Simultaneous maximum-likelihood calibration of odometry and sensor parameters

For a differential-drive mobile robot equipped with an on-board range sensor, there are six parameters to calibrate: three for the odometry (radii and distance between the wheels), and three for the pose of the sensor with respect to the robot frame. This paper describes a method for calibrating all six parameters at the same time, without the need for external sensors or devices. Moreover, it is not necessary to drive the robot along particular trajectories. The available data are the measures of the angular velocities of the wheels and the range sensor readings. The maximum-likelihood calibration solution is found in a closed form

Self-Organized UWB Localization for Robotic Swarm – First Results from an Analogue Mission on Volcano Etna

At the Institute of Communications and Navigation of the German Aerospace Center (DLR), we have studied and developed radio-based swarm navigation technologies for a decade. In this paper, we provide a complete solution of ultra-wide band (UWB) localization network for a robotic swarm. This network is organized in a fully decentralized fashion and resilient to clock imperfections, topology changes, packet loss and the hidden node problem. In this network, a multitude of active devices and an arbitrary number of passive devices can exploit the UWB signals for self-localization, i.e. estimating their relative positions and orientations, without sophisticated clock and antenna calibration, which dramatically simplifies the design and manufacturing of such a swarm. Our proposed solution is verified with experiments and was successfully demonstrated in a space-analogue multi-robot surface exploration mission on the volcano Mt. Etna, Sicily, Italy, in July 2022

Enabling Distributed Low Radio Frequency Arrays - Results of an Analog Campaign on Mt. Etna

Measurement of the red-shifted 21-cm signal of neutral hydrogen, and thus observing The Dark Ages is expected to be the holy grail of 21-cm Cosmology. A Radio-telescope to observe low radio frequency signals is needed, but radio interference on Earth and Earth's ionosphere blocking these signals are limiting science investigations in this field. Hence, such a radio-telescope composed of dozens to hundreds of antennas shall be deployed on the lunar far side. Such arrays are shielded from interference from Earth and Earth's ionosphere blocking very low radio frequencies is not present. Within the Helmholtz Future Topic Project Autonomous Robotic Networks to Help Modern Societies (ARCHES) we developed necessary technologies for autonomous robotic deployment of antenna elements, modular payload box design, and robust radio-localization to enable such distributed low-frequency arrays. In particular the antennas’ positions must be determined accurately, such that the array can be operated as phased array. Our developments lead to the execution of an analog-demonstration on the volcano Mt. Etna, Sicily, Italy, in June and July 2022 over the course of four weeks. We successfully demonstrated the autonomous robotic deployment of antenna elements and our decentralized real-time radio-localization system to obtain the antenna element positions. Additionally, we showed a proof-of-concept operation of the phased array comprising four antenna elements: estimating the signal direction of arrival of a radio-beacon with unknown position, and the beamforming capabilities itself, for a carrier frequency of 20 MHz. In this paper, we give insights into our developed technologies and the analog-demonstration on the volcano Mt. Etna, Sicily, Italy. We show results of the successfully executed mission and give an outlook how our developed technologies can be further used for lunar exploration

Cooperative Radio Navigation for Robotic Exploration: Evaluation of a Space-Analogue Mission

Autonomous robotic systems will play an important role in future planetary exploration missions. To allow autonomous operation of robots, reliable navigation is vital. Such a navigation solution is provided by cooperative radio navigation, where radio signals are exchanged among the robots. Based on the signal round-trip time (RTT) and direction-of-arrival (DoA), the robots' positions and orientations are estimated. Cooperative navigation has been well studied theoretically, but experiments mainly focused on indoor scenarios and other applications. For the first time, we have demonstrated cooperative radio navigation within a space-analogue exploration mission with two robotic rovers. The mission took place on the volcano Mt Etna, Sicily, Italy. During the first part of the mission, simultaneous localization and calibration (SLAC) is performed to improve the accuracy of RTT and DoA estimates by reducing the bias. Then, the rovers travel to a distant area of interest. Ultimately, one rover travels so far that it is connected to the network only via another rover. We find that even in this challenging single-link scenario, robust cooperative navigation is achieved. When the rovers are not further than 60 m away from the lander, their position root-mean-square errors (RMSEs) are 0.3m to 0.9m. Even for the most challenging mission phase, when the rovers are 100 m to 160 m away from the lander with single-link localization, the position RMSEs are 1.7m to 2.6m. The orientation RMSEs of the rovers lie between 2.4° to 6.1°. Thus, with this space-analogue mission, we show that cooperative radio navigation for planetary exploration is robust and accurate

Simultaneous localization and odometry self calibration for mobile robot

This paper presents both the theory and the experimental results of a method allowing simultaneous robot localization and odometry error estimation (both systematic and non-systematic) during the navigation. The estimation of the systematic components is carried out through an augmented Kalman filter, which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. In this first filter, the non-systematic error is defined as constant and it is overestimated. Then, the estimation of the real non-systematic component is carried out through another Kalman filter, where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter. There, the systematic parameters in the model are regularly updated with the values estimated by the first filter. The approach is theoretically developed for both the synchronous and the differential drive. A first validation is performed through very accurate simulations where both the drive systems are considered. Then, a series of experiments are carried out in an indoor environment by using a mobile platform with a differential driv

Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the 'bout' detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m²) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments

Watch Your Step! Terrain Traversability for Robot Control

Watch your step! Or perhaps, watch your wheels. Whatever the robot is, if it puts its feet, tracks, or wheels in the wrong place, it might get hurt; and as robots are quickly going from structured and completely known environments towards uncertain and unknown terrain, the surface assessment becomes an essential requirement. As a result, future mobile robots cannot neglect the evaluation of terrain’s structure, according to their driving capabilities. With the objective of filling this gap, the focus of this study was laid on terrain analysis methods, which can be used for robot control with particular reference to autonomous vehicles and mobile robots. Giving an overview of theory related to this topic, the investigation not only covers hardware, such as visual sensors or laser scanners, but also space descriptions, such as digital elevation models and point descriptors, introducing new aspects and characterization of terrain assessment. During the discussion, a wide number of examples and methodologies are exposed according to different tools and sensors, including the description of a recent method of terrain assessment using normal vectors analysis. Indeed, normal vectors has demonstrated great potentialities in the field of terrain irregularity assessment in both on‐road and off‐road environments

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen