Planetary robotic exploration driven by science hypotheses for geologic mapping

Candela, Alberto; Thompson, David R.; Dobrea, E. Noe; Wettergreen, David

doi:10.1109/iros.2017.8206231

Cited by 16 publications

(13 citation statements)

References 25 publications

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“…11 must consider all possible combinations. In prior work, we demonstrated how this could be overcome using Monte Carlo integration (van den Berg et al, 2003), or more efficiently, by fitting generalized logistic function relationships to Monte Carlo results (Candela et al, 2017). Such measures are beyond the scope of this study, which focuses on the empirical performance of different sampling strategies.…”

Section: Entropy and Information Gainmentioning

confidence: 98%

“…Such an identification system could be employed on a rover, providing real-time materials identification. A probabilistic spatial model relates mineral identifications to investigation objectives, enabling adaptive information-driven exploration planning (Arora et al, 2017;Candela et al, 2017) while the robot is out of touch with operators. In the context of a human scientist and robotic explorer working together, this transforms the corobotic relationship from one in which the scientist plots a path into a collaboration in which the human and robot work together to fill in gaps in knowledge and make discoveries.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Spectroscopic Models for Remote Exploration

et al. 2018

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Ancient hydrothermal systems are a high-priority target for a future Mars sample return mission because they contain energy sources for microbes and can preserve organic materials (Farmer, 2000 ; MEPAG Next Decade Science Analysis Group, 2008 ; McLennan et al., 2012 ; Michalski et al., 2017 ). Characterizing these large, heterogeneous systems with a remote explorer is difficult due to communications bandwidth and latency; such a mission will require significant advances in spacecraft autonomy. Science autonomy uses intelligent sensor platforms that analyze data in real-time, setting measurement and downlink priorities to provide the best information toward investigation goals. Such automation must relate abstract science hypotheses to the measurable quantities available to the robot. This study captures these relationships by formalizing traditional "science traceability matrices" into probabilistic models. This permits experimental design techniques to optimize future measurements and maximize information value toward the investigation objectives, directing remote explorers that respond appropriately to new data. Such models are a rich new language for commanding informed robotic decision making in physically grounded terms. We apply these models to quantify the information content of different rover traverses providing profiling spectroscopy of Cuprite Hills, Nevada. We also develop two methods of representing spatial correlations using human-defined maps and remote sensing data. Model unit classifications are broadly consistent with prior maps of the site's alteration mineralogy, indicating that the model has successfully represented critical spatial and mineralogical relationships at Cuprite. Key Words: Autonomous science-Imaging spectroscopy-Alteration mineralogy-Field geology-Cuprite-AVIRIS-NG-Robotic exploration. Astrobiology 18, 934-954.

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Section: Entropy and Information Gainmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Spatial Spectroscopic Models for Remote Exploration

et al. 2018

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“…More recently, Candela et al explored a similar geological classification scenario to our Mars Lab experiments in Sec. VI [40]. Like our approach, Candela also used BNs for classification and applied existing information gain path planning algorithms for efficient sampling.…”

Section: A Science Autonomy In Space Roboticsmentioning

confidence: 99%

Multi-modal active perception for information gathering in science missions

et al. 2019

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Robotic science missions in remote environments, such as deep ocean and outer space, can involve studying phenomena that cannot directly be observed using on-board sensors but must be deduced by combining measurements of correlated variables with domain knowledge. Traditionally, in such missions, robots passively gather data along prescribed paths, while inference, path planning, and other high level decision making is largely performed by a supervisory science team. However, communication constraints hinder these processes, and hence the rate of scientific progress. This paper presents an active perception approach that aims to reduce robots' reliance on human supervision and improve science productivity by encoding scientists' domain knowledge and decision making process on-board. We use Bayesian networks to compactly model critical aspects of scientific knowledge while remaining robust to observation and modeling uncertainty. We then formulate path planning and sensor scheduling as an information gain maximization problem, and propose a sampling-based solution based on Monte Carlo tree search to plan informative sensing actions which exploit the knowledge encoded in the network. The computational complexity of our framework does not grow with the number of observations taken and allows long horizon planning in an anytime manner, making it highly applicable to field robotics. Simulation results show statistically significant performance improvements over baseline methods, and we validate the practicality of our approach through both hardware experiments and simulated experiments with field data gathered during the NASA Mojave Volatiles Prospector science expedition.

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“…This is important in a variety of situations, for example if interrogation theory is used to describe robotic decision-making over very long time periods where accumulated experience is significant, and where inspiration is important and must be simulated, such as will likely be required in future unmanned space missions to investigate other planets (e.g. for autonomous geological mapping see Candela et al 2017). This is discussed further below.…”

Section: Updating the State Of Knowledgementioning

confidence: 99%

Interrogation theory

Arnold

Curtis

2018

Geophysical Journal International

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A B S T R A C TThe goal of an investigation, scientific or otherwise, is usually to find answers to some specific set of questions about the state of nature: what is the seismic velocity structure? How likely is this volcano to erupt within a certain period? Does a subsurface reservoir contain resources of interest? Background research may reveal the existence of pertinent knowledge and information discovered previously, new data are normally acquired, and an inference problem is solved in order to answer the questions taking both all of the a priori information and the new data into account. Inverse theory, decision theory and the theory of experimental design provide methods to optimize the design of the investigation and to estimate results. However, those theories are normally set in the context of a particular model of the universe, with its particular parametrization. This requires the investigator to specify a priori a coherent utility (a function that describes the risks and rewards) of all possible outcomes under that parametrization. Quite commonly, the investigator may not be able to do this. Ideally an investigator would be able merely to pose a set of questions, define a set of constraints on the data types, acquisition costs and logistics, and provide a functional to relate the questions to any particular parameter space. Theory and methodology would then semi-autonomously drive the interrogation of the state of nature by optimally selecting one or more relevant models and parameter spaces, and designing, acquiring and analysing data, in order to best answer the questions. If necessary this could be done in a sequential or iterative manner, which potentially then involves changing the questions posed in each iteration based on both previous results and inspiration from the investigator. We present such a theory of interrogation in this paper. We review the relevant aspects of decision and design theory, and cast them in a framework where the investigator specifies a utility only at the level required by the general questions to be posed. Each model under consideration is then mapped into this utility space of possible answers. We then extend this framework to sequential investigations, where the outcome of each step may affect all aspects of the problem: the models entertained, the utilities and even the questions themselves. A variety of examples illustrates the generality of this method: an asset team investigating how best to exploit a subsurface reservoir, Monte Carlo sampling to estimate the Bayesian evidence for geophysical models, discriminating between different rock physics models of strain in laboratory deformation experiments, an organization sequentially assessing the effectiveness of its methods to evaluate subsurface assets, assessing whether subsurface CO 2 storage should be promoted for climate change mitigation, and examples running through the text of seismic tomography, earthquake characterization and autonomous interplanetary robotic exploration.

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Planetary robotic exploration driven by science hypotheses for geologic mapping

Cited by 16 publications

References 25 publications

Spatial Spectroscopic Models for Remote Exploration

Spatial Spectroscopic Models for Remote Exploration

Multi-modal active perception for information gathering in science missions

Interrogation theory

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