A Robust Relative Positioning System for Multi-Robot Formations Leveraging an Extended GM-PHD Filter

We propose a multi-robot tracking method to provide state estimates that allow a group of robots to maintain a formation even when the communication fails. We extend a Gaussian Mixture Probability Hypothesis Density filter to incorporate, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. Sensory detections, information about the formation, and communicated data are all combined in the extended Gaussian Mixture Probability Hypothesis Density filter. Our method is capable of maintaining the state estimates even when long-duration occlusions occur, and improves awareness of the situation when the communication rate is slow or sporadic. The method is evaluated using a high-fidelity simulator in scenarios with a formation of up to five robots. Experiments confirm the ability of the filter to deal with occlusions and refinement of the state estimate even when poses are exchanged at a low frequency, resulting in drastic reduction of the chance of collisions compared to a tracking-free implementation

Cooperative Position and Orientation Estimation with Multi-Mode Antennas

Robotic multi-agent systems are envisioned for planetary exploration and terrestrial applications. Autonomous operation of robots requires estimations of their positions and orientations, which are obtained from the direction-of-arrival (DoA) and the time-of-arrival (ToA) of radio signals exchanged among the agents. In this thesis, we estimate the signal DoA and ToA using a multi-mode antenna (MMA). An MMA is a single antenna element, where multiple orthogonal current modes are excited by different antenna ports. We provide a first study on the use of MMAs for cooperative position and orientation estimation, specifically exploring their DoA estimation capabilities. Assuming the agents of a cooperative network are equipped with MMAs, lower bounds on the achievable position and orientation accuracy are derived. We realize a gap between the theoretical lower bounds and real-world performance of a cooperative radio localization system, which is caused by imperfect antenna and transceiver calibration. Consequentially, we theoretically analyze in-situ antenna calibration, introduce an algorithm for the calibration of arbitrary multiport antennas and show its effectiveness by simulation. To also improve calibration during operation, we propose cooperative simultaneous localization and calibration (SLAC). We show that cooperative SLAC is able to estimate antenna responses and ranging biases of the agents together with their positions and orientations, leading to considerably better position and orientation accuracy. Finally, we validate the results from theory and simulation by experiments with robotic rovers equipped with software-defined radios (SDRs). In conclusion, we show that DoA estimation with an MMA is feasible, and accuracy can be improved by in-situ calibration and SLAC