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 address the problem of synchronized convergence of multiple Unmanned Vehicles (UVs) onto a single moving and maneuvering target in a GPS-denied environment with each agent having fractionated information and sensing constraints. The UVs (or agents) are considered to be distributed over a topographical region with limited sensing range and field-of-view (FOV) as compared to the expanse of the region under consideration. These constraints are addressed using cooperative localization technique augmented with Proportional Navigation (PN) guidance law. Using nonlinear observability analysis, it is shown that the combined algorithm proposed in this paper keeps the states of target and UVs observable as long as the observability criteria are not violated. Further, simulations are performed to show that the theory is consistent and is able to perform simultaneous convergence.

Cooperative Simultaneous Arrival of Unmanned Vehicles onto a Moving Target in GPS-Denied Environment

Target-Centric Formation Control in GPS-denied Environments

In this paper, we develop a method to estimate the relative position and relative heading of an Unmanned Aerial Vehicle (UAV) attempting to land on a moving platform using range-only measurements under GPS-denied conditions. Landing problems typically require precise estimation of relative position and heading, the proposed solution estimates relative pose between the platform (in this case a ship deck) and the landing multirotor. Vision-based landing techniques although accurate will fail under hostile weather conditions with the lack of Global Positioning System (GPS). This formulation investigates the effect on estimation quality when a single vehicle attempts to land using measurements from the ship only versus a team of multirotors assisting a vehicle to land on the ship. A multi-vehicle simulator has been developed in MATLAB/Simulink that facilitates testing our approaches. The simulation results clearly demonstrate the advantage of using a team for assisted landing rather than alone vehicle performing relative pose estimation using a limited range measurements.

Cooperative Relative Pose Estimation to Aid Landing of an Unmanned Aerial Vehicle on a Moving Platform

In this paper, we develop a landing controller based on sliding-mode control law for landing a UAV on a moving ship using relative estimates in GPS-denied environments with range-only measurements Precise knowledge of relative position, orientation and velocity is required to accurately land an Unmanned Aerial Vehicle (UAV) onto the surface of a moving platform Although vision-based techniques have been used to previously solve such problems, they fail in dark or hostile weather conditions and also a line of sight is required with the landing platform at all times Cooperation among different Unmanned Vehicles (UVs) have been introduced to aid the estimation process We also investigate how different trajectories followed by supporting vehicles effect the localization accuracy A Matlab/Simulink simulator has been created and exhaustive simulations have been performed that demonstrates the effect of these trajectories on localization accuracy

Development of Sliding-Mode Landing Controller using Cooperative Relative Localization with Pattern Generation

This chapter provides a brief introduction to the cooperative localization problem, and some of the widely used techniques were discussed along with the mathematical analysis for convergence. The problem of cooperative localization is used to facilitate navigation in global positioning system-denied environments using the vehicle's motion information from its internal sensors and the information about its surroundings from its external sensors. Several different algorithms with various modifications have been employed to approach the problem of cooperative localization in both the centralized and distributed manner. Since maintaining system observability is crucial to ensuring filter consistency and in turn reliable estimates, quite a lot of attention has been devoted by several authors to develop controllers that can increase the information available to the system or maintain paths that will improve system observability. Cooperative localization has been employed by authors to solve complex problems such as landing Unmanned aerial vehicle on moving platforms such as cars or ships.

Anusna Chakraborty

Papers

Cooperative Simultaneous Arrival of Unmanned Vehicles onto a Moving Target in GPS-Denied Environment

Target-Centric Formation Control in GPS-denied Environments

Cooperative Relative Pose Estimation to Aid Landing of an Unmanned Aerial Vehicle on a Moving Platform

Development of Sliding-Mode Landing Controller using Cooperative Relative Localization with Pattern Generation

Cooperative Localization: Challenges and Future Directions