This article presents a method for cave surveying in total darkness using an autonomous aerial vehicle equipped with a depth camera for mapping, downward-facing camera for state estimation, and forward and downward lights. Traditional methods of cave surveying are labor-intensive and dangerous due to the risk of injury when operating in darkness, and the potential structural instability of the subterranean environment. Although these dangers can be mitigated by deploying robots to map dangerous passages and voids, real-time feedback is often needed to operate robots safely and efficiently. Few state-of-the-art, high-resolution perceptual modeling techniques attempt to reduce their high bandwidth requirements to work well with low bandwidth communication channels. To bridge this gap in the state-of- the-art, this work compactly represents sensor observations as Gaussian mixture models and maintains a local occupancy grid map for a motion planner that greedily maximizes an information-theoretic objective function. The approach accommodates both limited field of view (FoV) depth cameras and larger FoV LiDAR sensors and is extensively evaluated in long duration simulations on an embedded PC. An aerial system is leveraged to demonstrate the repeatability of the approach in a flight arena as well as the effects of communication dropouts. Finally, the system is deployed in Laurel Caverns, a commercially owned and operated cave in southwestern Pennsylvania, USA, and a wild cave in West Virginia, USA. Videos of the simulation and hardware results are available athttps://youtu.be/iwi3p7IENjEandhttps://youtu.be/H8MdtJ5VhyU.

Volumetric objectives for exploration and perception tasks seek to capture a sense of value (or reward) for hypothetical observations at one or more camera views for robots operating in unknown environments. For example, a volumetric objective may reward robots proportionally to the expected volume of unknown space to be observed. We identify connections between existing information-theoretic and coverage objectives in terms of expected coverage, particularly that mutual information without noise is a special case of expected coverage. Likewise, we provide the first comparison, of which we are aware, between information-based approximations and coverage objectives for exploration, and we find, perhaps surprisingly, that coverage objectives can significantly outperform information-based objectives in practice. Additionally, the analysis for information and coverage objectives demonstrates that Randomized Sequential Partitions—a method for efficient distributed sensor planning—applies for both classes of objectives, and we provide simulation results in a variety of environments for as many as 32 robots.

This paper enhances the feedback linearization controller for multirotors with a learned acceleration error model and a thrust input delay mitigation model. Feedback linearization controllers are theoretically appealing but their performance suffers on real systems, where the true system does not match the known system model. We take a step in reducing these robustness issues by learning an acceleration error model, applying this model in the position controller, and further propagating it forward to the attitude controller. We show how this approach improves performance over the standard feedback linearization controller in the presence of unmodeled dynamics and repeatable external disturbances in both simulation and hardware experiments. We also show that our thrust control input delay model improves the step response on hardware systems.

In human-in-the-loop navigation, the operator’s intention is to locally avoid obstacles while planning long-horizon paths in order to complete the navigation task. We propose a hierarchical teleoperation framework that captures these characteristics of intention, and generates trajectories that are locally safe and follow the operator’s global plan. The hierarchical teleoperation framework consists of 1) a global path which encapsulates the intended direction of the operator, 2) local trajectories that circumvent obstacles near the vehicle’s vicinity while following the global path, and 3) safety monitoring to avoid possible imminent collisions. By removing the operator from providing dynamic-level control inputs and instead having inputs inform trajectory generation, we show a significant reduction of the operator’s engagement while maintaining smooth performance.We showcase hierarchical teleoperation in navigation tasks in a random forest environment and a high-clutter warehouse characterized by narrow gaps and dense obstacles. With our method, we maintain consistent high speed throughout the task with smooth jerk profiles, decreased time to completion, and significantly reduced operator engagement.

In multi-robot multi-target tracking, robots coordinate to monitor groups of targets moving about an environment. We approach planning for such scenarios by formulating a receding-horizon, multi-robot sensing problem with a mutual information objective. Such problems are NP-Hard in general. Yet, our objective is submodular which enables certain greedy planners to guarantee constant-factor suboptimality. However, these greedy planners require robots to plan their actions in sequence, one robot at a time, so planning time is at least proportional to the number of robots. Solving these problems becomes intractable for large teams, even for distributed implementations. Our prior work proposed a distributed planner (RSP) which reduces this number of sequential steps to a constant, even for large numbers of robots, by allowing robots to plan in parallel while ignoring some of each others’ decisions. Although that analysis is not applicable to target tracking, we prove a similar guarantee, that RSP planning approaches performance guarantees for fully sequential planners, by employing a novel bound which takes advantage of the independence of target motions to quantify effective redundancy between robots’ observations and actions. Further, we present analysis that explicitly accounts for features of practical implementations including approximations to the objective and anytime planning. Simulation results—available via open source release—for target tracking with ranging sensors demonstrate that our planners consistently approach the performance of sequential planning (in terms of position uncertainty) given only 2–8 planning steps and for as many as 96 robots with a 24x reduction in the number of sequential steps in planning. Thus, this work makes planning for multi-robot target tracking tractable at much larger scales than before, for practical planners and general tracking problems.

