We address the problem of systematically covering all accessible floor space in an unknown environment by a mobile robot. Our approach uses rectangular regions that are swept across the environment. In the first stage, the robot covers each region using the classic boustrophedon pattern and planning paths to uncovered areas within the region while keeping track of its position uncertainty. The region is then moved sideways to cover the next part of the environment until all accessible space has been visited. In the second stage, the robot revisits the perimeter around the obstacles. We compare our method in terms of total trajectory length to 5 off-line methods including the distance transformation by Zelinsky et al. [1] in a standard test environment as well as in multi-room homes. The presented method has been employed in our Mint cleaning robot [2] for autonomously sweeping and mopping the floors.

We describe a method of generating and utilizing visual landmarks that is well suited for SLAM applications. The landmarks created are highly distinctive and reliably detected, virtually eliminating the data association problem present in other landmark schemes. Upon subsequent detections of a landmark, a 3-D pose can be estimated. The scheme requires a single camera.

This paper presents the Visual Simultaneous Localization and Mapping (vSLAMTM) algorithm, a novel algorithm for simultaneous localization and mapping (SLAM). The algorithm is vision-and odometry-based, and enables low-cost navigation in cluttered and populated environments. No initial map is required, and it satisfactorily handles dynamic changes in the environment, for example, lighting changes, moving objects and/or people. Typically, vSLAM recovers quickly from dramatic disturbances, such as “kidnapping”.

In this paper we describe the need for, and the characteristics of a software architecture for commercial robotic products. We describe the Evolution Robotics Software Platform (ERSP/spl trade/), which provides a commercial-grade software architecture for mobile robots. The architecture has been designed to be modular, scalable, lightweight, portable, and reusable. It follows a hybrid model of data flow, combining behavior-based processing modules for real-time reactions with event-based task planning routines. We provide a detailed description of the main architectural components and some basic usage studies. We also highlight areas where compromises have been made and for which continuing work is needed.

This paper presents a system for automatic reconstruction of 3D structure using two unsynchronized cameras. Three images are acquired sequentially from the left, right, and again from the left camera. A virtual image from the left camera synchronized with the right image is created by interpolating matching points of interest (SIFT features) in the two left images. Both geometric and probabilistic criteria are used to select the correct set of matching features amongst the three views. In an indoor environment, the method typically results in 3D structure with approximately 200 feature points, with a median 3D accuracy of 1.6 cm when the average depth is 3 m and the robot has moved 1-2 cm between each image acquisition.

Service robotics products are becoming a reality. This paper describes three core technologies that enable the next generation of service robots. They are low-cost, make use of low-cost hardware, and prepare for a short time-to-market for product development. The first technology is an object recognition system, which can be used by the robot to interact with the environment The second technology is a vision-based navigation system (vSLAM/spl trade/), which simultaneously can build a map and localize the robot in the map. Finally, the third technology is a flexible and rich software platform (ERSP/spl trade/) that assists developers in rapid design and prototyping of robotics applications.

Exploration of high risk terrain areas such as cliff faces and site construction operations by autonomous robotic systems on Mars requires a control architecture that is able to autonomously adapt to uncertainties in knowledge of the environment. We report on the development of a software/hardware framework for cooperating multiple robots performing such tightly coordinated tasks. This work builds on our earlier research into autonomous planetary rovers and robot arms. Here, we seek to closely coordinate the mobility and manipulation of multiple robots to perform examples of a cliff traverse for science data acquisition, and site construction operations including grasping, hoisting, and transport of extended objects such as large array sensors over natural, unpredictable terrain. In support of this work we have developed an enabling distributed control architecture called control architecture for multirobot planetary outposts (CAMPOUT) wherein integrated multirobot mobility and control mechanisms are derived as group compositions and coordination of more basic behaviors under a task-level multiagent planner. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. In this paper, we describe CAMPOUT, and its application to ongoing physical experiments with multirobot systems at the Jet Propulsion Laboratory in Pasadena, CA, for exploration of cliff faces and deployment of extended payloads.

