iRobot Corporation

The Robot Operating System 2 (ROS 2) is the second generation of ROS representing a step forward in the robotic framework. Several new types of nodes and executor models are integral to control where, how, and when information is processed in the computational graph. This paper explores and benchmarks one of these new node types - the Component node - which allows nodes to be composed manually or dynamically into processes while retaining separation of concerns in a codebase for distributed development. Composition is shown to achieve a high degree of performance optimization, particularly valuable for resource-constrained systems and sensor processing pipelines, enabling distributed tasks that would not be otherwise possible in ROS 2. In this work, we briefly introduce the significance and design of node composition, then our contribution of benchmarking is provided to analyze its impact on robotic systems. Its compelling influence on performance is shown through several experiments on the latest Long Term Support (LTS) ROS 2 distribution, Humble Hawksbill.

A Graph SLAM system is only as good as the edges in its pose graph. Critical mistakes in the generation of these edges can instantly render a map inconsistent, misleading, and ultimately unusable. For a lifelong mapping system, where the map is updated continuously, avoiding these errors altogether is infeasible. Instead, we propose a system for detection of and recovery from severe errors in edge generation. Our system remedies both edges created by view observations and edges created by an odometry motion model. For observation edges, we pair a novel method for monitoring ambiguous views with an intelligent graph-merging algorithm capable of rejecting a relocalization in progress. For motion edges, we propose a qualitative geometric approach for detecting structural aberrations characteristic of odometry failures. We conclude with an analysis of our results based on an empirical study of thousands of robot runs.

Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents each with individual start and goal states within a continuous state space. Existing approaches can be split into coupled methods which provide optimal solutions but struggle with scalability or decoupled methods which provide scalable solutions but offer no optimality guarantees. Recent work has explored hybrid approaches that leverage the advantages of both coupled and decoupled approaches in an easier discrete subproblem, Multi-Agent Pathfinding (MAPF). In this work, we adapt recent developments in hybrid MAPF to the continuous domain of MAMP. We demonstrate the scalability of our method to manage groups of up to 32 agents, demonstrate the ability to handle up to 8 high-DOF manipulators, and plan for heterogeneous teams. In all scenarios, our approach plans significantly faster while providing higher quality solutions.

Modular Fluidic Propulsion (MFP) is a novel concept for modular reconfigurable robots that 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. Both hydraulic and pneumatic implementations are considered. The robots are tasked to move towards a goal. We present 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 or a morphology-dependent distance from it, if of orthogonally convex or arbitrary shape, respectively. 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 the state-of-the-art, centralized controller. Given the simplicity of the hardware requirements, the MFP concept could pave the way for modular robots to be applied at the sub-centimeter-scale, where effective modular propulsion systems have not been demonstrated.

Swarm robotics investigates groups of relatively simple robots that use decentralized control to achieve a common goal. While the robots of many swarm systems communicate via optical links, the underlying channels and their impact on swarm performance are poorly understood. This paper models the optical channel of a widely used robotic platform, the e-puck. It proposes SwarmCom, a mobile ad-hoc network for mobile robots. SwarmCom has a detector that, with the help of the channel model, was designed to adapt to the environment and nearby robots. Experiments with groups of up to 30 physical e-pucks show that (i) SwarmCom outperforms the state-of-the-art infra-red communication software—libIrcom—in range (up to 3 times further), bit error rate (between 50 and 63% lower), or throughput (up to 8 times higher) and that (ii) the maximum number of communication channels per robot is relatively low, which limits the load per robot even for high-density swarms. Using channel coding, the bit error rate can be further reduced at the expense of throughput. SwarmCom could have profound implications for swarm robotics, contributing to system understanding and reproducibility, while paving the way for novel applications.

This paper proposes an extremum-seeking controller (ESC) design for a class of discrete-time nonlinear control systems subject to input constraints or quantized inputs. The proposed method implements a proportional-integral ESC design along with a discrete-time anti-windup mechanism. The anti-windup enforces input saturation while preserving the input dither signal. The technique incorporates a mechanism for adjusting the amplitude of the extremum seeking control dither signal. This mechanism ensures that any violation of constraints due to the dither signal is removed while maintaining the probing signal active. An amplitude update routine is also proposed. The amplitude update is coupled with a saturation bias estimation algorithm that correctly accounts for the inherent bias associated with systems operated at or near saturation conditions. The amplitude update is designed to remove the dither signal when the system approaches the optimum. It also ensures that a lower bound of the amplitude is enforced to guarantee that excitation conditions are maintained.