Safe high-speed autonomous navigation for MAVs in unknown environments requires fast planning to enable the robot to adapt and react quickly to incoming information about obstacles within the world. Furthermore, when operating in environments not known a priori, the robot may make decisions that lead to dead ends, necessitating global replanning through a map of the environment outside of a local planning grid. This work proposes a computationally-efficient planning architecture for safe high-speed operation in unknown environments that incorporates a notion of longer-term memory into the planner enabling the robot to accurately plan to locations no longer contained within a local map. A motion primitive-based local receding horizon planner that uses a probabilistic collision avoidance methodology enables the robot to generate safe plans at fast replan rates. To provide global guidance, a memory-efficient sparse topological graph is created online from a time history of the robot's path and a geometric notion of visibility within the environment to search for alternate pathways towards the desired goal if a dead end is encountered. The safety and performance of the proposed planning system is evaluated at speeds up to 10m/s, and the approach is tested in a set of large-scale, complex simulation environments containing dead ends. These scenarios lead to failure cases for competing methods; however, the proposed approach enables the robot to safely reroute and reach the desired goal.

We consider the kinodynamic multi-robot planning problem in cluttered 3-D workspaces. Reachability analysis on position invariant geometric trees is leveraged to find kino- dynamically feasible trajectories for the multi-robot team from potentially non-stationary initial states. The key contribution of our approach is that a collision-free geometric solution guarantees a kinodynamically feasible, safe solution without additional refinement. Simulation results with up-to 40 robots and hardware results with 5 robots suggest the viability of the proposed approach for online planning and replanning for large teams of aerial robots in cluttered 3-D workspaces.

This work addresses planning for multi-robot formations online in cluttered environments via a data-driven search approach. The user-specified objective function governing formation shape and rotation is expressed in terms of offline demonstrations of robot motions (performed in an obstacle free environment). We leverage the offline demonstration to inform online planning for coordinated motions in the presence of obstacles. We formulate planning as a discrete search over demonstrated multi-robot actions, and select actions using a best-first approach to minimize edge expansions for fast online operation. Actions are selected using a heuristic based on their probability distribution exhibited in the demonstration, and we show that this approach is able to recreate coordinated motions exhibited in the demonstration when navigating in the obstructed conditions of the cluttered test environments. We demonstrate results in simulation over environments with increasing numbers of obstacles, and show that resulting plans are collision free and obey dynamic constraints.

This work addresses the problem of learning a model of a dynamic environment using many independent Hidden Markov Models (HMMs) with a limited number of observations available per iteration. Many techniques exist to model dynamic environments, but do not consider how to deploy robots to build this model. Additionally, there are many techniques for exploring environments that do not consider how to prioritize regions when resources, in terms of robots to deploy and deployment durations, are limited. Here, we consider an environment model consisting of a series of HMMs that evolve over time independently and can be directly observed. At each iteration, we must determine which HMMs to observe in order to maximize the gain in model accuracy. We present a utility measure that balances a Pearson's χ2goodness-of-fit of the dynamics model with Mutual Information (MI) to ensure that observations are allocated to maximize the convergence rate of all HMMs, resulting in a faster convergence to higher steady-state model confidence and accuracy than either χ2or MI alone.

We present a method to assist the operator in teleoperation of mobile robots by generating trajectories such that the vehicle completes the desired task with ease in unstructured environments. Traditional assisted teleoperation methods have focused on reactive methods to avoid collisions, but neglect the operator's intention in doing so. Instead, we generate long horizon, smooth trajectories that follow the operator's intended direction while circumventing obstacles for a seamless teleoperation experience. For mobile robot teleoperation, an explicit goal in the state space is often unclear in cases such as exploration or navigation. Therefore, we model the intent as a direction and encode it as a cost function. As trajectories of various lengths can satisfy the same directional objective, we iteratively construct a tree of sequential actions that form multiple trajectories along the intended direction. We show our algorithm on a real-time teleoperation task of a simulated hexarotor vehicle in a dense random forest environment. By doing so, our approach allows operator to achieve the navigation task while requiring less effort than reactive methods.

In this work, we consider the problem of online centralized kinodynamic multi-robot replanning (from potentially non-stationary initial states) and coordination in known and cluttered workspaces. Offline state lattice reachability analysis is leveraged to decouple the planning problem into two sequential graph searches-one in the explicit geometric graph of the environment and the other in the graph of the higher-order derivatives of the robot's state-in a manner such that the intermediate vertices of a safe set of geometric paths are guaranteed to have a feasible assignment of higher-order derivatives. Without additional iterative refinement procedures, the resulting time parameterized polynomial trajectories are dynamically feasible and collision-free. Planning results with up to 20 robots in two and three dimensional workspaces suggest the suitability of the proposed approach for multi-robot replanning in known environments.

State-of-the-art forward facing monocular visual-inertial odometry algorithms are often brittle in practice, especially whilst dealing with initialisation and motion in directions that render the state unobservable. In such cases having a reliable complementary odometry algorithm enables robust and resilient flight. Using the common local planarity assumption, we present a fast, dense, and direct frame-to-frame visual-inertial odometry algorithm for downward facing cameras that minimises a joint cost function involving a homography based photometric cost and an IMU regularisation term. Via extensive evaluation in a variety of scenarios we demonstrate superior performance than existing state-of-the-art downward facing odometry algorithms for Micro Aerial Vehicles (MAVs).

This paper proposes a method to deploy teams of robots with constrained energy capacities to persistently maintain a map of an uncertain environment. Typical occupancy map approaches assume a static world; however, we introduce a decay in confidence that degrades the occupancy probability of grid cells and promotes revisitation. Further, sections of the map whose occupancy differs between observations are visited more frequently, while unchanging areas are scheduled less frequently. While naive planning is intractable through the entire space of multi-agent spatio-temporal states, the proposed algorithm decouples planning such that constraints are resolved separately by solving tracTable subproblems. We evaluate this approach in simulation and show how the uncertainty of our world model is maintained below an acceptable threshold while the algorithm retains a tractable computation time.

