Flying quadrotors in tight formations is a challenging problem. It is known that in the near-field airflow of a quadrotor, the aerodynamic effects induced by the propellers are complex and difficult to characterize. Although machine learning tools can potentially be used to derive models that capture these effects, these data-driven approaches can be sample inefficient and the resulting models often do not generalize as well as their first-principles counterparts. In this work, we propose a framework that combines the benefits of first-principles modeling and data-driven approaches to construct an accurate and sample efficient representation of the complex aerodynamic effects resulting from quadrotors flying in formation. The data-driven component within our model is lightweight, making it amenable for optimization-based control design. Through simulations and physical experiments, we show that incorporating the model into a novel learning-based nonlinear model predictive control (MPC) framework results in substantial performance improvements in terms of trajectory tracking and disturbance rejection. In particular, our framework significantly outperforms nominal MPC in physical experiments, achieving a 40.1% improvement in the average trajectory tracking errors and a 57.5% reduction in the maximum vertical separation errors. Our framework also achieves exceptional sample efficiency, using only a total of 46 seconds of flight data for training across both simulations and physical experiments. Furthermore, with our proposed framework, the quadrotors achieve an exceptionally tight formation, flying with an average separation of less than 1.5 body lengths throughout the flight.

Traditionally, unmanned aerial vehicles (UAVs) rely on CMOS-based cameras to collect images about the world below. One of the most successful applications of UAVs is to generate orthomosaics or orthomaps, in which a series of images are integrated to develop a larger map. However, using CMOS-based cameras with global or rolling shutters means that orthomaps are vulnerable to challenging light conditions, motion blur, and high-speed motion of independently moving objects (IMOs) under the camera. Event cameras are less sensitive to these issues, as their pixels trigger asynchronously on brightness changes. This work introduces the first orthomosaic approach using event cameras. We focus on addressing high-dynamic range and low-light problems in orthomosaics. In contrast to existing methods relying only on CMOS cameras, our approach enables map generation even in challenging light conditions, including direct sunlight and after sunset. The source code for EvMAPPER, the high-altitude hardware, and the dataset collected in this paper are available open source11https://evmapper.fcladera.com.

This paper introduces a novel multiobjective human-in-the-loop planning algorithm for information-driven path planning. We formulate the path planning problem as a multiobjective orienteering problem, aiming to optimize multiple survey objectives under operational constraints. Inspired by Indicator-based Fitness Evaluation and Tabu Search, the algorithm efficiently predicts high-scoring paths, which are presented to a human expert for refinement of waypoints. Once data is collected at the next waypoint, the expert updates the objectives of the relevant points of interest, allowing for dynamic adjustments based on evolving survey requirements. Tailored for autonomous geological surveys, we validate our approach with real-world data from Sage Hen, CA. The proposed solver outperforms existing MOOP solvers by achieving better results with lower variance, resulting in improved survey path coverage.

Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support complex tasks must be robust to unreliable and intermittent communication links. In this work, we present MOCHA (Multi-robot Opportunistic Communication for Heterogeneous Collaboration), a framework for resilient multi-robot collaboration that enables large-scale exploration in the absence of continuous communications. MOCHA is based on a gossip communication protocol that allows robots to interact opportunistically whenever communication links are available, propagating information on a peer-to-peer basis. We demonstrate the performance of MOCHA through real-world experiments with commercial-off-the-shelf (COTS) communication hardware. We further explore the system’s scalability in simulation, evaluating the performance of our approach as the number of robots increases and communication ranges vary. Finally, we demonstrate how MOCHA can be tightly integrated with the planning stack of autonomous robots. We show a communication-aware planning algorithm for a high-altitude aerial robot executing a collaborative task while maximizing the amount of information shared with ground robots.The source code for MOCHA and the high-altitude UAV planning system is available open source1.

Planning time-optimal trajectories for quadrotors in cluttered environments is a challenging, non-convex problem. This paper addresses minimizing the traversal time of a given collision-free geometric path without violating actuation bounds of the vehicle. Previous approaches have either relied on convex relaxations that do not guarantee dynamic feasibility or have generated overly conservative time parametrizations. We propose TOPPQuad, a time-optimal path parameterization algorithm for quadrotors which explicitly incorporates quadrotor rigid body dynamics and constraints, such as bounds on inputs (including motor thrusts) and state of the vehicle (including the pose, linear and angular velocity and acceleration). We demonstrate the ability of the planner to generate faster trajectories that respect hardware constraints of the robot compared to planners with relaxed notions of dynamic feasibility in both simulation and hardware. We also demonstrate how TOPPQuad can be used to plan trajectories for quadrotors that utilize bidirectional motors. Overall, the proposed approach paves a way towards maximizing the efficacy of autonomous micro aerial vehicles while ensuring their safety.

