This paper examines the problem of locating a mobile, non-adversarial target in an indoor environment using multiple robotic searchers. One way to formulate this problem is to assume a known environment and choose searcher paths most likely to intersect with the path taken by the target. We refer to this as the Multi-robot Efficient Search Path Planning (MESPP) problem. Such path planning problems are NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We present an approximation algorithm that utilizes finite-horizon planning and implicit coordination to achieve linear scalability in the number of searchers. We prove that solving the MESPP problem requires maximizing a nondecreasing, submodular objective function, which leads to theoretical bounds on the performance of our approximation algorithm. We extend our analysis by considering the scenario where searchers are given noisy non-line-of-sight ranging measurements to the target. For this scenario, we derive and integrate online Bayesian measurement updating into our framework. We demonstrate the performance of our framework in two large-scale simulated environments, and we further validate our results using data from a novel ultra-wideband ranging sensor. Finally, we provide an analysis that demonstrates the relationship between MESPP and the intuitive average capture time metric. Results show that our proposed linearly scalable approximation algorithm generates searcher paths competitive with those generated by exponential algorithms.

Abstract-A mobile robot we have developed is equipped with sensors to measure range to landmarks and can simultaneously localize itself as well as locate the landmarks. This modality is useful in those cases where environmental conditions preclude measurement of bearing (typically done optically) to landmarks. Here we extend the paradigm to consider the case where the landmarks (nodes of a sensor network) are able to measure range to each other. We show how the two capabilities are complimentary in being able to achieve a map of the landmarks and to provide localization for the moving robot. We present recent results with experiments on a robot operating in a randomly arranged network of nodes that can communicate via radio and range to each other using sonar. We find that incorporation of inter-node measurements helps reduce drift in positioning as well as leads to faster convergence of the map of the nodes. We find that addition of a mobile node makes the SLAM feasible in a sparsely connected network of nodes.

Summary. In this paper, we present recent results with using range from radio for mobile robot localization. In previous work we have shown how range readings from radio tags placed in the environment can be used to localize a robot. We have extended previous work to consider robustness. Specifically, we are interested in the case where range readings are very noisy and available intermittently. Also, we consider the case where the location of the radio tags is not known at all ahead of time and must be solved for simultaneously along with the position of the moving robot. We present results from a mobile robot that is equipped with GPS for ground truth, operating over several km.

Summary. In this paper we present results in mobile robot localization and simultaneous localization and mapping (SLAM) using range from radio. In previous work we have shown how range readings from radio tags placed in the environment can be used to localize a robot and map tag locations using a standard extended Kalman filter (EKF) that linearizes the probability distribution due to range measurements based on prior estimates. Our experience with this method was that the filter could perform poorly and even diverge in cases of missing data and poor initialization. Here we present a new formulation that gains robustness without sacrificing accuracy. Specifically, our method is shown to have significantly better performance with poor and even no initialization, infrequent measurements, and incorrect data association. We present results from a mobile robot equipped with high accuracy ground truth, operating over several kilometers. Motivation and Problem StatementHere we focus on the problems of robust localization and SLAM given range data between a mobile robot and static "tags". In the first case, the locations of the tags are known and the robot must accurately localize itself as it measures range to these tags while it moves. In the second case, the location of the tags are not known ahead of time and must be determined in addition to being used for localization. Hence our goal is to achieve a reliable and accurate estimate of pose for a robot operating in an environment containing radio tags to which the robot can measure range. This is a variant of a well known problem of SLAM for a robot using both range and bearing data to both map an environment and localize itself.Specifically, we would like a method that is robust to poor initialization, noise in measurement and missing data. We use three different datasets with over 2,000 range measurements each from a robot moving in a field of radio tags to compare the results from the proposed method to the classical standard EKF formulation. In each of the datasets, the robot travels over several km

Here we present five large data sets with range-only measurements between a mobile robot and stationary nodes. Each data set consists of range measurements, surveyed locations of the stationary radio nodes, dead-reckoned trajectory of the robot, and ground truth from a sophisticated inertial navigation system/global positioning system mounted on a robot traveling several kilometers at a time. Range measurements are made with two radio-based ranging systems: a RFID tag-based ranging system and an ultra-wide band ranging system. All the data are accurately time-stamped and presented in standard formats (i.e., text files). In addition to the raw data, we present some noise characteristics of the two different ranging systems to offer insight into the quality of the range data from each system. C 2009 Wiley Periodicals, Inc.