This paper presents a probabilistic environment representation that allows efficient high-fidelity modeling and inference toward enabling informed planning (active perception) on a computationally constrained mobile autonomous system. The proposed approach exploits the fact that real-world environments inherently possess structure that introduces dependencies between spatially distinct locations. Gaussian mixture models are employed to capture these structural dependencies and learn a semiparametric, arbitrary resolution spatial representation. A hierarchy of spatial models is proposed to enable a multifidelity representation with the variation in fidelity quantified via information-theoretic measures. Crucially for active perception, the proposed modeling approach enables a distribution over occupancy with an associated measure of uncertainty via incorporation of free space information. Evaluation of the proposed technique via a real-time graphics processing unit based implementation is presented on real-world data sets in diverse environments. The proposed approach is shown to perform favorably as compared to state-of-the-art occupancy mapping techniques in terms of memory footprint, prediction accuracy, and generalizability to structurally diverse environments.

Size, weight, and power constrained platforms impose constraints on computational resources that introduce unique challenges in implementing localization algorithms. We present a framework to perform fast localization on such platforms enabled by the compressive capabilities of Gaussian Mixture Model representations of point cloud data. Given raw structural data from a depth sensor and pitch and roll estimates from an on-board attitude reference system, a multi-hypothesis particle filter localizes the vehicle by exploiting the likelihood of the data originating from the mixture model. We demonstrate analysis of this likelihood in the vicinity of the ground truth pose and detail its utilization in a particle filter-based vehicle localization strategy, and later present results of real-time implementations on a desktop system and an off-the-shelf embedded platform that outperform localization results from running a state-of-the-art algorithm on the same environment.

In unknown, cluttered environments, robots require online real-time mapping and collision checking in order to navigate robustly. Discrete map representations are inefficient for collision checking as they are expensive in terms of memory and computation. This paper takes a probabilistic approach to local mapping by representing the environment as a Gaussian Mixture Model (GMM) and leverages its geometric properties to enable efficient collision checking given a time-parameterized trajectory. In contrast to current discretization-based methods, a GMM preserves geometric coverage of the environment without losing representation accuracy with varying map resolutions. We introduce a novel GMM local mapping algorithm that can be used with a single depth camera processed on a single CPU, and provide algorithms for collision avoidance given arbitrary trajectory representations. Finally, we provide experimentation results demonstrating safety, efficiency, and data coverage for real-time collision avoidance with a quadrotor navigating in a cluttered environment.

3D radiation source localization is a common task across applications such as decommissioning, disaster response, and security, but traditional count-based sensors struggle to efficiently disambiguate between symmetries in sensor, source, and environment configurations. Recent works have demonstrated successful passive source localization using a bearing sensor called the Compton gamma camera that can image radiation. This paper first presents an approach to mapping the spatial distribution of radiation with a gamma camera to estimate source locations. An active source localization framework is then developed that greedily selects new waypoints that maximize the Fisher Information provided by the camera's range and bearing observations for source localization. Finally the common assumption of a static step size in between waypoints is relaxed to allow step sizes to adapt online to the observed information. The proposed radiation mapping approach is evaluated in 5×4 m2and 14×6 m2laboratory environments, where multiple point sources were localized to within an average of 0.26 m or 0.6% of the environment dimensions. The active source localization approach is evaluated in simulation and an adaptive step size yields a 27% decrease in the localization time and a 16% decrease in the distance traveled to localize a source in a 15×15×15 m3environment.

We consider the problem in which a supervisor or remote pilot provides a real-time linear velocity reference to a multirotor aerial robot, either through a traditional remote control handset, a modern haptic interface, or semi-autonomous guidance control system. In all such cases, the goal is to servo-control the vehicle's velocity to the set point as quickly and as efficiently as possible. The challenge is to achieve this robustly in the presence of unknown wind disturbances and in situations in which the vehicle moves into global position system (GPS) denied environments (indoors, urban canyons, forests) where estimation of the vehicle's velocity is challenging. These situations include unclutterred environments, poor visibility environments caused by poor lighting, and poorly textured visual environments where laser- and vision-based sensors become unreliable. The approach taken is to develop a coupled nonlinear complementary velocity aided attitude filter that provides estimates of both the inertial and body-fixed frame linear velocities, as well as the attitude of a multirotor aerial vehicle, that functions effectively even when only the inertial measurement unit and barometric sensor measurements are available. When full inertial velocity measurements are available (from GPS, Vicon, or a vision system), the filter additionally estimates the external wind speed. In this paper, we formally present the proposed filter along with experimental results and a comparison of the filter to recent results in the literature and in situations in which inertial reference frame velocities are available intermittently. The proposed filter is computationally simple to implement, easy to calibrate and tune, and provides an excellent base level functionality for modern multirotor aerial robotic systems that will be required to function robustly in a variety of environments.