Communication constraints can significantly impact robots’ ability to share information, coordinate their movements, and synchronize their actions, thus limiting coordination in Multi-Robot Exploration (MRE) applications. In this work, we address these challenges by modeling the MRE application as a DEC-POMDP and designing a joint policy that follows a rendezvous plan. This policy allows robots to explore unknown environments while intermittently sharing maps opportunistically or at rendezvous locations without being constrained by joint path optimizations. To generate the rendezvous plan, robots represent the MRE task as an instance of the Job Shop Scheduling Problem (JSSP) and minimize JSSP metrics. They aim to reduce waiting times and increase connectivity, which correlates to the DEC-POMDP rewards and time to complete the task. Our simulation results suggest that our method is more efficient than using relays or maintaining intermittent communication with a base station, being a suitable approach for Multi-Robot Exploration. We developed a proof-of-concept using the Robot Operating System (ROS) that is available at: https://github.com/multirobotplayground/Noetic-Multi-Robot-Sandbox.

State estimation is an important aspect in many robotics applications. In this work, we consider the task of obtaining accurate state estimates for robotic systems by enhancing the dynamics model used in state estimation algorithms. Existing frameworks such as moving horizon estimation (MHE) and the unscented Kalman filter (UKF) provide the flexibility to incorporate nonlinear dynamics and measurement models. However, this implies that the dynamics model within these algorithms has to be sufficiently accurate in order to warrant the accuracy of the state estimates. To enhance the dynamics models and improve the estimation accuracy, we utilize a deep learning framework known as knowledge-based neural ordinary differential equations (KNODEs). The KNODE framework embeds prior knowledge into the training procedure and synthesizes an accurate hybrid model by fusing a prior first-principles model with a neural ordinary differential equation (NODE) model. In our proposed LEARNEST framework, we integrate the data-driven model into two novel model-based state estimation algorithms, which are denoted as KNODE-MHE and KNODE-UKF. These two algorithms are compared against their conventional counterparts across a number of robotic applications; state estimation for a cartpole system using partial measurements, localization for a ground robot, as well as state estimation for a quadrotor. Through simulations and tests using real-world experimental data, we demonstrate the versatility and efficacy of the proposed learning-enhanced state estimation framework.

Modular self-assembling systems typically assume that modules are present to assemble. But in sparsely observed ocean environments modules of an aquatic modular robotic system may be separated by distances they do not have the energy to cross, and the information needed for optimal path planning is often unavailable. In this work we present a flow-based rendezvous and docking controller that allows aquatic robots in gyre-like environments to rendezvous with and dock to a target by leveraging environmental forces. This approach does not require complete knowledge of the flow, but suffices with imperfect knowledge of the flow's center and shape. We validate the performance of this control approach in both simulations and experiments relative to naive rendezvous and docking strategies and show that energy efficiency improves as the scale of the gyre increases.

The use of bidirectional propellers provides quadrotors with greater maneuverability which is advantageous in constrained environments. This paper addresses the development of a trajectory planning algorithm for quadrotors with bidirectional motors. Previous work has shown that the property of differential flatness can be leveraged for efficient trajectory planning. However, planners that leverage flatness for quadrotors fail at points where the acceleration of the center of mass is equal to gravity, i.e., when the vehicle experiences free fall. The central contribution of this paper is a flatness-based trajectory planning method that allows quadrotors to use bidirectional propellers and pass through the so-called free-fall singularity. We model our system as a differentially flat hybrid system with the aid of coordinate charts derived from the Hopf fibration and develop an algorithm that computes forward and reverse thrusts for each propeller, resulting in smooth trajectories everywhere in SE(3). We demonstrate the planner's versatility by planning knife-edge maneuvers and trajectories passing through the free-fall singularity, while transitioning from forward to reverse thrust.