We have developed a cliff robot that is capable of descending a cliff and autonomously navigating to way-points on the cliff wall. This aggressive mobility system consists of an ensemble of three tethered robots, which cooperate under tight coordinated control and collective state estimation. The distributed task is described as a behavior network, which consists of a network of controllers spread across the robots and which interact through communication links to achieve a collective control objective. Fielded experimental results show that the cliff robot is capable of navigating to designated way-points on a cliff wall using the proposed control scheme.

The objective of the Robot Work Crew (RWC) project is to investigate key challenges in multi-robot coordination when performing tightly coupled coordination tasks such as transporting and handling of long objects on challenging planetary terrain. In this paper, we focus on tightly coupled coordination of two Mars rovers transporting a long payload. We have developed practical decentralized compliancy control and coordinated comply control algorithms that effectively address compliant control for compliantly coupled multiple mobile robots. Experiments at the Jet Propulsion Lab in Pasadena, CA of two Mars rovers carry an extended payload over uneven, natural terrain are used to validate and illustrate the approach.

Integration of planning and execution for single-agent systems has received considerable attention and numerous interesting approaches have been proposed. This is not the case for multi-agent systems, however. We describe one approach to representing joint team activities using a finite state machine augmented with synchronization primitives for orchestration of group activities. This representation encodes a sequence of activities that can be distributed across a team of robots and executed in a coordinated manner. We then show how such a representation can be mapped onto a behavior-based formalism for execution, as supported by a distributed, multi-robot control architecture, CAMPOUT. We demonstrate the proposed mechanisms within the context of a challenging task, where two rovers collectively carry an extended container over rough terrain.

We propose an approach to multi-robot coordination in the context of cooperative target acquisition. The approach is based on multiple objective behavior coordination extended to multiple robots. It provides mechanisms for distributed command fusion across a group of robots to pursue multiple goals of multiple robots in parallel. The mechanisms enable each robot to select actions that not only benefit itself but also benefit the group as a whole. Experimental results with two mobile robots validate that, by using this method, a group of robots can successfully truck and acquire a moving target.

The visual robustness of biological systems is in part due to their ability to actively integrate (fuse) information from a number of visual cues. In addition to active integration, the perception-action nature of biological vision demands event-driven behavioral composition. Providing mechanical vision systems with similar capabilities therefore requires tools and techniques for cue integration and behavioral composition. In this paper, we address two issues. First, we present a unified approach for handling both active integration and behavioral composition. The approach combines a theoretical framework that handles uncertainty using a voting scheme with a set of behaviors that are committed to achieving a specific goal through common effort and a well-known process composition model. Secondly, we address the issue of integration in the active vision activity of smooth pursuit. We have experimented with the fusion of four smooth pursuit techniques (blob tracking, edge tracking, template matching and image differencing). We discuss each technique, highlighting their strengths and weaknesses, and then show that fusing the techniques according to our formal framework improves system tracking behavior.

In this paper we present a technique that can help us design system components (behaviors) with reliability in mind. Our approach is similar to N-version programming. A team of functionally redundant behaviors vote for a set of possible actions. A voter then fuses the votes and selects the action which has received the maximum number of votes. We draw some design guidelines and show that with careful design, the selected action will have higher probability of success (reliability) than the reliability of each of the behaviors. A comprehensive set of experiments together with results are presented in which a team of tracking behaviors control a stereo camera head in the active vision task of smooth pursuit. The results show that the approach dramatically improves tracking performance.

Through adaptation of theoretical techniques from control theory and computer science it becomes possible to provide well posed expert systems for the control of mobile robots. In contrast to other expert systems for mobile robotics the adapted framework enables structured design and verification of implemented systems. This in turn results in a modular system, and a system with a predictable performance. The authors describe how a behaviour based system can be controlled by a rule based expert system, where the interaction between behaviours is described in terms of a process algebra, where different processes are modelled as discrete event systems, and compositions are controlled by a supervisory control-structure. To verify the described framework experimental results obtained with a prototype system are also outlined.