DARP: Divide Areas Algorithm for Optimal Multi-Robot Coverage Path Planning

This paper deals with the path planning problem of a team of mobile robots, in order to cover an area of interest, with prior-defined obstacles. For the single robot case, also known as single robot coverage path planning (CPP), an (n) optimal methodology has already been proposed and evaluated in the literature, where n is the grid size. The majority of existing algorithms for the multi-robot case (mCPP), utilize the aforementioned algorithm. Due to the complexity, however, of the mCPP, the best the existing mCPP algorithms can perform is at most 16 times the optimal solution, in terms of time needed for the robot team to accomplish the coverage task, while the time required for calculating the solution is polynomial. In the present paper, we propose a new algorithm which converges to the optimal solution, at least in cases where one exists. The proposed technique transforms the original integer programming problem (mCPP) into several single-robot problems (CPP), the solutions of which constitute the optimal mCPP solution, alleviating the original mCPP explosive combinatorial complexity. Although it is not possible to analytically derive bounds regarding the complexity of the proposed algorithm, extensive numerical analysis indicates that the complexity is bounded by polynomial curves for practically sized inputs. In the heart of the proposed approach lies the DARP algorithm, which divides the terrain into a number of equal areas each corresponding to a specific robot, so as to guarantee complete coverage, non-backtracking solution, minimum coverage path, while at the same time does not need any preparatory stage (video demonstration and standalone application are available on-line http://tinyurl.com/DARP-app)

Exploration via Cost-Aware Subgoal Design

The problem of exploration in unknown environments continues to pose a challenge for reinforcement learning algorithms, as interactions with the environment are usually expensive or limited. The technique of setting subgoals with an intrinsic reward allows for the use of supplemental feedback to aid agent in environment with sparse and delayed rewards. In fact, it can be an effective tool in directing the exploration behavior of the agent toward useful parts of the state space. In this paper, we consider problems where an agent faces an unknown task in the future and is given prior opportunities to ``practice'' on related tasks where the interactions are still expensive. We propose a one-step Bayes-optimal algorithm for selecting subgoal designs, along with the number of episodes and the episode length, to efficiently maximize the expected performance of an agent. We demonstrate its excellent performance on a variety of tasks and also prove an asymptotic optimality guarantee.Comment: Presented at TARL, ICLR 2019 worksho

Hyper-spectral imaging for airborne meteorite detection

Meteorites are sought after by both scientists and enthusiasts due to their unique characteristics and the window they provide to the broader universe. Current meteorite collection methods are labour and resource intensive and return only relatively few finds in the context of the investment. The basis of this project was to investigate whether meteorites can be identified through a hyper-spectral camera which would be ultimately fitted to an unmanned aerial vehicle (UAV). Such an approach would allow greater geographic coverage of search areas, less human resources and potentially due to these factors, a greater return on investment. While work has been undertaken on identifying the spectral signatures of meteorites and on the use of hyper-spectral imaging in detection and identification, a search of the literature reveals that no earlier work on the use of hyper-spectral imaging for the identification and detection of meteorites. This project therefore builds on the more general work undertaken to apply hyper-spectral imaging to meteorite detection and identification. A key component of this project was the design and construction of a low cost hyper-spectral camera, which involved the development of two prototypes. Collection of hyper-spectral data, including of meteorites and known and unknown terrestrial rocks, was performed. This was then analysed for the presence of meteorites. The analysis and interpretation of this data required the research and development of a system to analyse the data to determine the presence and location of objects of interest. Ultimately this has produced a system that analyses hyper-spectral data to determine the the presence of particular types of meteorites under full sun lit conditions. The software that produces these results also logs the presence of the meteorites against the frame number and location of the find. The findings of the project indicate that hyper-spectral imaging is an appropriate way to detect and identify meteorites both at a pure spectral level and practically with imperfect equipment that relies upon reflections of sunlight off the sample materials. The project identifies further work which would allow meteorite detection from an aerial vehicle. While, the software which enables the meteorite detection system to perform hyper-spectral analysis and meteorite detection on board an aerial vehicle has been written, the hardware requires further work. The hardware (that is, the hyper-spectral camera) requires refinement to support its use on an aerial vehicle, including ensuring an appropriate level of robustness to support its use on an aerial vehicle in remote areas