The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despite attaining substantial performance improvements over their non-learning counterparts, many of these frameworks rely on an offline learning procedure to synthesize a dynamics model. This implies that uncertainties encountered by the robot during deployment are not accounted for in the learning process. On the other hand, learning-based MPC methods that learn dynamics models online are computationally expensive and often require a significant amount of data. To alleviate these shortcomings, we propose a novel learning-enhanced MPC framework that incorporates components from C1adaptive control into learning-based MPC. This integration enables the accurate compensation of both matched and unmatched uncertainties in a sample-efficient way, enhancing the control performance during deployment. In our proposed framework, we present two variants and apply them to the control of a quadrotor system. Through simulations and physical experiments, we demonstrate that the proposed framework not only allows the synthesis of an accurate dynamics model on-the-fly, but also significantly improves the closed-loop control performance under a wide range of spatio-temporal uncertainties.

With the rapidly changing climate and an increase in extreme weather events, it is necessary to have better methods to monitor and study the impacts of these phenomena on urban river environments. Multi-robot environmental monitoring has long focused on strategies that assign individual robots to distinct regions or task objectives. While these methods have seen success for Autonomous Surface Vehicles (ASVs), the spatial expanse and temporal variability of rivers impose an increased burden on existing techniques, necessitating computationally intensive replanning. Alternative methods aim to model and control teams of robots by prescribing global constraints on the system, using the insight that robots' transitions between tasks are stochastic and time-based. These methods do not require replanning because robots will perform different tasks achieving the overall desired system state, focusing on temporal switching alone limits their overall descriptive power. In this paper, we present a method that considers collaborations between robots to inform task switching based on spatial proximity. Our results suggest that in unknown environments macroscopic models provide increased flexibility for individual robot task execution as compared to coverage control methods.

Autonomous Marine Vehicles (AMVs) have gained interest for scientific and commercial applications, including pipeline and algae bloom monitoring, contaminant tracking, and ocean debris removal. The Team Orienteering Problem (TOP) is relevant in this context as Multi-Robot Systems (MRSs) allow for better coverage of the area of interest, simultaneous data collection at different locations, and an increase in the overall robustness and efficiency of the mission. However, route planning for AMVs in dynamic ocean environments is challenging due to the coupling of environmental and vehicle dynamics. We propose a multi-objective formulation that accounts for the trade-offs between visiting multiple task locations and energy consumption by the vehicles subject to a time budget. This work focuses on vehicles that can maintain a constant net speed but can be adapted to vehicles with constant thrust. Different from existing approaches, our method is able to leverage time-varying ocean currents to improve the energy efficiency of resulting routes. We validate our approach experimentally by superimposing ocean flow models with benchmark instances of the TOP.

Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a novel, generic, and accurate diffeomorphic image registration framework that utilizes neural ordinary differential equations (NODEs). We model each voxel as a moving particle and consider the set of all voxels in a 3D image as a high-dimensional dynamical system whose trajectory determines the targeted deformation field. Our method leverages deep neural networks for their expressive power in modeling dynamical systems, and simultaneously optimizes for a dynamical system between the image pairs and the corresponding transformation. Our formulation allows various constraints to be imposed along the transformation to maintain desired regularities. Our experiment results show that our method outperforms the benchmarks under various metrics. Additionally, we demonstrate the feasibility to expand our framework to register multiple image sets using a unified form of transformation, which could possibly serve a wider range of applications.

Understanding decentralized dynamics from collective behaviors in swarms is crucial for informing robot controller designs in artificial swarms and multi-agent robotic systems. However, the complexity in agent-to-agent interactions and the decentralized nature of most swarms pose a significant challenge to the extraction of single-robot control laws from collective behaviors. In this work, we consider the important task of learning decentralized single-robot controllers based solely on the state observations of a swarm's trajectory. We present a general framework by adopting knowledge-based neural ordinary differential equations (KNODE) ─ a hybrid machine learning method capable of combining artificial neural networks with known agent dynamics. Our approach distinguishes itself from most prior works in that we do not require action data for learning. We apply our framework to two different flocking swarms in 2D and 3D respectively, and demonstrate efficient training by leveraging the graphical structure of the swarms' information network. We further show that the learnt single-robot controllers can not only mimic flocking behavior in the original swarm but also scale to swarms with more robots.

We present a flow-based control strategy that enables resource-constrained marine robots to patrol gyre-like flow environments on an orbital trajectory with a periodicity in a given range. The controller does not require a detailed model of the flow field and relies only on the robot's location relative to the center of the gyre. Instead of precisely tracking a pre-defined trajectory, the robots are tasked to stay in between two bounding trajectories with known periodicity. Furthermore, the proposed strategy leverages the surrounding flow field to minimize control effort. We prove that the proposed strategy enables robots to cycle in the flow satisfying the desired periodicity requirements. Our method is tested and validated both in simulation and in experiments using a low-cost, underactuated, surface swimming robot, i.e. the Modboat.

In this paper, we present an online adaptive planning strategy for a team of robots with heterogeneous sensors to sample from a latent spatial field using a learned model for decision making. Current robotic sampling methods seek to gather information about an observable spatial field. However, many applications, such as environmental monitoring and precision agriculture, involve phenomena that are not directly observable or are costly to measure, called latent phenomena. In our approach, we seek to reason about the latent phenomenon in real-time by effectively sampling the observable spatial fields using a team of robots with heterogeneous sensors, where each robot has a distinct sensor to measure a different observable field. The information gain is estimated using a learned model that maps from the observable spatial fields to the latent phenomenon. This model captures aleatoric uncertainty in the relationship to allow for information theoretic measures. Additionally, we explicitly consider the correlations among the observable spatial fields, capturing the relationship between sensor types whose observations are not independent. We show it is possible to learn these correlations, and investigate the impact of the learned correlation models on the performance of our sampling approach. Through our qualitative and quantitative results, we illustrate that empirically learned correlations improve the overall sampling efficiency of the team. We simulate our approach using a data set of sensor measurements collected on Lac Hertel, in Quebec, which we make publicly available.

In many environmental monitoring applications robots are often tasked to visit various distinct locations to make observations and/or collect specific measurements. The problem of scheduling and assigning robots to the various tasks and planning feasible paths for the robots can be posed as an Orienteering Problem (OP). In the standard OP, routing and scheduling is achieved by maximizing an objective function by visiting the most rewarding locations while respecting a limited travel budget. However, traditional formulations for such problems usually neglect some environmental features that can greatly impact the tour, e.g., flows, such as wind or ocean currents. This is of particular importance for applications in marine and atmospheric environments where vehicle motions can be significantly impacted by the environmental dynamics and the environment exerts a non-negligible force on the vehicles. In this paper, we tackle the OP in fluid environments where robots must operate in the presence of ocean and/or atmospheric currents. We introduce a novel multi-objective formulation that combines both task and path planning problems, and whose goals are to (i) maximize the collected reward, while (ii) minimizing the energy expenditure by leveraging the environmental dynamics wherever possible. We validate our strategy using simulated ocean model data to show that our approach can generate a diverse set of solutions that have an adequate compromise between both objectives.

In this article, we propose an approach to construct a time-varying communication topology with a resilient consensus performance for robot swarms with limited communication ranges. The robots are deployed to explore a large task space and achieve consensus despite the existence of a finite number of noncooperative members in the team. Existing methods encouraged robots to stay close to each other to achieve certain robustness requirements on the connectivity of the communication topology. We leverage on the robots’ mobility to design a time-varying integrated topology composed of several subgroups of robots deployed on nonoverlapping closed-loop paths. Robots are spread out and move along the paths, forming periodic communication links within or across groups. We analyze the time-varying topology synthesized and provide sufficient conditions for individual subgroups and the interconnection between them. We show designs satisfying the conditions with simulated examples in a lattice space, as well as in a task space with predefined paths.

In this paper, we propose an evolutionary algorithm for solving the multi-robot orienteering problem where a team of cooperative robots aims to maximize the total information collected by visiting a subset of given nodes within a fixed budget on travel costs. Multi-robot orienteering problems are relevant to applications such as logistic delivery services, precision agriculture, and environmental sampling and monitoring. We consider the case where the information gain at each node is related to the service time each robot spends at the node. As such, we address a variant of the Orienteering Problem where the collected rewards are a function of the time a robot spends at a given location. We present a genetic algorithm solver to this cooperative Team Orienteering Problem with service-time dependent rewards. We evaluate the approach over a diverse set of node configurations and for different team sizes. Lastly, we evaluate the effects of team heterogeneity on overall task performance through numerical simulations.

