication failures for such multi-robot architectures. For each robot, the proposed algorithm computes a time horizon over which collisions with other robots are guaranteed not to occur. These safe time horizons are included in the commands being transmitted to the individual robots. In the event of a communication failure, the robots execute the last received velocity commands for the corresponding safe time horizons leading to a provably safe open-loop motion strategy. The resulting algorithm is computationally effective and is agnostic to the task that the robots are performing. The efficacy of the strategy is verified in simulation as well as on a team of differential-drive mobile robots.

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.

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.

This paper revisits the formation control problem, whereby a team of mobile robots assemble and maintain a desired geometric shape. However, the types of shapes that are realizable by a given robot team depends directly on the sensing modalities of the individual robots. For example, rigidity-based approaches make use of distance measurements, and the idea is to restrict the motion of the robots so that all inter-robot distances are maintained, thereby ensuring purely rigid motion. Rather than distances, we notice that for certain sensor classes, such as monocular pinhole cameras, only relative angle measurements are available. To this end, we introduce the notion of shape-similar motions, which constitutes a class of angle preserving motions that also preserve the shape of the formation in the sense that rigid motions as well as uniform scalings are permissible. We construct the shape-similarity matrix, whose null-space captures angle preserving motions, and show that by restricting the dimension of the null-space, we can guarantee that motions of the formation preserve the shape. Lastly, we implement a shape-similar controller on a team of differential drive mobile robots.

In this paper, we propose a distributed version of the Hungarian method to solve the well-known assignment problem. In the context of multirobot applications, all robots cooperatively compute a common assignment that optimizes a given global criterion (e.g., the total distance traveled) within a finite set of local computations and communications over a peer-to-peer network. As a motivating application, we consider a class of multirobot routing problems with “spatiotemporal” constraints, i.e., spatial targets that require servicing at particular time instants. As a means of demonstrating the theory developed in this paper, the robots cooperatively find online suboptimal routes by applying an iterative version of the proposed algorithm in a distributed and dynamic setting. As a concrete experimental test bed, we provide an interactive “multirobot orchestral” framework, in which a team of robots cooperatively plays a piece of music on a so-called orchestral floor.

This paper presents a framework for multirobot motion planning that characterizes pairwise interactions between agents, e.g., crossing paths while en route to a destination. Mixing is identified as the number of pairwise crossings exhibited by the robot motion. Mixing patterns specified through elements of the braid group provide sufficient level of abstraction to describe interactions without concern for the geometry of the motion. Controllers are constructed explicitly reasoning about the spatial collocation of robots to execute mixing patterns, achieving rich motion in a shared space, e.g., to exchange inter-robot information. We do not focus on achieving a particular pattern, but rather on the problem of being able to execute a whole class of them (e.g., all patterns with at most $M$ pairwise interactions). The result is a hybrid system driven by symbolic inputs that are mapped onto paths, realizing desired mixing levels. Controllers derived from optimal control provide theoretical bounds on the achievable amount of mixing, satisfaction of spatio-temporal constraints, and collision-free trajectories. Designs are carried to implementation on real robot platforms.

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.

We propose a distributed and cooperative motion and task control scheme for a team of mobile robots that are subject to dynamic constraints including inter-robot collision avoidance and connectivity maintenance of the communication network. Moreover, each agent has a local high-level task given as a Linear Temporal Logic (LTL) formula of desired motion and actions. Embedded graph grammars (EGGs) are used as the main tool to specify local interaction rules and switching control modes among the robots, which is then combined with the model-checking-based task planning module. It is ensured that all local tasks are satisfied while the dynamic constraints are obeyed at all time. The overall approach is demonstrated by simulation and experimental results.

Current multi-agent robotic testbeds are prohibitively expensive or highly specialized and as such their use is limited to a small number of research laboratories. Given the high price tag, what is needed to scale multi-agent testbeds down both in price and size to make them accessible to a larger community? One answer is the GRITSBot, an inexpensive differential drive microrobot designed specifically to lower the entrance barrier to multi-agent robotics. The robot allows for a straightforward transition from current ground-based systems to the GRITSBot testbed because it closely resembles expensive platforms in capabilities and architecture. Additionally, the GRITSBot's support system allows a single user to easily operate and maintain a large collective of robots. These features include automatic sensor calibration, autonomous recharging, wireless reprogramming of the robot, as well as collective control.

This paper presents an algorithm that optimally explores an unknown environment with regions of varying degrees of importance. The algorithm, termed Ergodic Environmental Exploration (E3), is a finite receding horizon optimal control algorithm that minimizes control effort and the difference between the time average behavior of the system's trajectory and the distribution of the gain in information. The novelty of the E3algorithm is the gain in information distribution used in the exploration trajectory optimization. The gain in information distribution uses an estimate of the information distribution and the confidence value on that estimate. Successful experiments have been conducted using E3on a real mobile robot to explore an unknown 2-dimensional area. Results of these experiments are discussed and displayed with figures and a movie.

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.

This paper investigates how haptic interactions can be defined for enabling a single operator to control and interact with a team of mobile robots. Since there is no unique or canonical mapping from the swarm configuration to the forces experienced by the operator, a suitable mapping must be developed. To this end, multi-agent manipulability is proposed as a potentially useful mapping, whereby the forces experienced by the operator relate to how inputs, injected at precise locations in the team, translate to swarm-level motions. Small forces correspond to directions in which it is easy to move the swarm, while larger forces correspond to more costly directions. Initial experimental results support the viability of the proposed, haptic, human-swarm interaction mapping, through a user study where operators are tasked with driving a collection of robots through a series of way points.

An approach is presented for influencing teams of robots by means of time-varying density functions, representing rough references for where the robots should be located. A continuous-time coverage algorithm is proposed and distributed approximations are given whereby the robots only need to access information from adjacent robots. Robotic experiments show that the proposed algorithms work in practice, as well as in theory.

Summary form only given. As physical objects connect over information-exchange networks, new abstractions and design tools are needed. This is particularly true when the physical objects move and act in the world, e.g., robots. We will discuss some of the fundamental challenges and opportunities that present themselves when robots connect on a large scale, taking us from an internet with things, to an Internet of Things.

Summary form only given. The last few years have seen significant progress in our understanding of how one should structure multi-robot systems. New control, coordination, and communication strategies have emerged and, in this talk, we discuss some of these developments. In particular, we will show how one can go from global, geometric, team-level specifications to local coordination rules for achieving and maintaining formations, area coverage, and swarming behaviors. One aspect of this concerns how users can interact with networks of mobile robots in order to inject new, global information and objectives. We will also investigate what global objectives are fundamentally implementable in a distributed manner on a collection of spatially distributed and locally interacting agents.

This paper investigates the problem of finding shortest paths through 3-dimensional cluttered environments. In particular, an algorithm is presented that determines the shortest path between two points in an environment with obstacles which can be implemented on robots with capabilities of detecting obstacles in the environment. As knowledge of the environment is increasing while the vehicle moves around, the algorithm provides not only the global minimizer - or shortest path - with increasing probability as time goes by, but also provides a series of local minimizers. The feasibility of the algorithm is demonstrated on a quadrotor robot flying in an environment with obstacles.

This paper addresses the issue of human-swarm interactions by proposing a new set of affordances that make a multi-robot system amenable to human control. In particular, we propose to use clay- a deformable medium- as the “joystick” for controlling the swarm, supporting such affordances as stretching, splitting and merging, shaping, and mixing. The contribution beyond the formulation of these affordances is the coupling of an image recognition framework to decentralized control laws for the individual robots, and the developed human-swarm interaction methodology is applied to a team of mobile robots.

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.

Snake robots are controlled by implementing gaits inspired from their biological counterparts. However, transitioning between these gaits often produces undesired oscillations which cause net movements that are difficult to predict. In this paper we present a framework for implementing gaits which will allow for smooth transitions. We also present a method to determine the optimal time for each module of the snake to switch between gaits in a decentralized fashion. This will allow for each module to participate in minimizing a cost by communicating with a set of modules in a local neighborhood. Both of these developments will help to maintain desired properties of the gaits during transition.