This work considers estimation of mass parameters for multi-robot coordinated lifting in the context of coordinated aerial manipulation, and develops strategies for active parameter estimation for cooperative manipulation tasks through an information-theoretic framework. The active sensing problem is formulated based on application of increasing forces to the object and detection of small motions that occur when the center of pressure exits the convex hull formed by existing contacts. In order to enable identification of informative actions, we develop and employ a closed-form solution of Cauchy-Schwarz quadratic mutual information (Ics) for non-parametric filters. The evaluation considers iterative selection from a finite set of measurements and demonstrates that choosing measurements to maximize Icssignificantly improves the convergence rate of the parameter estimates compared to random and cyclic selection methods. This approach is extended to consider actuator constraints and feasible lifting configurations and achieves an 80% success rate in formation of feasible lifting configurations compared to a 53% baseline performance.

This work presents Experience-driven Predictive Control (EPC) as a fast technique for solving nonlinear model predictive control (NMPC) problems with uncertain system dynamics. EPC leverages an affine dynamics model that is updated online via Locally Weighted Projection Regression (LWPR) to capture nonlinearities, uncertainty, and changes in the system dynamics. This model enables the NMPC problem to be re-cast as a quadratic program (QP). The QP can then be solved via multi-parametric techniques to generate a mapping from state, reference, and dynamics model to a locally optimal, affine feedback control law. These mappings, in conjunction with the basis functions learned via LWPR, define a notion of experience for the controller as they capture the full input-output relationship for previous actions the controller has taken. The resulting experience database allows EPC to avoid solving redundant optimization problems, and as it is constructed online, enables the system to operate more efficiently over time. We demonstrate the performance of EPC through a set of hardware-in-the-loop simulation studies of a quadrotor micro air vehicle that is subjected to unmodeled exogenous perturbations.

We propose a task-independent adaptive teleoperation methodology that seeks to improve operator performance and efficiency by concurrently modeling user intent and adapting the set of available actions according to the predicted intent. User input selects a robot motion from a finite set of dynamically feasible and safe motions, represented as a motion primitive library. User intent is modeled as a probabilistic distribution with respect to future actions that represents the likelihood of action selection given recent user input, which can be formulated independent of task, environment, or user. As the intent model becomes increasingly confident, the action set is adapted in order to reduce the error between the intended and actual performance. Experimental evaluation of teleoperating a quadrotor for nonaggressive, single-intent maneuvers such as following a racetrack and conducting a free-hand helix motion shows improved performance, validating that the approach provides efficient adaptation towards achieving the user intent.

In this work, we consider the problem of fast, accurate control of a robot with constrained dynamics. We present a new nonlinear model predictive control (MPC) technique, Nonlinear Partial Enumeration (NPE), that combines online and offline computation in a nonlinear version of the partial enumeration method for MPC, thereby dramatically decreasing the compute time per control iteration. We apply NPE to the problem of MAV flight and demonstrate through a set of simulation trials that NPE outperforms other fast control methodologies during aggressive motion and enables the system to learn a reusable set of local feedback controllers that enable more efficient operation over time.

This paper presents a fast approximate multi-agent decision theoretic planning method extended from the well-known Markov Decision Process (MDP). Our objective is to plan motions for a team of homogeneous micro air vehicles (MAVs) toward a set of goals, such that each MAV at any state at any moment follows an action policy toward a unique goal, while considering action uncertainty. We pursue an efficient formulation by first considering a deterministic abstraction of the stochastic system based on approximate initial paths. These deterministic and decoupled sub-problems are converted to the stochastic domain and improved by individual agents or a subset of agents. The resulting decoupled formulation requires processing of a partial state space and enables online operation given applications with emerging tasks.

We consider the problem of generating dynamically feasible and safe plans for teams of aerial robots (quadrotors) while holding a fixed relative formation as well as transitioning between a sequence of formations. We extend the existing assignment and planning approaches for quadrotor teams to find minimal-time trajectories to enable team transition between non-rest initial and ending states while ensuring dynamic feasibility with respect to predefined kinematic, dynamic, and collision constraints. This work also presents a method for safe splitting and merging of robot formations according to input specification. The proposed methodology is capable of generating dynamically feasible and safe plans for teams of quadrotors in real time. We validate the performance of the proposed approach through various trials and scenarios conducted in simulation.

This paper presents an online optimization-based approach to compute trajectories to enable substitution of robots in formation-based deployments with durations that exceed the energy capacity of individual systems. The proposed algorithm computes trajectories in a multi-robot context to ensure a collision-free exchange, even where congestion is a concern. The quality of the resulting trajectories is determined by the amount of time spent deviating from the original plan while maintaining collision-free, speed-limited polynomial splines. The algorithm is shown through simulation and experiments to be viable with average deviation time gaps of less than 16 seconds and average computation times of under 3 minutes for the presented scenarios with varying numbers of robots and deployment specifications.

This paper presents a real-time, kinodynamic planning and information-theoretic exploration framework that enables high-resolution mapping of three-dimensional environments featuring complex concavities and disjoint objects. The proposed approach targets planetary exploration applications and seeks to achieve real-time operation on computationally constrained systems while ensuring energy-efficient information acquisition. Trajectories are selected by maximizing a measure of information gain per an expected execution cost (e.g., time or energy). The proposed trajectory generation formulation is based on state-lattice motion primitives and evaluation of the Cauchy-Schwarz quadratic mutual information (CSQMI) at each lattice state. An expanded search structure is proposed that extends the state-lattice to a finite horizon to enable expansive space coverage while remaining real-time viable. Additionally, compression techniques are employed to reduce the computational burden associated with the CSQMI calculation over expansive environments while preserving fidelity. The performance of the proposed methodology is evaluated through simulated exploration of a three-dimensional terrestrial pit environment by a quadrotor aerial robot which acts as a surrogate for a propulsive vehicle when operating on an airless body.