This paper presents a synchronization controller for mobile sensors that are minimally actuated and can only communicate with each other over a very short range. This work is motivated by ocean monitoring applications where large-scale sensor networks consisting of drifters with minimal actuation capabilities, i.e., active drifters, are employed. We assume drifters are tasked to monitor regions consisting of gyre flows where their trajectories are periodic. As drifters in neighboring regions move into each other's proximity, it presents an opportunity for data exchange and synchronization to ensure future rendezvous. We present a nonlinear synchronization control strategy to ensure that drifters will periodically rendezvous and maximize the time they are in their rendezvous regions. Numerical simulations and small-scale experiments validate the efficacy of the control strategy and hint at extensions to large-scale mobile sensor networks.

We present a path planning strategy for a magnetic millirobot where the nonlinearities in the external magnetic force field (MFF) are encoded in the graph used for planning. The strategy creates a library of candidate MFFs and characterizes their topologies by identifying the unstable manifolds in the workspace. The path planning problem is then posed as a graph search problem where the computed path consists of a sequence of unstable manifold segments and their associated MFFs. By tracking the robot's position and sequentially applying the MFFs, the robot navigates along each unstable manifold until it reaches the goal. We discuss the theoretical guarantees of the proposed strategy and experimentally validate the strategy.

This work presents an asynchronous multi-robot adaptive sampling strategy through the synthesis of an intermittently connected mobile robot communication network. The objective is to enable a team of robots to adaptively sample and model a nonlinear dynamic spatiotemporal process. By employing an intermittently connected communication network, the team is not required to maintain an all-time connected network enabling them to cover larger areas, especially when the team size is small. The approach first determines the next meeting locations for data exchange and as the robots move towards these predetermined locations, they take measurements along the way. The data is then shared with other team members at the designated meeting locations and a reducedorder-model (ROM) of the process is obtained in a distributed fashion. The ROM is used to estimate field values in areas without sensor measurements, which informs the path planning algorithm when determining a new meeting location for the team. The main contribution of this work is an intermittent communication framework for asynchronous adaptive sampling of dynamic spatiotemporal processes. We demonstrate the framework in simulation and compare different reduced-order models under full, all-time and intermittent connectivity.

In this work, we present an information based exploration strategy tailored for the generation of high resolution 3D maps. We employ RGBD panoramas because they have been shown to provide memory efficient high quality representations of space. Robots explore the environment by selecting locations with maximal Cauchy-Schwarz Quadratic Mutual Information (CSQMI) computed on an angle enhanced occupancy grid to collect these RGBD panoramas. By employing the angle enhanced occupancy grid, the resulting exploration strategy emphasizes perspective in addition to binary coverage. Furthermore, the goal selection strategy is improved by using image morphology to reduce the search space over which CSQMI is computed. We present experimental results demonstrating the improved performance in perception related tasks by capturing panoramas using this approach, near frontier exploration, and a control of logging images at regular intervals while teleoperating the robot through the workspace. Collect imagery was passed through an object detection library with our perspective aware approach yielding a greater number of successful detections compared to near frontier exploration.

Uncertainties in flow models have to be explicitly considered for effective path planning in marine environments. In this paper, we present two methods to compute minimum expected cost policies and paths over an uncertain flow model. The first method based on a Markov Decision Process computes a minimum expected cost policy while the second graph search based method, computes a minimum expected cost path. A transition probability model is developed to compute the probability of transition from one state to another under a given action. In addition, a method to compute the expected cost of a path when it is executed in an uncertain flow field is also presented. The two methods are used to compute minimum energy paths in an ocean environment and the results are analyzed in simulations.

We present a differential geometric approach towards the synthesis of cooperative controllers for a team of autonomous surface vehicles transporting a buoyant load. We are interested in cooperative transport of large objects by teams of autonomous surface vehicles (ASVs) operating in marine and littoral environments. We consider the cooperative towing problem where individual ASVs connected to a load via cables must coordinate to transport the load along a desired trajectory. We present a differential geometric approach towards the synthesis of open and closed loop strategies for the team. The main advantage of the proposed strategy is the ability to synthesize agent-level controllers that can simultaneously satisfy all the holonomic and non-holonomic constraints within the system. We validate the approach in both simulations and experiments.

We present a novel framework for integrity analysis of swarm robotic systems using the symmetric Kullback-Leibler Divergence. The objective is to understand a robot swarm's vulnerability to malicious intrusion and to develop the necessary computational tools that would detect the presence of malicious agents within the swarm. Using ensemble approaches for modeling and analyzing stochastic task allocation, we analyze the performance of the proposed strategy subject to different system parameters, and show how different design choices can facilitate early intrusion detection. We further evaluate the performance of our method in realistic scenarios through stochastic simulations for different team sizes. The main contribution is an analysis framework whose output can be used to avoid system-inherent design flaws and to decrease the damage that can be inflicted by an undetected attacker.

This Special Issue draws six papers from the Thirteenth IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). SSRR is an international forum for furthering the study of key issues underpinning the research of safety, security, and rescue robotics as well as solutions necessary for the fielding of robots and sensor systems across a variety of challenging application areas. We are very pleased to have selected these subset of papers from an extremely strong technical program focused on automation themes for SSRR applications.

We consider the problem of distributing a large group of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, in which each species (robot type) is defined by the traits (capabilities) that it owns. In order to solve the distribution problem, we develop centralized as well as decentralized methods to efficiently control the heterogeneous swarm of robots. Our methods assume knowledge of the underlying task topology and are based on a continuous model of the system that defines transition rates to and from tasks, for each robot species. Our optimization of the transition rates is fully scalable with respect to the number of robots, number of species, and number of traits. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates as a function of the state of the current robot distribution. We also show how the robot distribution can be approximated based on local information only, consequently enabling the development of a decentralized controller. We evaluate our methods by means of microscopic simulations and show how the performance of the latter is well predicted by the macroscopic equations. Importantly, our framework also includes a diversity metric that enables an evaluation of the impact of swarm heterogeneity on performance. The metric defines the notion of minspecies, i.e., the minimum set of species that are required to achieve a given goal. We show that two distinct goal functions lead to two specializations of minspecies, which we term as eigenspecies and coverspecies. Quantitative results show the relation between diversity and performance.

We are interested in a principled study of the impact of diversity in heterogeneous large-scale distributed robotic systems. In order to evaluate the implications of heterogeneity on performance, we consider the concrete problem of distributing a large group of robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type) is defined by the traits (capabilities) that it owns. We develop a continuous model of the system at a macroscopic level, and formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. In order to evaluate the effects of heterogeneity, we propose a diversity metric that defines the notion of eigenspecies. We show that our metric correlates with performance: the higher the cardinality of the eigenspecies, the harder it becomes to optimize the system. Our approach is validated over multiple levels of abstraction, and real robot results confirm its validity on physical platforms.

We present a distributed control strategy that enables a swarm of autonomous surface vehicles (ASVs) to cooperatively grasp and manipulate a large floating object from an initial position and orientation to a desired final position and orientation. Given an initial set of random robot positions, our control strategy enables the team to synchronize their arrival times around the object. Analytical motion trajectories are then computed to enable the swarm to transport the object to the final desired position. We validate the proposed strategy in simulation and present experimental results to demonstrate the feasibility of the proposed strategy.

We present a new descriptor for tactile 3D object classification. It is invariant to object movement and simple to construct, using only the relative geometry of points on the object surface. We demonstrate successful classification of 185 objects in 10 categories, at sparse to dense surface sampling rate in point cloud simulation, with an accuracy of 77.5% at the sparsest and 90.1% at the densest. In a physics-based simulation, we show that contact clouds resembling the object shape can be obtained by a series of gripper closures using a robotic hand equipped with sparse tactile arrays. Despite sparser sampling of the object's surface, classification still performs well, at 74.7%. On a real robot, we show the ability of the descriptor to discriminate among different object instances, using data collected by a tactile hand.

Similar to the atmosphere, coherent structures, e.g., fronts, exist in the ocean. These frontal structures are known to be highly productive, supporting the whole spectrum of marine life. Ocean fronts are dynamic in time and space, and are a key component to a comprehensive knowledge of ocean dynamics and aquatic ecosystems in relation to climate change. However, dynamic features such as fronts are difficult to study through conventional oceanographic techniques. In this paper, we build upon our previous work in sampling and tracking an ocean front based on predictions and/or priors. Specifically, given a prior (that may not be accurate or up-to-date) we present and experimentally validate a method for an autonomous surface or underwater vehicle to plan a mission and adapt this mission on-the-go to track a dynamic, but coherent, structure. Experimental results using a novel indoor testbed, capable of creating controllable fluidic features in an indoor laboratory setting, are presented. These results demonstrate that the vehicle is able to adapt its path to follow a desired, time-varying contour.