In this paper, we connect local properties in a mobile planar multi-robot team to the task of creating decentralized real time algorithmic music. Using a nonlinear formation control law inspired by the consensus equation, we map the local motion parameters of robots to Euclidean rhythms with the use of sequencers. The control parameters allow a human user to direct this decentralized musical process by guiding and interfering with the robots' motion, which subsequently affects their musical activity. We simulate such a robotic system in real time, demonstrating the expressiveness of the decentralized algorithmic musical output as well as a number of behaviors that arise out of the manipulation of the control parameters.

Model predictive control can be computationally intensive as it has to compute an optimal control trajectory at each time instant. As such, we present a method in which parametrized behaviors are introduced as a level of abstraction to give a finite representation to the control trajectory optimization. As these control laws can be designed to accomplish different tasks, the robot is able to use the presented framework to tune the parameters online to achieve desirable results. Moreover, we build on switch-time optimization techniques to allow the model predictive control framework to optimize over a series of given behaviors, allowing for an added level of adaptability. We illustrate the utility of the framework through the control of a nonholonomic mobile robot.

We introduce and experimentally validate a novel algorithmic model for physical human-robot interaction with hybrid dynamics. Our computational solutions are complementary to passive and compliant hardware. We focus on the case where human motion can be predicted. In these cases, the robot can select optimal motions in response to human actions and maximize safety. By representing the domain as a Markov Game, we enable the robot to not only react to the human but also to construct an infinite horizon optimal policy of actions and responses. Experimentally, we apply our model to simulated robot sword defense. Our approach enables a simulated 7-DOF robot arm to block known attacks in any sequence. We generate optimized blocks and apply game theoretic tools to choose the best action for the defender in the presence of an intelligent adversary.

This paper addresses the classification problem for a set of autonomous robots that interact with each other. The objective is to classify agents that "behave" in "different way", due to their own physical dynamics or to the interaction protocol they are obeying to, as belonging to different "species". This paper describes a technique that allows a decentralized classification system to be built in a systematic way, once the hybrid models describing the behavior of the different species are given. This technique is based on a decentralized identification mechanism, by which every agent classifies its neighbors using only local information. By endowing every agent with such a local classifier, the overall system is enhanced with the ability to run behaviors involving individuals of the same species as well as of different ones. The mechanism can also be used to measure the level of cooperativeness of neighbors and to discover possible intruders among them. General applicability of the proposed solution is shown through examples of multiagent systems from Biology and from Robotics.

In this paper, we present a control theoretic formulation for composing human control inputs with an automatic controller for shared control of a quadruped rescue robot. The formulation utilizes a model predictive controller to guide human controlled leg positions to satisfy state constraints that correspond to static stability for the robot. A hybrid control architecture that incorporates the model predictive controller is developed to implement a gait that guarantees stable foot-placements for the robot. The algorithm is applied to a simulation of a quadruped rescue robot with human input provided through haptic joysticks.

Receding horizon control strategies have proven effective in many control and robotic applications. These methods simulate the state a certain time horizon into the future to choose the optimal trajectory. However, in many cases, such as in mobile robot navigation, the selection of an appropriate time horizon is important as too long of a time horizon can amplify the detrimental effects caused by environmental uncertainties in the prediction of the future state while too of a short horizon will lead to reduced performance. In this paper we strike a balance between these two conflicting objectives by first introducing a receding horizon method for navigation founded on schema-based behaviors. We then suggest a method of adapting the time horizon by minimizing a cost function which balances the performance of the underlying control problem (which prefers longer horizons) with the performance of our state prediction (which prefers shorter time horizons). We illustrate the operation with an example which shows the usefulness of our navigation scheme with an adaptive time horizon.

In this paper we investigate how power consumption affects mobility-based coordination algorithms for multi-robot systems by explicitly coupling the control laws to the available power levels. In particular, we will consider a sensor network comprising of mobile sensors which use omni directional RF or radar based antennas for communication, with a disk-shaped communications footprint. As power decrease with time, the footprint shrinks as well, and in this paper we propose a controller that solves the rendezvous problem for such systems, thus providing a novel coupling between mobility algorithms and the available power levels.

