We propose a novel concept for modular robots, termed modular fluidic propulsion (MFP), which promises to combine effective propulsion, a large reconfiguration space, and a scalable design. MFP robots are modular fluid networks. To propel, they route fluid through themselves. In this article, both hydraulic and pneumatic implementations are considered. The robots move towards a goal by way of a decentralized controller that runs independently on each module face, uses two bits of sensory information and requires neither run-time memory, nor communication. We prove that 2-D MFP robots reach the goal when of orthogonally convex shape, or reach a morphology-dependent distance from it when of arbitrary shape. We present a 2-D hydraulic MFP prototype and show, experimentally, that it succeeds in reaching the goal in at least 90% of trials, and that 71% less energy is expended when modules can communicate. Moreover, in simulations with 3-D hydraulic MFP robots, the decentralized controller performs almost as well as a state-of-the-art and centralized controller. Given the simplicity of the hardware requirements, the MFP concept could pave the way for modular robots to be used at sub-centimeter-scale, where effective modular propulsion systems have not been demonstrated.

A robot understands its world through the raw information it senses from its surroundings. This raw information is not suitable as a shared representation between the robot and its user. A semantic map, containing high-level information that both the robot and user understand, is better suited to be a shared representation. We use the semantic map as the user-facing interface on our fleet of floor-cleaning robots. Jitter in the robot's sensed raw map, dynamic objects in the environment, and exploration of new space by the robot are common challenges for robots. Solving these challenges effectively in the context of semantic maps is key to enabling semantic maps for lifelong mapping. First, as a robot senses new changes and alters its raw map in successive runs, the semantics must be updated appropriately. We update the map using a spatial transfer of semantics. Second, it is important to keep semantics and their relative constraints consistent even in the presence of dynamic objects. Inconsistencies are automatically determined and resolved through the introduction of a map layer of meta-semantics. Finally, a discovery phase allows the semantic map to be updated with new semantics whenever the robot uncovers new information. Deployed commercially on thousands of floor-cleaning robots in real homes, our user-facing semantic maps provide a intuitive user experience through a lifelong mapping robot.

Searching for a lost teammate is an important task for multirobot systems. We present a variant of rapidly-expanding random trees (RRT) for generating search paths based on a probabilistic belief of the target teammate's position. The belief is updated using a hidden Markov model built from knowledge of the target's planned or historic behavior. For any candidate search path, this belief is used to compute a discounted reward which is a weighted sum of the connection probability at each time step. The RRT search algorithm uses randomly sampled locations to generate candidate vertices and adds candidate vertices to a planning tree based on bounds on the discounted reward. Candidate vertices are along the shortest path from an existing vertex to the sampled location, biasing the search based on the topology of the environment. This method produces high quality search paths which are not constrained to a grid and can be computed fast enough to be used in real time. Compared with two other strategies, it found the target significantly faster in the most difficult 60% of situations and was similar in the easier 40% of situations.

We study the problem of controlling a swarm of anonymous, mobile robots to cooperatively cover an unknown two-dimensional space. The novelty of our proposed solution is that it is applicable to extremely simple robots that lack run-time computation or storage. The solution requires only a single bit of information per robot-whether or not another robot is present in its line of sight. Computer simulations show that our deterministic controller, which was obtained through off-line optimization, achieves around 71-76% coverage in a test scenario with no robot redundancy, which corresponds to a 26-39% reduction of the area that is not covered, when compared to an optimized random walk. A moderately lower level of performance was observed in 20 experimental trials with 25 physical e-puck robots. Moreover, we demonstrate that the same controller can be used in environments of different dimensions and even to navigate a maze. The controller provides a baseline against which one can quantify the performance improvements that more advanced and expensive techniques may offer. Moreover, due to its simplicity, it could potentially be implemented on swarms of sub-millimeter-sized robots. This would pave the way for new applications in micro-medicine.