In this paper, we present a first attempt toward experimental validation of a multi-robot strategy for tracking manifolds and Lagrangian coherent structures (LCS) in flows. LCS exist in natural fluid flows at various scales, and they are time-varying extensions of stable and unstable manifolds of time invariant dynamical systems. In this work, we present the first steps toward experimentally validating our previously proposed real-time manifold and LCS tracking strategy that relies solely on local measurements. Although we have validated the strategy in simulations using analytical flow models, experimental flow data, and actual ocean data, the strategy has never been implemented on an actual robotic platform. We demonstrate the tracking strategy using a team of micro autonomous surface vehicles (mASVs) in our laboratory testbed and investigate the feasibility of the strategy with vehicles operating in an actual fluid environment. Our experimental results show that the team of mASVs can successfully track LCS using a simulated velocity field, and we present preliminary results showing the feasibility of a team of mASVs tracking manifolds in real flows using only local measurements obtained from their onboard flow sensors.

Lagrangian coherent structures (LCSs) are separatrices that delineate dynamically distinct regions in general dynamical systems and can be viewed as the extensions of stable and unstable manifolds to general time-dependent systems. Identifying LCS in dynamical systems is useful for many applications, including oceanography and weather prediction. In this paper, we present a collaborative robotic control strategy that is designed to track stable and unstable manifolds in dynamical systems, including ocean flows. The technique does not require global information about the dynamics, and is based on local sensing, prediction, and correction. The collaborative control strategy is implemented with a team of three robots to track coherent structures and manifolds on static flows, a time-dependent model of a wind-driven double-gyre flow often seen in the ocean, experimental data that are generated by a flow tank, and actual ocean data. We present simulation results and discuss theoretical guarantees of the collaborative tracking strategy.

Tracking Lagrangian coherent structures in dynamical systems is important for many applications such as oceanography and weather prediction. In this paper, we present a collaborative robotic control strategy designed to track stable and unstable manifolds. The technique does not require global information about the fluid dynamics, and is based on local sensing, prediction, and correction. The collaborative control strategy is implemented on a team of three robots to track coherent structures and manifolds on static flows as well as a noisy time-dependent model of a wind-driven double-gyre often seen in the ocean. We present simulation and experimental results and discuss theoretical guarantees of the collaborative tracking strategy.

We present an ensemble framework for the design of distributed control and communication strategies for the dynamic allocation of a team of robots to a set of tasks. In this work, we assume individual robot controllers are sequentially composed of individual task controllers. This assumption enables the representation of the robot ensemble dynamics as a class of stochastic hybrid systems that can be modeled as continuous-time Markov jump processes where feedback strategies can be derived to control the team's distribution across the tasks. Since the distributed implementation of these feedback strategy requires the estimation of certain population variables, we show how the ensemble model can be expanded to incorporate the dynamics of the information exchange. This then enables us to optimize the individual robot control policies to ensure overall system robustness given some likelihood of resource failures. We consider the assignment of a team of homogeneous robots to a collection of spatially distributed tasks and validate our approach via high-fidelity simulations.

We address the distributed assembly of a structure by a team of homogeneous robots. We present an algorithm to partition 2- and 3-D assembly tasks into N separate subtasks that satisfy local and global precedence constraints between the assembly components. The objective is to achieve a partitioning that minimizes the workload imbalance between the robots and maximizes assembly parallelization. The algorithm consists of three phases: 1) an initial allocation of the subtasks to each robot via a variant of Dijkstra's algorithm; 2) a component trading protocol to balance each robot's workload without violating any constraints; and 3) the creation of an assembly plan for each robot that minimizes conflicts during execution. We present simulation results for three variants of the algorithm and experiments using our multi-robot testbed.

We present an investigation of dynamic team formation strategies for robot ensembles performing a collection of single and two-robot tasks. Specifically, we consider the abstract “stick and pebble” problem, as a variation of the “stick pulling” problem discussed in the literature. We present a formulation of the dynamic team formation problem that is independent of ensemble size and develop a macroscopic analytical description of the ensemble dynamics. The macroscopic model is then used to determine the optimal teaming strategy for two different performance metrics. We present agent-based simulation results to support the validity of our macroscopic analysis.

We present an investigation of specialization when considering the execution of collaborative tasks by a robot swarm. Specifically, we consider the stick-pulling problem first proposed by Martinoli et al. [1], [2] and develop a macroscopic analytical model for the swarm executing a set of tasks that require the collaboration of two robots. We show, for constant external conditions, maximum productivity can be achieved by a single species swarm with carefully chosen operational parameters. While the same applies for a two species swarm, we show how specialization is a strategy best employed for changing external conditions.