Performance analysis of a random search algorithm for distributed autonomous mobile robots

Master'sMASTER OF ENGINEERIN

Automated Image Interpretation for Science Autonomy in Robotic Planetary Exploration

Advances in the capabilities of robotic planetary exploration missions have increased the wealth of scientific data they produce, presenting challenges for mission science and operations imposed by the limits of interplanetary radio communications. These data budget pressures can be relieved by increased robotic autonomy, both for onboard operations tasks and for decision- making in response to science data. This thesis presents new techniques in automated image interpretation for natural scenes of relevance to planetary science and exploration, and elaborates autonomy scenarios under which they could be used to extend the reach and performance of exploration missions on planetary surfaces. Two computer vision techniques are presented. The first is an algorithm for autonomous classification and segmentation of geological scenes, allowing a photograph of a rock outcrop to be automatically divided into regions by rock type. This important task, currently performed by specialists on Earth, is a prerequisite to decisions about instrument pointing, data triage, and event-driven operations. The approach uses a novel technique to seek distinct visual regions in outcrop photographs. It first generates a feature space by extracting multiple types of visual information from the image. Then, in a training step using labeled exemplar scenes, it applies Mahalanobis distance metric learning (in particular, Multiclass Linear Discriminant Analysis) to discover the linear transformation of the feature space which best separates the geological classes. With the learned representation applied, a vector clustering technique is then used to segment new scenes. The second technique interrogates sequences of images of the sky to extract, from the motion of clouds, the wind vector at the condensation level — a measurement not normally available for Mars. To account for the deformation of clouds and the ephemerality of their fine-scale features, a template-matching technique (normalized cross-correlation) is used to mutually register images and compute the clouds’ motion. Both techniques are tested successfully on imagery from a variety of relevant analogue environments on Earth, and on data returned from missions to the planet Mars. For both, scenarios are elaborated for their use in autonomous science data interpretation, and to thereby automate certain steps in the process of robotic exploration

Informationsfusion in der Mess- und Sensortechnik

Die Entwicklung und Beherrschung der immer komplexer werdenden technischen Systeme führt in zunehmenden Maße zu Anforderungen an die Mess- und Sensortechnik, die mit dem Einsatz eines Einzelsensors oft nicht mehr erfüllt werden können. Dies hat in den letzten Jahren zu einem starken Entwicklungsschub für Multisensorsysteme und die Grundlagenforschung zur Fusion von Messdaten und Information aus unterschiedlichen Quellen geführt. Dieses Buch greift diese Entwicklung sowohl hinsichtlich ihrer theoretischen Grundlagen als auch wichtiger Anwendungsfelder auf

Robotic Antarctic Meteorite Search: Outcomes

: Automation of the search for and classification of Antarctic meteorites offers a unique case for early demonstration of robotics in a scenario analogous to geological exploratory missions to other planets and to the Earth's extremes. Moreover, the discovery of new meteorite samples is of great value because meteorites are the only significant source of extraterrestrial material available to scientists. In this paper we focus on the primary outcomes and technical lessons learned from the first field demonstration of autonomous search and in situ classification of Antarctic meteorites by a robot. Using a novel autonomous control architecture, specialized science sensing, combined manipulation and visual servoing, and Bayesian classification, the Nomad robot classified five indigenous meteorites during an expedition to the remote site of Elephant Moraine in January 2000. Nomad's expedition proved the rudiments of science autonomy and exemplified the merits of machine learning techniques for autonomous geological classification in real-world settings. On the other hand, the expedition showcased the difficulty in executing reliable robotic deployment of science sensors and a limited performance in the speed and coverage of autonomous search. Keywords: Robotic Meteorite Search, Science Autonomy.