Multirobot coverage is the problem of planning paths for several identical robots such that the combined regions traced out by the robots completely cover their environment. We consider the problem of multirobot coverage with the objective of minimizing the mission time, which depends on the number of turns taken by the robots. To solve this problem, we first partition the environment into ranks which are long thin rectangles the width of the robot's coverage tool. Our novel partitioning heuristic produces a set of ranks which minimizes the number of turns. Next, we solve a variant of the multiple travelling salesperson problem (m-TSP) on the set of ranks to minimize the robots' mission time. The resulting coverage plan is guaranteed to cover the entire environment. We present coverage plans for a robotic vacuum using real maps of 25 indoor environments and compare the solutions to paths planned without the objective of minimizing turns. Turn minimization reduced the number of turns by 6.7% and coverage time by 3.8% on average for teams of 1-5 robots.

As communication is important for cooperation, teams of mobile robots need a way to re-establish a wireless connection if they get separated. We develop a method for mobile robots to maintain a belief of each other's positions using locally available information. They can use their belief to plan paths with high probabilities of reconnection. This approach also works for subteams cooperatively searching for a robot or group of robots that they would like to reconnect with. The problem is formulated as a constrained optimization problem which is solved using a branch-and-bound approach. We present simulation results showing the effectiveness of this strategy at reconnecting teams of up to five robots and compare the results to two other strategies.

In this paper, we address the problem of detecting intruders in complex bidimensional environments with a team of robots arranged in line formations called sweep lines. Sweep lines are used to coordinate the motion of multiple robots and guarantee the detection of any number of arbitrarily fast intruders, even when each robot has a limited sensor footprint. We present a formalization of the problem, coined Line-Clear, which requires the computation of sweep schedules to coordinate the motion of multiple sweep lines using the fewest robots possible. We provide a proof of NP-hardness of the general Line-Clear problem based on results from graph searching. An algorithm to compute sweep schedules for simply connected environments, which additionally guarantees that the cleared area is connected and not recontaminated, is then presented. We analyze its complexity formally and in simulation experiments and present solutions for a number of subproblems required for an implementation of the algorithm. The analysis provides a formal criterion for when the algorithm runs in polynomial time and the experiments indicate that this criterion may be satisfied for most environments in practice.

We consider a visibility-based pursuit-evasion problem in which a single robot with an omnidirectional but unreliable sensor moving through an environment must systematically search that environment to detect an unpredictably moving target. A common assumption in visibility-based pursuit-evasion is that the sensors used to detect the evader are perfectly reliable. That is, any evader that moves within view of the pursuer for any interval of time will be detected. This assumption is problematic because, when implemented on real sensor systems, such plans cannot account for the possibility of short-term false negative errors in evader detection. This paper addresses this limitation by introducing a model based on the idea of pessimal unoccluded distance to reason about the degree of plausibility that the evader may be concealed within each occluded region. We describe a decomposition of the environment that fully characterizes the opportune moment for an evader to take advantage of sensor error. Furthermore, we present a complete algorithm that solves the active problem of planning a search for a pursuer which maximizes the distance that the evader must travel through the pursuer robot's sensor footprint.

We introduce RoomsSeg, a novel method for segmenting occupancy grid maps into regions that represent rooms and corridors in the real world. The segmentation is utilized for systematic room-by-room cleaning on autonomous vacuum cleaners running in private homes. RoomsSeg is based on automated clutter removal and watershed segmentation on grid maps. Segmented regions are merged into rooms by semantic decision rules. Presented experimental results clearly indicate the efficiency and accuracy of the approach when compared with state of the art methods. When deployed on cleaning robots, a substantial decrease in mission time can be achieved.