We develop a computationally efficient control policy for active perception that incorporates explicit models of sensing and mobility to build 3D maps with ground and aerial robots. Like previous work, our policy maximizes an information-theoretic objective function between the discrete occupancy belief distribution (e.g., voxel grid) and future measurements that can be made by mobile sensors. However, our work is unique in three ways. First, we show that by using Cauchy-Schwarz Quadratic Mutual Information (CSQMI), we get significant gains in efficiency. Second, while most previous methods adopt a myopic, gradient-following approach that yields poor convergence properties, our algorithm searches over a set of paths and is less susceptible to local minima. In doing so, we explicitly incorporate models of sensors, and model the dependence (and independence) of measurements over multiple time steps in a path. Third, because we consider models of sensing and mobility, our method naturally applies to both ground and aerial vehicles. The paper describes the basic models, the problem formulation and the algorithm, and demonstrates applications via simulation and experimentation.

We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment using indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop closures by incorporating robot pose uncertainty, which significantly improves the trajectory accuracy over long-term navigation. An EM and hypothesis based method is used to determine a common reference frame. We detail a 2D laser scan correspondence method to form robust correspondences between laser scans shared amongst robots. The implementation is experimentally validated using teams of aerial vehicles, and analyzed to determine its accuracy, computational efficiency, scalability to many robots, and robustness to varying environments. We demonstrate through multiple experiments that our method can efficiently build maps of large indoor and outdoor environments in a distributed, online, and real-time setting.

In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by refueling at depots. We also present a heuristic method to allow us to compute bounded suboptimal results in real time. The results produced by our algorithm will allow a team of robots to efficiently cover a given set of targets or tasks persistently over long periods of time, even when the cost to transition between tasks is dynamic.

There have been increasing interests in the robotics community in building smaller and more agile autonomous micro aerial vehicles (MAVs). In particular, the monocular visual-inertial system (VINS) that consists of only a camera and an inertial measurement unit (IMU) forms a great minimum sensor suite due to its superior size, weight, and power (SWaP) characteristics. In this paper, we present a tightly-coupled nonlinear optimization-based monocular VINS estimator for autonomous rotorcraft MAVs. Our estimator allows the MAV to execute trajectories at 2 m/s with roll and pitch angles up to 30 degrees. We present extensive statistical analysis to verify the performance of our approach in different environments with varying flight speeds.

We propose the Stochastic Multi-Robot Persistent Coverage Problem (SMRPCP) and correspondant methodology to compute an optimal schedule that enables a fleet of energy-constrained unmanned aerial vehicles to repeatedly perform a set of tasks while maximizing the frequency of task completion and preserving energy reserves via recharging depots. The approach enables online modeling of uncertain task costs and yields a schedule that adapts according to an evolving energy expenditure model. A fast heuristic method is formulated that enables online generation of a schedule that concurrently maximizes task completion frequency and avoids the risk of individual robot energy-depletion and consequential platform failure. Failure mitigation is introduced through a recourse strategy that routes robots based on acceptable levels of risk. Simulation and experimental results evaluate the efficacy of the proposed methodology and demonstrate online system-level adaptation due to increasingly certain costs models acquired during the deployment execution.

We propose information-theoretic strategies for Occupancy Grid (OG) compression to enable high-speed exploration on computationally constrained mobile robots. We first formulate optimal lossy compression for OGs based on the Principle of Relevant Information. The solution to this formulation is a simple compression algorithm that is motivated by rate distortion theory. We then compress OGs to different resolutions and develop a second optimization based on the Information Bottleneck method that chooses a resolution simultaneously maximizing compression and minimizing loss of information from the robot's sensor measurements. On computationally constrained systems, the resulting reduction in computational complexity enables planning over longer predictive horizons, leading to higher-speed operation. Using these techniques to adaptively optimize OG resolution as the robot enters a new area causes it to autonomously slow down in obstacle-dense locations and speed up in open expanses. We simulate and experimentally evaluate mutual information-based exploration through cluttered indoor environments with exploration rates that adapt based on environment complexity, leading to an order-of-magnitude increase in the maximum rate of exploration in contrast to non-adaptive techniques given the same finite computational resources.

This paper presents a hierarchical planning architecture that generates vehicle trajectories that adapt to uncertain, spatially-varying disturbance forces toward enhanced tracking performance. The disturbance force is modeled as a discrete conditional probability distribution that is updated online by local measurements as the vehicle navigates. A global planner identifies the optimal route to the goal and adapts this route according to a cost metric derived from the belief distribution on the disturbance force. A local planner embeds the belief distribution in the trajectory generation process to compute dynamically feasible trajectories along the global plan that evolve with the belief. Simulation studies analyze and demonstrate the increased trajectory tracking accuracy via the proposed methodology with a single vehicle and the impact of the approach to multiple agents performing collaborative inference toward enhanced collective performance.

