Self-assembly enables nature to build complex forms, from multicellular organisms to complex animal structures such as flocks of birds, through the interaction of vast numbers of limited and unreliable individuals. Creating this ability in engineered systems poses challenges in the design of both algorithms and physical systems that can operate at such scales. We report a system that demonstrates programmable self-assembly of complex two-dimensional shapes with a thousand-robot swarm. This was enabled by creating autonomous robots designed to operate in large groups and to cooperate through local interactions and by developing a collective algorithm for shape formation that is highly robust to the variability and error characteristic of large-scale decentralized systems. This work advances the aim of creating artificial swarms with the capabilities of natural ones.

Programmable self-assembly in a thousand-robot swarm

Environment models serve as important resources for an autonomous robot by providing it with the necessary task-relevant information about its habitat. Their use enables robots to perform their tasks more reliably, flexibly, and efficiently. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models: for manipulation purposes their models have to include the objects present in the world, together with their position, form, and other aspects, as well as an interpretation of these objects with respect to the robot tasks. The dissertation presented in this article (Rusu, PhD thesis, 2009) proposes Semantic 3D Object Models as a novel representation of the robot’s operating environment that satisfies these requirements and shows how these models can be automatically acquired from dense 3D range data.

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Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

Visual SLAM (simultaneous localization and mapping) refers to the problem of using images, as the only source of external information, in order to establish the position of a robot, a vehicle, or a moving camera in an environment, and at the same time, construct a representation of the explored zone. SLAM is an essential task for the autonomy of a robot. Nowadays, the problem of SLAM is considered solved when range sensors such as lasers or sonar are used to built 2D maps of small static environments. However SLAM for dynamic, complex and large scale environments, using vision as the sole external sensor, is an active area of research. The computer vision techniques employed in visual SLAM, such as detection, description and matching of salient features, image recognition and retrieval, among others, are still susceptible of improvement. The objective of this article is to provide new researchers in the field of visual SLAM a brief and comprehensible review of the state-of-the-art.

Visual simultaneous localization and mapping: a survey

The Kiva warehouse-management system creates a new paradigm for pick-pack-and-ship warehouses that significantly improves worker productivity. The Kiva system uses movable storage shelves that can be lifted by small, autonomous robots. By bringing the product to the worker, productivity is increased by a factor of two or more, while simultaneously improving accountability and flexibility. A Kiva installation for a large distribution center may require 500 or more vehicles. As such, the Kiva system represents the first commercially available, large-scale autonomous robot system. The first permanent installation of a Kiva system was deployed in the summer of 2006.

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Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses

This paper addresses the reactive collision avoidance for vehicles that move in very dense, cluttered, and complex scenarios. First, we describe the design of a reactive navigation method that uses a "divide and conquer" strategy based on situations to simplify the difficulty of the navigation. Many techniques could be used to implement this design (since it is described at symbolic level), leading to new reactive methods that must be able to navigate in arduous environments (as the difficulty of the navigation is simplified). We also propose a geometry-based implementation of our design called the nearness diagram navigation. The advantage of this reactive method is to successfully move robots in troublesome scenarios, where other methods present a high degree of difficulty in navigating. We show experimental results on a real vehicle to validate this research, and a discussion about the advantages and limitations of this new approach.

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Nearness diagram (ND) navigation: collision avoidance in troublesome scenarios

We describe the development of Centibots, a framework for very large teams of robots that are able to perceive, explore, plan and collaborate in unknown environments.

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Centibots: very large scale distributed robotic teams

Scene modeling has a key role in applications ranging from visual mapping to augmented reality. This paper presents an end-to-end solution for creating accurate three-dimensional (3D) textured models using monocular video sequences. The methods are developed within the framework of sequential structure from motion, in which a 3D model of the environment is maintained and updated as new visual information becomes available. The proposed approach contains contributions at different levels. The camera pose is recovered by directly associating the 3D scene model with local image observations, using a dual-registration approach. Compared to the standard structure from motion techniques, this approach decreases the error accumulation while increasing the robustness to scene occlusions and feature association failures, while allowing 3D reconstructions for any type of scene. Motivated by the need to map large areas, a novel 3D vertex selection mechanism is proposed, which takes into account the geometry of the scene. Vertices are selected not only to have high reconstruction accuracy but also to be representative of the local shape of the scene. This results in a reduction in the complexity of the final 3D model, with minimal loss of precision. As a final step, a composite visual map of the scene (mosaic) is generated. We present a method for blending image textures using 3D geometric information and photometric differences between registered textures. The method allows high-quality mosaicing over 3D surfaces by reducing the effects of the distortions induced by camera viewpoint and illumination changes. The results are presented for four scene modeling scenarios, including a comparison with ground truth under a realistic scenario and a challenging underwater data set. Although developed primarily for underwater mapping applications, the methods are general and applicable to other domains, such as aerial and land-based mapping. © 2009 Wiley Periodicals, Inc.

Leaving Flatland: Efficient real-time three-dimensional perception and motion planning

Centibots: Very Large Scale Distributed Robotic Teams.

We present an integrated approach to multirobot exploration, mapping and searching suitable for large teams of robots operating in unknown areas lacking an existing supporting communications infrastructure. We present a set of algorithms that have been both implemented and experimentally verified on teams--of what we refer to as Centibots--consisting of as many as 100 robots. The results that we present involve search tasks that can be divided into a mapping stage in which robots must jointly explore a large unknown area with the goal of generating a consistent map from the fragment, a search stage in which robots are deployed within the environment in order to systematically search for an object of interest, and a protection phase in which robots are distributed to track any intruders in the search area. During the first stage, the robots actively seek to verify their relative locations in order to ensure consistency when combining data into shared maps; they must also coordinate their exploration strategies so as to maximize the efficiency of exploration. In the second and third stages, robots allocate search tasks among themselves; since tasks are not defined a priori, the robots first produce a topological graph of the area of interest and then generate a set of tasks that reflect spatial and communication constraints. Our system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust.

Distributed multirobot exploration, mapping, and task allocation

We report our first experiences with Leaving Flatland, an exploratory project that studies the key challenges of closing the loop between autonomous perception and action on challenging terrain. We propose a comprehensive system for localization, mapping, and planning for the RHex mobile robot in fully 3D indoor and outdoor environments. This system integrates Visual Odometry-based localization with new techniques in real-time 3D mapping from stereo data. The motion planner uses a new decomposition approach to adapt existing 2D planning techniques to operate in 3D terrain. We test the map-building and motion-planning subsystems on real and synthetic data, and show that they have favorable computational performance for use in high-speed autonomous navigation.

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Benoit Morisset

Papers

Centibots: very large scale distributed robotic teams

Leaving Flatland: Efficient real-time three-dimensional perception and motion planning

Centibots: Very Large Scale Distributed Robotic Teams.

Distributed multirobot exploration, mapping, and task allocation

Leaving Flatland: Toward real-time 3D navigation