We present a scalable approach to dynamically allocating a swarm of homogeneous robots to multiple tasks, which are to be performed in parallel, following a desired distribution. We employ a decentralized strategy that requires no communication among robots. It is based on the development of a continuous abstraction of the swarm obtained by modeling population fractions and defining the task allocation problem as the selection of rates of robot ingress and egress to and from each task. These rates are used to determine probabilities that define stochastic control policies for individual robots, which, in turn, produce the desired collective behavior. We address the problem of computing rates to achieve fast redistribution of the swarm subject to constraint(s) on switching between tasks at equilibrium. We present several formulations of this optimization problem that vary in the precedence constraints between tasks and in their dependence on the initial robot distribution. We use each formulation to optimize the rates for a scenario with four tasks and compare the resulting control policies using a simulation in which 250 robots redistribute themselves among four buildings to survey the perimeters.

We present a decentralized approach to multi- robot manipulation where the team of robots surround and trap an object and transport it, by dragging or pushing, to the goal configuration in an environment with obstacles. The proposed feedback controllers are obtained by sequentially composing vector fields or behaviors and are decentralized in the sense that robots do not exchange each other's state information. Rather, cooperative manipulation is achieved by relying solely on each robot's local information and a global knowledge of the task. We present computer simulations and experimental results obtained using our multi-robot testbed.

We develop decentralized controllers for a team of disk-shaped robots to converge to and circulate along the boundary of a desired two-dimensional geometric pattern specified by a smooth function with collision avoidance. The proposed feedback controllers rely solely on each robot's range and bearing sensors which allow them to obtain information about positions of neighbors within a given range. This is relevant for applications such as perimeter surveillance or containing hazardous regions where limited bandwidth must be preserved for situational awareness. The computational complexity of the decentralized controller for each agent is linear in the number of neighboring agents, making it scalable to robot swarms. We establish stability and convergence properties of the controllers and verify the feasibility of the method through computer simulations.

In this paper, we present an optimization framework for target tracking with mobile robot teams. The target tracking problem is modeled as a generic semidefinite program (SDP). When paired with an appropriate objective function, the solution to the resulting problem instance yields an optimal robot configuration for target tracking at each time-step, while guaranteeing target coverage (each target is tracked by at least one robot) and maintaining network connectivity. Our methodology is based on the graph theoretic result where the second smallest eigenvalue of the interconnection graph Laplacian matrix is a measure for the connectivity of the graph. This formulation enables us to model agent-target coverage and inter-agent communication constraints as linearmatrix inequalities.We also show that when the communication constraints can be relaxed, the resulting problem can be reposed as a second-order cone program (SOCP) which can be solved significantly more efficiently than its SDP counterpart. Simulation results for a team of robots tracking multiple targets are presented.

We present an approach for the dynamic assignment and reassignment of a large team of homogeneous robotic agents to multiple locations with applications to search and rescue, reconnaissance and exploration missions. Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between multiple candidate nests. We design stochastic control policies that enable the team of agents to distribute themselves between multiple candidate sites in a specified ratio. Additionally, we present an extension to our model to enable fast convergence via switching behaviors based on quorum sensing. The stability and convergence properties of these control policies are analyzed and simulation results are presented.

Communication is essential for coordination in most cooperative control and sensing paradigms. In this paper, we present an experimental study of strategies for maintaining end-to-end communication links for tasks such as surveillance and search and rescue where team connectivity is essential for providing situational awareness to a base station. We consider the differences between monitoring point-to-point signal strength versus data throughput and present experimental results with our multi-robot testbed in outdoor environments

We develop decentralized controllers for a swarm of robots to generate a desired two-dimensional geometric pattern specified by a smooth function while maintaining specified relative distance constraints. The controllers are decentralized in the sense that the robots do not exchange or sense each other's state information. However, we assume that the robots have range sensors allowing them to obtain information about distances to neighbors within a known range. We establish stability and convergence properties of the controllers

Communication is essential for coordination in most cooperative control and sensing paradigms. In this paper, we investigate the construction of a map of radio signal strength that can be used to plan multirobot tasks and also serve as useful perceptual information. We show how nominal models of an urban environment, such as those obtained by aerial surveillance, can be used to generate strategies for exploration and present preliminary experimental results with our multi-robot testbed.