This paper presents OpenSwarm, a lightweight easy-to-use open-source operating system. To our knowledge, it is the first operating system designed for and deployed on miniature robots. OpenSwarm operates directly on a robot's microcontroller. It has a memory footprint of 1 kB RAM and 12 kB ROM. OpenSwarm enables a robot to execute multiple processes simultaneously. It provides a hybrid kernel that natively supports preemptive and cooperative scheduling, making it suitable for both computationally intensive and swiftly responsive robotics tasks. OpenSwarm provides hardware abstractions to rapidly develop and test platform-independent code. We show how OpenSwarm can be used to solve a canonical problem in swarm robotics—clustering a collection of dispersed objects. We report experiments, conducted with five e-puck mobile robots, that show that an OpenSwarm implementation performs as good as a hardware-near implementation. The primary goal of OpenSwarm is to make robots with severely constrained hardware more accessible, which may help such systems to be deployed in real-world applications.

Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.

This paper proposes a strategy for transporting a large object to a goal using a large number of mobile robots that are significantly smaller than the object. The robots only push the object at positions where the direct line of sight to the goal is occluded by the object. This strategy is fully decentralized and requires neither explicit communication nor specific manipulation mechanisms. We prove that it can transport any convex object in a planar environment. We implement this strategy on the e-puck robotic platform and present systematic experiments with a group of 20 e-pucks transporting three objects of different shapes. The objects were successfully transported to the goal in 43 out of 45 trials. When using a mobile goal, teleoperated by a human, the object could be navigated through an environment with obstacles. We also tested the strategy in a 3-D environment using physics-based computer simulation. Due to its simplicity, the transport strategy is particularly suited for implementation on microscale robotic systems.

We consider the problem of computing trajectories for a team of of coordinated unmanned aerial vehicles (UAVs) in large and complex 2D and 2.5D environments to guarantee the detection of any evading target. Our approach is based on the coordination of 2D sweep lines that move through the environment to clear it from all contamination, representing the possibility of a target being located in an area, and thereby detecting all targets. The trajectories of the UAVs are computed from the motion of these sweep lines. Low cost coordination strategies of the UAV sweep lines are computed in 2D and simply-connected polygonal environments and then converted to strategies capable of clearing multiply-connected 2.5D environments. We present simulation experiments with maps of real and artificial environments and demonstrate the execution of strategies with simulated quadrotors using the Robot Operating System (ROS) framework. The algorithms used for the experiments are made available on a public repository.

In this paper we consider the problem of searching for an arbitrarily smart and fast evader in a large environment with a team of unmanned aerial vehicles (UAVs) while providing guarantees of detection. Our emphasis is on the fast execution of efficient search strategies that minimize the number of UAVs and the search time. We present the first approach for computing fast guaranteed search strategies utilizing additional searchers to speed up the execution time and thereby enabling large scale UAV search. In order to scale to very large environments when using UAVs one would either have to overcome the energy limitations of UAVs or pay the cost of utilizing additional UAVs to speed up the search. Our approach is based on coordinating UAVs on sweep lines, covered by the UAV sensors, that move simultaneously through an environment. We present some simulation results that show a significant reduction in execution time when using multiple UAVs and a demonstration of a real system with three AR.Drone 2.0.

In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors.

In this paper we investigate principles of swarm control that enable a human operator to exert influence on and control large swarms of robots. We present two principles, coined selection and beacon control, that differ with respect to their temporal and spatial persistence. The former requires active selection of groups of robots while the latter exerts a passive influence on nearby robots. Both principles are implemented in a testbed in which operators exert influence on a robot swarm by switching between a set of behaviors ranging from trivial behaviors up to distributed autonomous algorithms. Performance is tested in a series of complex foraging tasks in environments with different obstacles ranging from open to cluttered and structured. The robotic swarm has only local communication and sensing capabilities with the number of robots ranging from 50 to 200. Experiments with human operators utilizing either selection or beacon control are compared with each other and to a simple autonomous swarm with regard to performance, adaptation to complex environments, and scalability to larger swarms. Our results show superior performance of autonomous swarms in open environments, of selection control in complex environments, and indicate a potential for scaling beacon control to larger swarms.