This paper presents a novel approach for multirobot pose graph localization and data association without requiring prior knowledge about the initial relative poses of the robots. Without a common reference frame, the robots can only share observations of interesting parts of the environment, and trying to match between observations from different robots will result in many outlier correspondences. Our approach is based on the following key observation: while each multi-robot correspondence can be used in conjunction with the local robot estimated trajectories, to calculate the transformation between the robot reference frames, only the inlier correspondences will be similar to each other. Using this concept, we develop an expectation-maximization (EM) approach to efficiently infer the robot initial relative poses and solve the multi-robot data association problem. Once this transformation between the robot reference frames is estimated with sufficient measure of confidence, we show that a similar EM formulation can be used to solve also the full multi-robot pose graph problem with unknown multi-robot data association. We evaluate the performance of the developed approach both in a statistical synthetic-environment study and in a real-data experiment, demonstrating its robustness to high percentage of outliers.

We present a modular and extensible approach to integrate noisy measurements from multiple heterogeneous sensors that yield either absolute or relative observations at different and varying time intervals, and to provide smooth and globally consistent estimates of position in real time for autonomous flight. We describe the development of algorithms and software architecture for a new 1.9kg MAV platform equipped with an IMU, laser scanner, stereo cameras, pressure altimeter, magnetometer, and a GPS receiver, in which the state estimation and control are performed onboard on an Intel NUC 3rdgeneration i3 processor. We illustrate the robustness of our framework in large-scale, indoor-outdoor autonomous aerial navigation experiments involving traversals of over 440 meters at average speeds of 1.5 m/s with winds around 10 mph while entering and exiting buildings.

In this paper, we consider the development of a rotorcraft micro aerial vehicle (MAV) system capable of vision-based state estimation in complex environments. We pursue a systems solution for the hardware and software to enable autonomous flight with a small rotorcraft in complex indoor and outdoor environments using only onboard vision and inertial sensors. As rotorcrafts frequently operate in hover or nearhover conditions, we propose a vision-based state estimation approach that does not drift when the vehicle remains stationary. The vision-based estimation approach combines the advantages of monocular vision (range, faster processing) with that of stereo vision (availability of scale and depth information), while overcoming several disadvantages of both. Specifically, our system relies on fisheye camera images at 25 Hz and imagery from a second camera at a much lower frequency for metric scale initialization and failure recovery. This estimate is fused with IMU information to yield state estimates at 100 Hz for feedback control. We show indoor experimental results with performance benchmarking and illustrate the autonomous operation of the system in challenging indoor and outdoor environments.

A quadrotor with a cable-suspended load with eight degrees of freedom and four degrees underactuation is considered and the system is established to be a differentially-flat hybrid system. Using the flatness property, a trajectory generation method is presented that enables finding nominal trajectories with various constraints that not only result in minimal load swing if required, but can also cause a large swing in the load for dynamically agile motions. A control design is presented for the system specialized to the planar case, that enables tracking of either the quadrotor attitude, the load attitude or the position of the load. Stability proofs for the controller design and experimental validation of the proposed controller are presented.

This paper considers the problem of finding optimal time parameterized trajectories for N unlabeled robots navigating through a cluttered environment to N unlabeled goal locations where success is defined as every goal being reached by any robot. We propose a complete computationally-tractable algorithm for simultaneously finding trajectories and assignment of goal locations. This method is then demonstrated to have an upper complexity bound of that scales polynomially in the number of robots, O(N3). The trajectories generated are guaranteed to be minimum length and collision free, while the assignment policy minimizes the maximum distance travelled. The key idea in the paper comes from the coupling between the optimal assignment, the properties of the resulting paths, and the set of valid priority assignments to the robots. These benefits result from structure in the solution to the optimal assignment to create a partial ordering of the robots, which in turn allows safe trajectories to be easily generated. Finally, we demonstrate the performance of the algorithm through simulations with tens and hundreds of robots operating in cluttered and confined environments.

The focus of this study is on the design of feedback control laws for swarms of robots that are based on models from fluid dynamics. We apply an incompressible fluid model to solve a pattern generation task. Possible applications of an efficient solution to this task are surveillance and the cordoning off of hazardous areas. More specifically, we use the smoothed-particle hydrodynamics (SPH) technique to devise decentralized controllers that force the robots to behave in a similar manner to fluid particles. Our approach deals with static and dynamic obstacles. Considerations such as finite size and nonholonomic constraints are also addressed. In the absence of obstacles, we prove the stability and convergence of controllers that are based on the SPH method. Computer simulations and actual robot experiments are shown to validate the proposed approach.

The objective of source seeking problems is to determine the minimum of an unknown signal field, which represents a physical quantity of interest, such as heat, chemical concentration, or sound. This paper proposes a strategy for source seeking in a noisy signal field using a mobile robot and based on a stochastic gradient descent algorithm. Our scheme does not require a prior map of the environment or a model of the signal field and is simple enough to be implemented on platforms with limited computational power. We discuss the asymptotic convergence guarantees of algorithm and give specific guidelines for its application to mobile robots in unknown indoor environments with obstacles. Both simulations and real-world experiments were carried out to evaluate the performance of our approach. The results suggest that the algorithm has good finite time performance in complex environments.

In this paper, we propose a stochastic differential equation-based exploration algorithm to enable exploration in three-dimensional indoor environments with a payload constrained micro-aerial vehicle (MAV). We are able to address computation, memory, and sensor limitations by considering only the known occupied space in the current map. We determine regions for further exploration based on the evolution of a stochastic differential equation that simulates the expansion of a system of particles with Newtonian dynamics. The regions of most significant particle expansion correlate to unexplored space. After identifying and processing these regions, the autonomous MAV navigates to these locations to enable fully autonomous exploration. The performance of the approach is demonstrated through numerical simulations and experimental results in single and multi-floor indoor experiments.

