UGV-to-UAV cooperative ranging for robust navigation in GNSS-challenged environments

Flight-testing of a cooperative UGV-to-UAV strategy for improved positioning in challenging GNSS environments

The multi-robot coverage motion planning (MCMP) problem in which every reachable area must be covered is common in multi-robot systems. To deal with the MCMP problem, we propose an efficient, complete, and off-line algorithm, named the “auction-based spanning tree coverage (A-STC)” algorithm. First, the configuration space is divided into mega cells whose size is twice the minimum coverage range of a robot. Based on connection relationships among mega cells, a graph structure can be obtained. A robot that circumnavigates a spanning tree of the graph can generate a coverage trajectory. Then, the proposed algorithm adopts an auction mechanism to construct one spanning tree for each robot. In this mechanism, an auctioneer robot chooses a suitable vertex of the graph as an auction item from neighboring vertexes of its spanning tree by heuristic rules. A bidder robot submits a proper bid to the auctioneer according to the auction vertexes’ relationships with the spanning tree of the robot and the estimated length of its trajectory. The estimated length is calculated based on vertexes and edges in the spanning tree. The bidder with the highest bid is selected as a winner to reduce the makespan of the coverage task. After auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm and the method for estimating trajectory lengths. The proposed algorithm is also compared with the state-of-the-art algorithms. The comparative results show that the A-STC algorithm has apparent advantages in terms of the running time and the makespan for large crowded configuration spaces.

A-STC: auction-based spanning tree coverage algorithm formotion planning of cooperative robots

Triangular Orthogonal Functions for the Analysis of Continuous Time Systems: Walsh, Block Pulse, and Related Orthogonal Functions in Systems and Control

Recent technologies, such as, Internet of Things and cloud services, increases the usage of small and low-cost networked unmanned aerial vehicles (UAVs), which needs to be robust against malicious global positioning system (GPS) attacks. Due to the availability of low-cost GPS jammers in the commercial market, there has been a rising risk of multiple jammers and not just one. However, it is challenging to locate multiple jammers because the traditional jammer localization via multilateration is applicable for only a single jammer case. Also, during a jamming attack, the positioning capability of an on-board GPS receiver is compromised given its inability to track GPS signals. We propose a simultaneous localization of multiple jammers and receivers (SLMR) algorithm by analyzing the variation in the front-end signal power recorded by the GPS receivers on-board a network of UAVs. Our algorithm not only locates multiple jammers but also utilizes these malicious sources as additional navigation signals for positioning the UAVs. We design a Gaussian mixture probability hypothesis density filter over a graph framework, which is optimized using a Levenberg–Marquardt minimizer. Using a simulated experimental setup, we validate the convergence and localization accuracy of our SLMR algorithm for various cases, including attacks with a single jammer, multiple jammers, and a varying number of jammers. We also demonstrate that our SLMR algorithm is able to simultaneously locate multiple jammers and UAVs, even for a larger transmitted power of the jammers.

Locating Multiple GPS Jammers Using Networked UAVs

In this paper, we investigate how relative measurements between fixed wing unmanned aerial vehicles (UAVs) and known landmarks can be used to cooperatively localize UAVs when GPS signals are not available. A centralized Extended Kalman Filter (EKF) is used to combine local sensor information from all the UAVs to estimate the required states of all the UAVs. We compare the localization accuracy of the cooperative localization algorithm for different relative measurements such as range, bearing, range rate, and line-of-sight rate. The dynamics, IMU, airspeed, and altimeter data used in the algorithm were collected from flight tests by Naval Postgraduate School [1]. However, the relative measurements are simulated using the flight data. The results are then compared against the GPS data available from the flight test.

Cooperative localization for fixed wing unmanned aerial vehicles

In this paper, we solve a discrete-time bearing-only cooperative localization problem for a team of autonomous vehicles with a special focus on switching sensing topology. A centralized Extended Ka...

Observability Conditions for Switching Sensing Topology for Cooperative Localization

Cooperative Localization for Multi-rotor UAVs

The ability for autonomous vehicles to cooperatively navigate, especially in GPS denied environments, is becoming increasingly important. It also requires the ability to initialize, or reinitialize estimation algorithms for cooperative systems on-the-fly in cases where precise a priori state information is unavailable. In this paper, we provide a framework that allows estimation of the relative pose and orientation between vehicles in the presence of high initial uncertainty. Effects of cooperation among multiple vehicles exchanging estimates of heading rate and velocity and external sensor measurements are analyzed. A Multi-Hypothesis Extended Kalman Filter (MHEKF) technique is used to initialize pairwise vehicles using range-only measurements. Using solutions identified by the MHEKF algorithm, a joint filter comprising of multiple vehicles is initialized. Sufficient conditions to maintain bounded errors are derived through nonlinear observability analysis using Lie derivatives for the pairwise and the multi-vehicle cases. Using these conditions as passive constraints in the system, simulation and hardware experiments are performed to demonstrate the advantages of using MHEKF when the initial conditions are unreliable. A multi-vehicle testbed for heterogeneous platforms with different sensing modalities is developed to facilitate hardware testing. Improvements in the system performance when cooperation is introduced among vehicles is also highlighted through experiments.

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Cooperative Relative Localization Using Range Measurements Without a Priori Information

In this paper, a relative frame localization problem is addressed for pairs of vehicles with significant initial pose uncertainties. A relative frame Extended Kalman filter (EKF) is developed for the case where vehicles share odometry (body-frame delta position values), and are capable of getting inter-vehicle range measurements. The extended Kalman filter is not robust to large initial errors in this nonlinear system, therefore a Multi-Hypothesis approach is used to accommodate the unknown, up to approximate range, initial relative pose (relative position and orientation). This formulation provides a lightweight and robust relative initialization approach that identifies an accurate relative pose and would be appropriate for relative guidance, initialization of more complex (multi-vehicle) navigation approaches, or shared, synthetic aperture-like measurement processing. Results are presented for both simulated and hardware implementations of the filter.

Anusna Chakraborty

Papers

Cooperative localization for fixed wing unmanned aerial vehicles

Observability Conditions for Switching Sensing Topology for Cooperative Localization

Cooperative Localization for Multi-rotor UAVs

Cooperative Relative Localization Using Range Measurements Without a Priori Information

Relative Pose Estimation using Range-only Measurements with Large Initial Uncertainty