Swarm robots use simple local rules to create complex emergent behaviors. The simplicity of the local rules allows for large numbers of low-cost robots in deployment, but the same simplicity creates difficulties when deploying in many applicable environments. These complex missions sometimes require human operators to influence the swarms towards achieving the mission goals. Human swarm interaction (HSI) is a young field with few user studies exploring operator behavior. These studies all assume perfect information between the operator and the swarm, which is unrealistic in many applicable scenarios. Indoor search and rescue or underwater exploration may present environments where radio limitations restrict the bandwidth of the robots. This study explores this bandwidth restriction in a user study. Three levels of bandwidth are explored to determine what amount of information is necessary to accomplish a swarm foraging task. The lowest bandwidth condition performs poorly, but the medium and high bandwidth condition both perform well. The medium bandwidth condition does so by aggregating useful swarm information to compress the state information. Further, the study shows operators preferences that should have hindered task performance, but operator adaptation allowed for error correction.

Autonomous swarm algorithms have been studied extensively in the past several years. However, there is little research on the effect of injecting human influence into a robot swarm-whether it be to update the swarm's current goals or reshape swarm behavior. While there has been growing research in the field of human-swarm interaction (HSI), no previous studies have investigated how humans interact with swarms under communication latency.We investigate the effects of latency both with and without a predictive display in a basic swarm foraging task to see if such a display can help mitigate the effects of delayed feedback of the swarm state. Furthermore, we introduce a new concept called neglect benevolence to represent how a human operator may need to give time for swarm algorithms to stabilize before issuing new commands, and we investigate it with respect to task performance. Our study shows that latency did affect a user's ability to control a swarm to find targets in the foraging task, and that the predictive display helped to remove these effects. We also found evidence for neglect benevolence, and that operators exploited neglect benevolence in different ways, leading to two different, but equally successful strategies in the target-searching task.

In this paper, we present an asynchronous display method, coined image queue, which allows operators to search through a large amount of data gathered by autonomous robot teams. We discuss and investigate the advantages of an asynchronous display for foraging tasks with emphasis on Urban Search and Rescue. The image queue approach mines video data to present the operator with a relevant and comprehensive view of the environment in order to identify targets of interest such as injured victims. It fills the gap for comprehensive and scalable displays to obtain a network-centric perspective for UGVs. We compared the image queue to a traditional synchronous display with live video feeds and found that the image queue reduces errors and operator's workload. Furthermore, it disentangles target detection from concurrent system operations and enables a call center approach to target detection. With such an approach we can scale up to very large multi-robot systems gathering huge amounts of data that is then distributed to multiple operators.

We address the problem of searching for moving targets in large outdoor environments represented by height maps. To solve the problem we present a complete system that computes from an annotated height map a graph representation and search strategies based on worst-case assumptions about all targets. These strategies are then used to compute a schedule and task assignment for all agents. We improve the graph construction from previous work and for the first time present a method that computes a schedule to minimize the execution time. For this we consider travel times of agents determined by a path planner on the height map. We demonstrate the entire system in a real environment with an area of 700,000m2in which eight human agents search for two intruders using mobile computing devices (iPads). To the best of our knowledge this is the first demonstration of a search system applied to such a large environment.

Using classical methods the time course of workload over a work session is rarely observed. Subjective measures of necessity are for the entire session rather than instantaneous. While this distinction makes little difference to researchers wanting to compare task difficulty, it is important to designers of systems that wish to schedule operator attention because they must predict workload. The present study provides an experimental setting in which intervals of continuous work and uninterrupted rest can be precisely controlled. No differences in probability of error or latency of response were found for the length of break/rest intervals or their location in a sequence of intervals. The local utilization within a break/work interval (ratio of work/(work + rest)), however, was significant, suggesting that utilization effects are strictly local and that simple algorithms could be used to optimize human-system performance for such tasks.