We address formation control for a team of quadrotors in which the robots follow a specified group trajectory while safely changing the shape of the formation according to specifications. The formation is prescribed by shape vectors which dictate the relative separations and bearings between the robots, while the group trajectory is specified as the desired trajectory of a leader or a virtual robot in the group. Each robot plans its trajectory independently based on its local information of neighboring robots which includes both the neighbor's planned trajectory and an estimate of its state. We show that the decentralized trajectory planners (a) result in consensus on the planned trajectory for predefined shapes and (b) achieve safe reconfiguration when changing shapes.

This video features advanced control capabilities for a quadrotor helicopter. We use an Ascending Technologies Hummingbird quadrotor [1] in conjunction with a Vicon [2] motion capture system in a flight arena [3] . The video shows a single quadrotor flying through windows with only three inches of clearance on any side of the platform and perching on vertical, declined, and inclined walls. Robustness to failed perching is demonstrated when it recovers to a stable hover from a failed perching attempt. Next, the video shows control along optimal trajectories through desired waypoints which includes flying through static and thrown hoops. Seen next is a gripper designed for the quadrotor to pickup objects and perch on horizontal surfaces. Finally, multiple quadrotors are shown cooperatively manipulating payloads to which they are rigidly attached.

This paper examines the dynamic interplay between decentralized controllers and mesh networking protocols for controlling groups of robots. A proportional controller is used to maintain robots in a formation based on estimates of the robots' states observed through the network. The state information is propagated through the network using a flooding algorithm, which introduces topology-dependent time delays. The coupled interaction of information flow over the network with the dynamics of the robots is modeled as a linear dynamical system. With this model it is shown that systems made up of robots with stable first order dynamics are stable for all network update times, positive feedback gains, and connected communication graphs. With higher order robot dynamics it is found that stability is a complex and counter intuitive function of feedback gain and network update time. A performance metric is proposed for analyzing the convergence rate of the multi-robot system. Experiments with flying quadrotor robots verify the predictions of the model and the performance metric.

In this paper, we consider the problem of autonomous navigation with a micro aerial vehicle (MAV) in indoor environments. In particular, we are interested in autonomous navigation in buildings with multiple floors. To ensure that the robot is fully autonomous, we require all computation to occur on the robot without need for external infrastructure, communication, or human interaction beyond high-level commands. Therefore, we pursue a system design and methodology that enables autonomous navigation with real time performance on a mobile processor using only onboard sensors. Specifically, we address multi-floor mapping with loop closure, localization, planning, and autonomous control, including adaptation to aerodynamic effects during traversal through spaces with low vertical clearance or strong external disturbances. We present experimental results with ground truth comparisons and performance analysis.

In this paper, we consider the problem of autonomous navigation with a micro aerial vehicle (MAV) in indoor environments. In particular, we are interested in autonomous navigation in buildings with multiple floors. To ensure that the robot is fully autonomous, we require all computation to occur on the robot without need for external infrastructure, communication, or human interaction beyond high-level commands. Therefore, we pursue a system design and methodology that enables autonomous navigation with real time performance on a mobile processor using only onboard sensors. Specifically, we address multi-floor mapping with loop closure, localization, planning, and autonomous control, including adaptation to aerodynamic effects during traversal through spaces with low vertical clearance or strong external disturbances. We present experimental results with ground truth comparisons and performance analysis.

We consider the problem of deploying robots in formations that ensure network connectivity between a fixed base station and a set of independent agents wandering in the environment. We adopt a communications model that requires line-of-sight and then solve for robot placements by finding mutually-visible configurations in a polygonal decomposition of the environment map. Both the static deployment case and the case of finding deployments that minimize total robot movement are considered. We provide algorithms for the moving agent case, consider their performance on various discretizations for a range of problem sizes, and discuss our experimental implementation of the presented ideas.

In this paper, we formulate a distributed, energy-aware control policy aimed at enabling persistent surveillance of a specified region of interest by teams of networked robots. Central to our formulation is the fundamental idea that as an agent participating in coverage approaches a low energy reserve the team should cooperatively adjust the coverage formation to allow the agent to return to a designated base station, where it can recharge before rejoining the effort. Towards this end, we build upon recent efforts in employing Centroidal Voronoi Tessellation (CVT)-based coverage control laws by defining a policy that exploits a power-dependent weighting scheme that embeds an agent's trade-off to achieve its coverage mission and to maintain a desired energy reserve to guarantee its own safety. Stability of the proposed approach is considered, and we show that coupling our continuous controller with a straightforward switching mechanism guarantees every agent will return to its base station safely. Simulation results are presented to verify and demonstrate the utility of the proposed control scheme.

In this paper, we focus on the detailing of a system architecture capable of addressing the problem of persistent surveillance with a team of autonomous micro-aerial vehicles (MAVs). We detail the problem of interest, discuss system requirements, and provide an overview of our approach. The remainder of the paper is dedicated to the system design and evaluation on a team of quadrotors in simulation and experiments.

