Joseph A. Djugash

Range-only SLAM for robots operating cooperatively with sensor networks

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

Efficient Multi-robot Search for a Moving Target

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 non-decreasing, 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 that are competitive with those generated by exponential algorithms.

Further results with localization and mapping using range from radio

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.

A Robust Method of Localization and Mapping Using Only Range

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 Cartesian 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 utilizes a polar parameterization to gain 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.

Coordinated Search in Cluttered Environments Using Range from Multiple Robots

In this paper, we describe real-time methods for incorporating non-line-of-sight range measurements into a framework for finding a non-adversarial target in cluttered environments using multiple robotic searchers. We extend previous coordinated search strategies to utilize information from noisy non-line-of-sight range measurements. Sensors using ultra-wideband radio are becoming available that provide range measurements to targets even when they are occluded. We present two Bayesian methods for updating the expected location of a mobile target and integrating these updates into planning. We present simulated results in a complex museum environment as well as on mobile robots. Our results show the success of our algorithms at utilizing information from measurements in a coordinated search framework.

Target tracking without line of sight using range from radio

We propose a framework for utilizing fixed ultra-wideband ranging radio nodes to track a moving target radio node in an environment without guaranteed line of sight or accurate odometry. For the case where the fixed nodes’ locations are known, we derive a Bayesian room-level tracking method that takes advantage of the structural characteristics of the environment to ensure robustness to noise. For the case of unknown fixed node locations, we present a two-step approach that first reconstructs the target node’s path using Gaussian Process Latent Variable models (GPLVMs) and then uses that path to determine the locations of the fixed nodes. We present experiments verifying our algorithm in an office environment, and we compare our results to those generated by online and batch SLAM methods, as well as odometry mapping. Our algorithm is successful at tracking a moving target node without odometry and mapping the locations of fixed nodes using radio ranging data that are both noisy and intermittent.

Decentralized mapping of robot-aided sensor networks

A key problem in the deployment of sensor networks is that of determining the location of each sensor such that subsequent data gathered can be registered. We would also like the network to provide localization for mobile entities, allowing them to navigate and explore the environment. In this paper, we present a robust decentralized algorithm for mapping the nodes in a sparsely connected sensor network using range- only measurements and odometry from a mobile robot. Our approach utilizes an extended Kalman filter (EKF) in polar space allowing us to model the nonlinearities within the range-only measurements using Gaussian distributions. We also extend this unimodal centralized EKF to a multi-modal decentralized framework enabling us to accurately model the ambiguities in range-based position estimation. Each node within the network estimates its position along with its neighbors position and uses a message-passing algorithm to propagate its belief to its neighbors. Thus, the global network localization problem is solved in pieces, by each node independently estimating its local network, greatly reducing the computation done by each node. We demonstrate the effectiveness of our approach using simulated and real-world experiments with little to no prior information about the node locations.

Preliminary Results in Tracking Mobile Targets Using Range Sensors from Multiple Robots

In urban search and rescue scenarios, human first responders risk their lives as they routinely encounter hazardous environments. A team of robots, equipped with various sensors, deployed in such an environment can be used to track emergency personnel such as firefighters, reducing the risk to human life. This paper explores techniques for tracking a mobile target and coordinating a team of robots, equipped with range-only sensors, through smoke-filled, high-temperature environments. The particular strengths of our tracking and cooperative control algorithms are identified through a set of simulated examples.

Tracking a moving target in cluttered environments with ranging radios

In this paper, we propose a framework for utilizing fixed, ultra-wideband ranging radio nodes to track a moving target node through walls in a cluttered environment. We examine both the case where the locations of the fixed nodes are known as well as the case where they are unknown. For the case when the fixed node locations are known, we derive a Bayesian room-level tracking method that takes advantage of the structural characteristics of the environment to ensure robustness to noise. We also develop a method using mixtures of Gaussians to model the noise characteristics of the radios. For the case of unknown fixed node locations, we present a two-step approach that first reconstructs the target nodes path and then uses that path to determine the locations of the fixed nodes. We reconstruct the path by projecting down from a higher-dimensional measurement space to the 2D environment space using non-linear dimensionality reduction with Gaussian Process Latent Variable Models(GPLVMs). We then utilize the reconstructed path to map the locations of the fixed nodes using a Bayesian occupancy grid. We present experimental results verifying our methods in an office environment. Our methods are successful at tracking a moving target node and mapping the locations of fixed nodes using radio ranging data that are both noisy and intermittent.

Motion-aided network SLAM with range

A key problem in the deployment of sensor networks is that of determining the location of each sensor such that subsequent data gathered can be registered. We would also like the network to provide localization for mobile entities, allowing them to navigate and explore the environment. In this paper, we present a thorough evaluation of our algorithm for localizing and mapping the mobile and stationary nodes in sparsely connected sensor networks using range-only measurements and odometry from the mobile node. Our approach utilizes an extended Kalman filter (EKF) in polar space allowing us to model the non-linearity within the range-only measurements using Gaussian distributions. Utilizing the motion information from a mobile node, we show additional improvements to the static network localization solution. In addition to this centralized filtering technique, an asynchronous and decentralized approach is investigated and experimentally proven. This decentralized filtering technique distributes the computation across all nodes in the network, leveraging their numbers for improved efficiency. We demonstrate the effectiveness of our approach using simulated and real-world experiments in challenging environments with limited network connectivity. Our results reveal that our proposed method offers good accuracy in these challenging environments even when little to no prior information is available. Additionally, it is shown that by initializing the network map with a static network solution, the network mapping with a mobile node can be further improved.