We provide a theoretical framework for controlling graph connectivity in mobile robot networks. We discuss proximity-based communication models composed of disk-based or uniformly-fading-signal-strength communication links. A graph-theoretic definition of connectivity is provided, as well as an equivalent definition based on algebraic graph theory, which employs the adjacency and Laplacian matrices of the graph and their spectral properties. Based on these results, we discuss centralized and distributed algorithms to maintain, increase, and control connectivity in mobile robot networks. The various approaches discussed in this paper range from convex optimization and subgradient-descent algorithms, for the maximization of the algebraic connectivity of the network, to potential fields and hybrid systems that maintain communication links or control the network topology in a least restrictive manner. Common to these approaches is the use of mobility to control the topology of the underlying communication network. We discuss applications of connectivity control to multirobot rendezvous, flocking and formation control, where so far, network connectivity has been considered an assumption.

This paper presents a control theoretic formulation and optimal control solution for integrating human control inputs subject to linear state constraints. The formulation utilizes a receding horizon optimal controller to update the control effort given the most recent state and human control input information. The novel solution to the corresponding finite horizon optimal control problem with terminal constraint is derived using Hilbert space methods. The control laws are applied to two planar human-driven mass-cart pendula, where the task is to synchronize the pendula's oscillations.

In this paper we study the problem of controlling a number 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. This paper first presents time-optimal paths for providing convoy protection to static ground vehicles. Then this paper addresses paths and control strategies to provide convoy protection to ground vehicles moving on a straight line. Minimum numbers of UAVs required to provide perpetual convoy protection for both cases are derived.

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.

Vision-based control of a robot formation is challenging because the on-board sensor (camera) only provides the view-angle to the other moving robots, but not the distance that must be estimated. In order to guarantee a consistent estimate of the distance by knowing the control inputs and the sensor outputs in a given interval, the nonlinear multi-robot system must preserve its observability. Recent theoretical studies on leader-follower robot formation exploit the interesting influence that the control actions have on observability. Based on these results, in this paper we present a switching active control strategy for formation control. Our control strategy is active in the sense that, while asymptotically achieving the formation control tasks, it also guarantees the system observability in those cases in which all the robots tend to move along non-observable paths. As a result, both estimation and formation performances are improved. Extensive simulation results show the effectiveness of the proposed design.

Heterogeneous multi-robot networks require novel tools for applications that require achieving and maintaining formations. This is the case for distributing sensing devices with heterogeneous mobile sensor networks. Here, we consider a heterogeneous multi-robot network of mobile robots. The robots have a limited range in which they can estimate the relative position of other network members. The network is also heterogeneous in that only a subset of robots have localization ability. We develop a method for automatically configuring the heterogeneous network to deploy a desired formation at a desired location. This method guarantees that network members without localization are deployed to the correct location in the environment for the sensor placement.

Novel tools are needed to deploy multi-agent networks in applications that require a high degree of accuracy in the achievement and maintenance of geometric formations. This is the case when deploying distributed sensing devices across large spatial domains. Through so-called embedded graph grammars (EGGs), this paper develops a method for automatically generating control programs that ensure that a multi-robot network is deployed according to the desired configuration. This paper presents a communication protocol needed for implementing and executing the control programs in an accurate and deadlock-free manner.

Layered hybrid controllers typically include a planner at the top level with reactive control at the lower levels. The planner considers the state of the robot in a global context. The low-level controllers consider only the local environment of the robot and are able to operate at a high frequency to ensure the safety of the robot. Also, it is often the case that the low-level controllers consider more aspects of the robot's state (e.g. kinematic constraints) than the planner. The consideration of such constraints at the planning level would prohibitively increase the state space the planner must consider and, accordingly, its running time and complexity. In this paper, we investigate how we can take advantage at the planning level of domain knowledge encapsulated in the lower level controllers, and we introduce a feedback mechanism that enables low-level controllers to influence the high-level planner.