Exploration of unknown environments has numerous applications in the domains of search and rescue, pursuit-evasion, map building, and military espionage and intelligence gathering. While it is ideal to have autonomous robots search intelligently based on global patterns and features of each environment, such learning algorithms should also be informed by, and perform at least as well as, human exploration. Most autonomous exploration algorithms focus on local and greedy choices and are less concerned with learning to recognize structural features of environments in order to improve global exploration performance. If the strategies people use during exploration can be determined, they can be used as a basis for creating good utility functions for autonomous robots to be optimized by machine learning techniques. Here, we perform a study where human participants explore both random and patterned environments, in two different time-sensitive scenarios, in an attempt to determine what strategies people use to maximize area explored under a variety of cases. We found that participants in a controlled study did in fact adapt their exploration strategies in time-sensitive scenarios and also exploited features of the environments in order to achieve better global exploration performance.

We propose a distributed algorithm enabling a large team of robots to detect all intruders within a large planar environment. Each robot can only detect intruders and communicate with other robots within a limited range. No map of the environment is given, and none is built during the process. Robots are only capable of following walls and other robots that are nearby. The algorithm puts together elementary behaviors giving robots the means to coordinate their movement in order to cover lines between opposite walls with their sensors and discover nearby new walls. A line has leading robots at its endpoints that follow walls and hence move the line of robots forward. Multiple such lines move through the entire assigned area in order to detect all intruders. The movement of multiple lines is coordinated by using a graph representation of the environment that describes possible line movements and their associated costs in terms of robots. This coordination requires only local communication between the leaders of different robot lines when they meet. Finally, we demonstrate how the algorithm can be implemented using elementary wall following and obstacle discovery behaviors.

In this paper we present an approach for a pursuit-evasion problem that considers a 2.5d environment represented by a height map. Such a representation is particularly suitable for large-scale outdoor pursuit-evasion, captures some aspects of 3d visibility and can include target heights. In our approach we construct a graph representation of the environment by sampling strategic locations and computing their detection sets, an extended notion of visibility. From the graph we compute strategies using previous work on graph-searching. These strategies are used to coordinate the robot team and to generate paths for all robots using an appropriate classification of the terrain. In experiments we investigate the performance of our approach and provide examples including a sample map with multiple loops and elevation plateaus and two realistic maps, a village and a mountain range. To the best of our knowledge the presented approach is the first viable solution to 2.5d pursuit-evasion with height maps.

We present graph-clear: a novel pursuit-evasion problem on graphs which models the detection of intruders in complex indoor environments by robot teams. The environment is represented by a graph, and a robot team can execute sweep and block actions on vertices and edges, respectively. A sweep action detects intruders in a vertex and represents the capability of the robot team to detect intruders in the region associated to the vertex. Similarly, a block action prevents intruders from crossing an edge and represents the capability to detect intruders as they move between regions. Both actions may require multiple robots to be executed. A strategy is a sequence of block and sweep actions to detect all intruders. When instances of graph-clear are being solved, the goal is to determine optimal strategies, i.e., strategies that use the least number of robots. We prove that for the general case of graphs, the problem of computation of optimal strategies is NP-hard. Next, for the special case of trees, we provide a polynomial-time algorithm. The algorithm ensures that throughout the execution of the strategy, all cleared vertices form a connected subtree, and we show that it produces optimal strategies.

This paper introduces a probabilistic model for multirobot surveillance applications with limited range and possibly faulty sensors. Sensors are described with a footprint and a false negative probability, i.e. the probability of failing to report a target within their sensing range. The model implements a probabilistic extension to our formerly developed deterministic approach for modeling surveillance tasks in large environments with large robot teams known as Graph-Clear. This extension leads to a new algorithm that allows to answer new design and performance questions, namely 1) how many robots are needed to obtain a certain confidence that the environment is free from intruders, and 2) given a certain number of robots, how should they coordinate their actions to minimize their failure rate.