We propose an architecture controlled by a thin computational layer designed to tightly correspond with the lambda calculus, drawing on principles of functional programming to bring the assembly much closer to myriad reasoning frameworks and specification languages. This approach allows assembly-level verified versions of critical code to operate safely in tandem with arbitrary code without the need for large supporting trusted computing bases.

Visual topological localization is a process typically required by varied mobile autonomous robots, but it is a complex task if long operating periods are considered. This is because of the appearance variations suffered in a place: dynamic elements, illumination or weather. Due to these problems, long-term visual place recognition across seasons has become a challenge for the robotics community. For this reason, we propose an innovative method for a robust and efficient life-long localization using cameras. In this paper, we describe our approach (ABLE), which includes three different versions depending on the type of images: monocular, stereo and panoramic. This distinction makes our proposal more adaptable and effective, because it allows to exploit the extra information that can be provided by each type of camera. Besides, we contribute a novel methodology for identifying places, which is based on a fast matching of global binary descriptors extracted from sequences of images. The presented results demonstrate the benefits of using ABLE, which is compared to the most representative state-of-the-art algorithms in long-term conditions.

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. IEEE

In this paper, we propose a feature-tracking algorithm for sea ice drift retrieval from a pair of sequential satellite synthetic aperture radar (SAR) images. The method is based on feature tracking comprising feature detection, description, and matching steps. The approach exploits the benefits of nonlinear multiscale image representations using accelerated-KAZE (A-KAZE) features, a method that detects and describes image features in an anisotropic scale space. We evaluated several state-of-the-art feature-based algorithms, including A-KAZE, Scale Invariant Feature Transform (SIFT), and a very fast feature extractor that computes binary descriptors known as Oriented FAST and Rotated BRIEF (ORB) on dual polarized Sentinel-1A C-SAR extra wide swath mode data over the Arctic. The A-KAZE approach outperforms both ORB and SIFT up to an order of magnitude in ice drift. The experimental results showed high relevance of the proposed algorithm for retrieval of ice drift at subkilometre resolution from a pair of SAR images with 100-m pixel size. From this paper, we found that feature tracking using nonlinear scale-spaces is preferable due to its high efficiency against noise with respect to image features compared with other existing feature tracking alternatives that make use of Gaussian or linear scale spaces.

We propose a continuous optimization method for solving dense 3D scene flow problems from stereo imagery. As in recent work, we represent the dynamic 3D scene as a collection of rigidly moving planar segments. The scene flow problem then becomes the joint estimation of pixel-to-segment assignment, 3D position, normal vector and rigid motion parameters for each segment, leading to a complex and expensive discrete-continuous optimization problem. In contrast, we propose a purely continuous formulation which can be solved more efficiently. Using a fine superpixel segmentation that is fixed a-priori, we propose a factor graph formulation that decomposes the problem into photometric, geometric, and smoothing constraints. We initialize the solution with a novel, high-quality initialization method, then independently refine the geometry and motion of the scene, and finally perform a global non-linear refinement using Levenberg-Marquardt. We evaluate our method in the challenging KITTI Scene Flow benchmark, ranking in third position, while being 3 to 30 times faster than the top competitors (x37 [10] and x3.75 [24]).

No abstract is available for this article.

No abstract is available for this article.

When the first edition of this book was published domestic robots were spoken of as a dream that was slowly becoming reality. At that time, in 2008, we looked back on more than twenty years of research and development in domestic robotics, especially in cleaning robotics. Although everybody expected cleaning to be the killer app for domestic robotics in the first half of these twenty years nothing big really happened. About ten years before the first edition of this book appeared, all of a sudden things started moving. Several small, but also some larger enterprises announced that they would soon launch domestic cleaning robots. The robotics community was anxiously awaiting these first cleaning robots and so were consumers. The big burst, however, was yet to come. The price tag of those cleaning robots was far beyond what people were willing to pay for a vacuum cleaner. It took another four years until, in 2002, a small and inexpensive device, which was not even called a cleaning robot, brought the first breakthrough: Roomba. Sales of the Roomba quickly passed the first million robots and increased rapidly. While for the first years after Roomba’s release, the big players remained on the sidelines, possibly to revise their own designs and, in particular their business models and price tags, some other small players followed quickly and came out with their own products. We reported about theses devices and their creators in the first edition. Since then the momentum in the field of domestics robotics has steadily increased. Nowadays most big appliance manufacturers have domestic cleaning robots in their portfolio. We are not only seeing more and more domestic cleaning robots and lawn mowers on the market, but we are also seeing new types of domestic robots, window cleaners, plant watering robots, tele-presence robots, domestic surveillance robots, and robotic sports devices. Some of these new types of domestic robots are still prototypes or concept studies. Others have already crossed the threshold to becoming commercial products. For the second edition of this chapter, we have decided to not only enumerate the devices that have emerged and survived in the past five years, but also to take a look back at how it all began, contrasting this retrospection with the burst of progress in the past five years in domestic cleaning robotics. We will not describe and discuss in detail every single cleaning robot that has seen the light of the day, but select those that are representative for the evolution of the technology as well as the market. We will also reserve some space for new types of mobile domestic robots, which will be the success stories or failures for the next edition of this chapter. Further we will look into nonmobile domestic robots, also called smart appliances, and examine their fate. Last but not least, we will look at the recent developments in the area of intelligent homes that surround and, at times, also control the mobile domestic robots and smart appliances described in the preceding sections.