This paper addresses the connectedness issue in multiagent coordination, i.e., the problem of ensuring that a group of mobile agents stays connected while achieving some performance objective. In particular, we study the rendezvous and the formation control problems over dynamic interaction graphs, and by adding appropriate weights to the edges in the graphs, we guarantee that the graphs stay connected.

We show how the introduction of a fixed goal location allows us to lower complexity compared to reduced visibility graphs. The number of inter-polygonal edges is decreased from as much as square to not more than simply twice the number of polygons. By virtue of this restriction, we demonstrate how to deploy plan-based navigation strategies in highly unstructured, dynamic environments. This approach has been exercised extensively through numerous outdoor experiments. The vehicle used was the DARPA LAGR robot, and the various test environments included trees, ditches, bushes, tall and short grass, closed canopy, and varyingly-sloped terrain

In this paper, we study the problem of going from a real-world, multi-agent system to the generation of control programs in an automatic fashion. In particular, a computer vision system is presented, capable of simultaneously tracking multiple agents, such as social insects. Moreover, the data obtained from this system is fed into a mode-reconstruction module that generates low-complexity control programs, i.e. strings of symbolic descriptions of control-interrupt pairs, consistent with the empirical data. The result is a mechanism for going from the real system to an executable implementation that can be used for controlling multiple mobile robots.

In this paper, the multiagent coordination problem is studied. This problem is addressed for a class of robots for which control Lyapunov functions can be found. The main result is a suite of theorems about formation maintenance, task completion time, and formation velocity. It is also shown how to moderate the requirement that, for each individual robot, there exists a control Lyapunov function. An example is provided that illustrates the soundness of the method.

We propose a model independent coordination strategy for multi-agent formation control. The main theorem states that under a bounded tracking error assumption our method stabilizes the formation error. We illustrate the usefulness of the method by applying it to rigid body constrained motions, as well as to mobile manipulation.

We study the interactions between symbolic computer programs and mechanical devices, e.g. mobile robots. We show that by using motion description languages for generating continuous motions from symbolic input strings, this interaction between the continuous and the discrete can be given a meaningful control theoretic interpretation. We furthermore illustrate how robot behaviors can be learned within this framework. We also investigate how to choose the motion description languages in order to minimize the lengths of the inputs to the robots.

We propose a model independent coordination strategy for multi-agent formation control. The main theorem states that under a bounded tracking error assumption, our method stabilizes the formation error. We illustrate the usefulness of the method by applying it to rigid body constrained motions.

A platform independent control approach for mobile manipulation and coordinated trajectory following is proposed and analyzed. Given a path for the gripper to follow, another path is planned for the base in such a way that it is feasible with respect to manipulability. The base and the end-effector then follow their respective reference trajectories according to proven stable, error-feedback control algorithms, while the base is placed in such a way that the end-effector trajectory always is within reach for the manipulator.

A solution to the trajectory tracking problem for mobile manipulators is proposed, that allows for the base to be influenced by a reactive, obstacle avoidance behavior. Given a trajectory for the gripper to follow, a tracking algorithm for the manipulator is designed, and at the same time the base motions are generated in such a way that the base is coordinated with the gripper. Furthermore, it is shown that the method allows arbitrary upper and lower bounds on the gripper-base distance to be set and this can be achieved without introducing deadlocks into the system. The solution also ensures that the control effort, spent on slow base motions, is kept small.

We present a scheme for mobile manipulation by introducing a mobile manipulation control architecture (MMCA). This architecture is motivated by a need for a systematic control structure for robotic manipulation within a behavior based framework. The control structure enables integration of the manipulator into a behavior based control structure for the platform. Furthermore, our suggested MMCA is designed in such a way that it supports design and performance analysis from both a manipulator dynamics and a hybrid automata perspective.

A solution to the problem of controlling a car-like nonholonomic robot is proposed using a "virtual" vehicle approach, which is shown to be robust with respect to errors and disturbances. The proposed algorithms are model independent, and the stability analysis is done using a dynamical model in which, for instance, the side slip angles are taken into account.