In this paper we present a method to extract surveillance graphs from occupancy grid maps. Surveillance graphs are part of the graph-clear framework and model the problem of detecting targets using multiple robots with limited range sensors. Robots can only execute basic actions called sweep and block on vertices and edges, respectively. Sweep detects targets in vertices and block prevents them from crossing edges. The extracted graphs accurately model the complexity of the planar environment to be searched, and are constructed as duals of the Voronoi diagram. We give a geometric embedding for blocking and sweeping actions of the graph into the environment by directly associating them to sweep lines that robots cover with their sensors. This paper solves two open problems, namely the generation of surveillance graphs and the implementation of actions on a robot team. Sweep lines can then be directly translated into control inputs to the robot team. The new method is superior to previous heuristics for the extraction of graphs not only through its direct geometric relationship to the environment, but also due to its increased performance in direct experimental comparisons. Additionally, it provides a basis for possible theoretical results regarding the optimal coordination of multiple robots to detect targets in an arbitrary planar environment.

The main contribution of this paper is an improved algorithm for the GRAPH-CLEAR problem, a novel NP-complete graph theoretic problem we recently introduced as a tool to model multi-robot surveillance tasks. The proposed algorithm combines two previously developed solving techniques and produces strategies that require less robots to be executed. We provide a theoretical framework useful to identify the conditions for the existence of an optimal solution under special circumstances, and a set of mathematical tools characterizing the problem being studied. Finally we also identify a set of open questions deserving more investigations.

GRAPH-CLEAR is a recently introduced theoretical framework to model surveillance tasks accomplished by multiple robots patrolling complex indoor environments. In this paper we provide a first step to close the loop between its graph-based theoretical formulation and practical scenarios. We show how it is possible to algorithmically extract suitable so-called surveillance graphs from occupancy grid maps. We also identify local graph modification operators, called contractions, that alter the graph being extracted so that the original surveillance problem can be solved using less robots. The algorithm we present is based on the generalized Voronoi diagram, a structure that can be simply computed using watershed like algorithms. Our algorithm is evaluated by processing maps produced by mobile robots exploring indoor environments. It turns out that the proposed algorithm is fast, robust to noise, and opportunistically modifies the graph so that less expensive strategies can be computed.

In this paper we present a novel graph theoretic problem, called GRAPH-CLEAR, useful to model surveillance tasks where multiple robots are used to detect all possible intruders in a given indoor environment. We provide a formal definition of the problem and we investigate its basic theoretical properties, showing that the problem is NP-complete. We then present an algorithm to compute a strategy for the restriction of the problem to trees and present a method how to use this solution in applications. The method is then tested in simple simulations. GRAPH-CLEAR is useful to describe multirobot pursuit evasion games when robots have limited sensing capabilities, i.e. multiple agents are needed to perform basic patrolling operations.

This paper presents a behavior-based solution to the problem of observing multiple mobile targets by multiple mobile robots. Robots sense targets using sensors and in addition exchange information about them with other robots. Workload is shared between different robots by requesting help when targets are escaping and supporting robots requesting such help. We provide a detailed description of the proposed solution, as well as significant simulation tests to outline its performance. The described approach outperforms formerly proposed solutions

A standard method to obtain stochastic models for symbolic time series is to train state-emitting hidden Markov models (SE-HMMs) with the Baum-Welch algorithm. Based on observable operator models (OOMs), in the last few months a number of novel learning algorithms for similar purposes have been developed: (1,2) two versions of an "efficiency sharpening" (ES) algorithm, which iteratively improves the statistical efficiency of a sequence of OOM estimators, (3) a constrained gradient descent ML estimator for transition-emitting HMMs (TE-HMMs). We give an overview on these algorithms and compare them with SE-HMM/EM learning on synthetic and real-life data.