Long-lived complex electromechanical systems, such as vehicles or industrial machinery, often need to be adapted for new uses or new environments. Adapting the design for such a system is frequently complicated by the fact that they are often tightly integrated, such that any change will have consequences throughout the design, and must take many different aspects of the system into consideration. Functional blueprints simplify adaptation by incorporating the reasons for design decisions and their consequences directly into the specification of a system. This allows a human designer to be supported by automated reasoning that can identify potential conflicts, suggest design fixes, and propagate changes implicit in the choices of the designer. This chapter presents the functional blueprints approach in detail, including both review of prior work and new results.

We discuss an experiment investigating the influence of social cues expressed by a robot on human attributions of interpersonal characteristics towards a robot and assessments of its interaction behaviors. During a hallway navigation scenario, participants were exposed to varying expressions of proxemic behavior and gaze cues over repeated interactions with a robot. Analysis of participant perceptions of the robot’s personality revealed that cues indicative of socially mindful behavior expressed by the robot promote positive interpersonal attributions and perceptions of safe robot behavior. Results of the present study contribute to the scholarly discussion on robotic design for encouraging natural and effective interactions between humans and robots.

Certain embodiments of the present invention provide robotic control modules for use in a robotic control system of a vehicle, including structures, systems and methods, that can provide (i) a robotic control module that has multiple functional circuits, such as a processor and accompanying circuits, an actuator controller, an actuator amplifier, a packet network switch, and a power supply integrated into a mountable and/or stackable package/housing; (ii) a robotic control module with the noted complement of circuits that is configured to reduce heat, reduce space, shield sensitive components from electro-magnetic noise; (iii) a robotic control system utilizing robotic control modules that include the sufficiently interchangeable functionality allowing for interchangeability of modules; and (iv) a robotic control system that distributes the functionality and processing among a plurality of robotic control modules in a vehicle.

An obstacle detector for a mobile robot while the robot is in motion is disclosed. The detector preferably includes at least one light source configured to project pulsed light in the path of the robot; a visual sensor for capturing a plurality of images of light reflected from the path of the robot; a processing unit configured to extract the reflections from the images; and an obstacle detection unit configured to detect an obstacle in the path of the robot based on the extracted reflections. In the preferred embodiment, the reflections of the projected light are extracted by subtracting pairs of images in which each pair includes a first image captured with the at least one light source on and a second image captured with the at least one light source off, and then combining images of two or more extracted reflections to suppress the background.

Searching for objects and observing parts of a known environment efficiently is a fundamental problem in many real-world robotic applications, e.g., household robots searching for objects, inspection robots searching for leaking pipelines, and rescue robots searching for survivors after a disaster. We consider the problem of identifying and planning sequences of sensor locations from which robot sensors can observe and cover complex three-dimensional (3D) environments while traveling only short distances. Our approach is based on sampling and ranking a large number of sensor locations for a 3D environment represented by an OctoMap. The visible area from these sensor locations induces a minimal partition of the 3D environment that we exploit for planning sequences of sensor locations with short travel times efficiently. We present multiple planning algorithms designed for single robots and for multirobot teams. These algorithms include variants that are greedy, optimal, or based on decomposing the planning problem into a set cover and traveling salesman problem. We evaluated and compared these algorithms empirically in simulation and real-world robot experiments with up to four robots. Our results demonstrate that, despite the intractability of the overall problem, computing and executing effective solutions for multirobot coverage search in real 3D environments is feasible and ready for real-world applications.