There are many examples in nature where large groups of individuals are able to maintain 3-D formations while navigating in complex environments. This paper addresses the development of a framework and robot controllers that enable a group of aerial robots to maintain a formation with partial state information while avoiding collisions. The central concept is to develop a low-dimensional abstraction of the large teams of robots, facilitate planning, command, and control in a low-dimensional space, and to realize commands or plans in the abstract space by synthesizing controllers for individual robots that respect the specified abstraction. We derive the basic theory and present experimental and simulation results with a team of rotor crafts.

Different robotic tasks can be solved by controlling a robot to circulate along curves. These include, for example, border inspection and surveillance, multirobot manipulation, and pattern generation. In a previous, work we have proposed a vector field approach for robot convergence and circulation along time-varying curves embedded in N-dimensional spaces. In the present work we instantiate this approach for three-dimensional spaces and, for the first time, show the efficacy of this method to control actual robots. Besides new theoretical analysis when constant speed control is applied, we present experimental results with aerial (quadrotors) and ground (differential-driven) robot.

We study radio signal propagation in indoor environments using low-power devices leveraging the Zigbee and Bluetooth specifications. We present results from experiments where two robots equipped with radio signal devices and enabled to control and localize autonomously in an indoor hallway and laboratory environment densely sample RSSI at various times over several days. We show that simulated RSSI measurements using existing radio signal models and experimentally gathered RSSI measurements match closely, suggesting that for robotics applications requiring predicted RSSI, low-power radio signal devices are a well-posed sensing modality.

This paper deals with vision-based localization for leader–follower formation control. Each unicycle robot is equipped with a panoramic camera that only provides the view angle to the other robots. The localization problem is studied using a new observability condition valid for general nonlinear systems and based on the extended output Jacobian. This allows us to identify those robot motions that preserve the system observability and those that render it nonobservable. The state of the leader–follower system is estimated via the extended Kalman filter, and an input-state feedback control law is designed to stabilize the formation. Simulations and real-data experiments confirm the theoretical results and show the effectiveness of the proposed formation control.

In this paper, we study the problem of distributed motion coordination among a group of nonholonomic ground robots. We develop vision-based control laws for parallel and balanced circular formations using a consensus approach. The proposed control laws are distributed in the sense that they require information only from neighboring robots. Furthermore, the control laws are coordinate-free and do not rely on measurement or communication of heading information among neighbors but instead require measurements of bearing, optical flow, and time to collision, all of which can be measured using visual sensors. Collision-avoidance capabilities are added to the team members, and the effectiveness of the control laws are demonstrated on a group of mobile robots.

Distributed task assignment for multiple agents raises fundamental and novel problems in control theory and robotics. A new challenge is the development of distributed algorithms that dynamically assign tasks to multiple agents, not relying on a priori assignment information. We address this challenge using market-based coordination protocols where the agents are able to bid for task assignment with the assumption that every agent has knowledge of the maximum number of agents that any given task can accommodate. We show that our approach always achieves the desired assignment of agents to tasks after exploring at most a polynomial number of assignments, dramatically reducing the combinatorial nature of discrete assignment problems. We verify our algorithm through both simulation and experimentation on a team of non-holonomic robots performing distributed formation stabilization and group splitting and merging.

We address the problem of pattern generation in obstacle-filled environments by a swarm of mobile robots. Decentralized controllers are devised by using the Smoothed Particle Hydrodynamics (SPH) method. The swarm is modelled as an incompressible fluid subjected to external forces. Actual robot issues such as finite size and nonholonomic constraints are also addressed. Collision avoidance guarantees are discussed. Finally, in the absence of obstacles, we prove for the first time stability and convergence of controllers based on the SPH.

We consider the task of controlling a large team of nonholonomic ground robots with an unmanned aerial vehicle in a decentralized manner that is invariant to the number of ground robots. The central idea is the development of an abstraction for the team of ground robots that allows the aerial platform to control the team without any knowledge of the specificity of individual vehicles. This happens in much the same way as a human operator can control a single robot vehicle by simply commanding the forward and turning velocities without a detailed knowledge of the specifics of the robot. The abstraction includes a gross model of the shape of the formation of the team and information about the position and orientation of the team in the plane. We derive controllers that allow the team of robots to move in formation while avoiding collisions and respecting the abstraction commanded by the aerial platform. We provide simulation and experimental results using a team of indoor mobile robots and a three-dimensional, cable-controlled, parallel robot which serves as our indoor unmanned aerial platform.

We address the problem of modeling and controlling a swarm of fully actuated point-like robots in three dimensions by generalizing the planar framework from (C. Belta and V. Kumar, 2004). We define a nine-dimensional abstraction of the swarm that has a product structure of the six-dimensional Euclidean group and a three-dimensional shape, and is independent of the number of robots. The group captures the pose of an ellipsoid spanning the swarm with semiaxes given by the shape variables. The overall abstract description is invariant to robot permutations. In addition, the shape is also invariant to left actions of the group. This description allows one to define and control the behavior of the swarm at a high level, with automatic generation of individual robot control laws. We present simulation results for controlling swarms of rotorcrafts

We address the synthesis of controllers for large groups of robots and sensors, tackling the specific problem of controlling a swarm of robots to generate patterns specified by implicit functions of the form s(x, y) = 0. We derive decentralized controllers that allow the robots to converge to a given curve S and spread along this curve. We consider implicit functions that are weighted sums of radial basis functions created by interpolating from a set of constraint points, which give us a high degree of control over the desired 2D curves. We describe the generation of simple plans for swarms of robots using these functions and illustrate our approach through simulations and real experiments.