Author name cluster

Alberto Candela

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7 papers

2 author rows

ICRA Conference 2024 Conference Paper

Dynamic Targeting of Satellite Observations Incorporating Slewing Costs and Complex Observation Utility

Akseli Kangaslahti
Alberto Candela
Jason Swope
Qing Yue
Steve A. Chien

Maximizing the utility of limited Earth observing satellite resources is a difficult ongoing problem. Dynamic Targeting is an approach to this challenge that intelligently plans and executes primary sensor observations based on information from a look-ahead sensor. However, current implementations have failed to account for realistic satellite operational constraints and have used static utility for repeat observations of the same target. To address these limitations, we implement a more general Dynamic Targeting framework that comprises a physics-based slew model, a dynamic model of observation utility, and an algorithm for gathering high-utility observations. To demonstrate this framework, we also supply complex dynamic utility models that are applicable to many missions and new algorithms for intelligently scheduling observations with slewing restrictions and changing utility, including a greedy algorithm and a depth-first search algorithm. To evaluate these algorithms, we test their performance across simulated runs through two datasets and compare to the performance of an algorithm representative of most scheduling algorithms aboard Earth science missions today as well as an intractable upper bound. We show that our algorithms have great potential to improve science return from Earth science missions.

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ICRA Conference 2023 Conference Paper

Multi-Objective Ergodic Search for Dynamic Information Maps

Ananya Rao
Abigail Breitfeld
Alberto Candela
Benjamin Jensen
David Wettergreen
Howie Choset

Robotic explorers are essential tools for gathering information about regions that are inaccessible to humans. For applications like planetary exploration or search and rescue, robots use prior knowledge about the area to guide their search. Ergodic search methods find trajectories that effectively balance exploring unknown regions and exploiting prior information. In many search based problems, the robot must take into account multiple factors such as scientific information gain, risk, and energy, and update its belief about these dynamic objectives as they evolve over time. However, existing ergodic search methods either consider multiple static objectives or consider a single dynamic objective, but not multiple dynamic objectives. We address this gap in existing methods by presenting an algorithm called Dynamic Multi-Objective Ergodic Search (D-MO-ES) that efficiently plans an ergodic trajectory on multiple changing objectives. Our experiments show that our method requires up to nine times less compute time than a naïve approach with comparable coverage of each objective.

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HAXP Workshop 2023 Workshop Paper

Outcome Prediction and Explainability for Mission Operations of Autonomous Spacecraft

Alberto Candela
Tiago Vaquero
Bennett Huffman
Nihal Dhamani
Federico Rossi
Rebecca Castano

As planning and autonomy in general become increasingly deployed on board spacecraft, missions will face a paradigm shift in how ground operations teams command and interact with the spacecraft: moving from specifying timed sequences of commands to high-level goals that on-board autonomy will elaborate based on the spacecraft’s state and sensed environment. It will become increasingly difficult for operators to predict a mission's outcome as autonomous spacecraft venture into deeper space, react to unknown conditions, and face stronger communication constraints. However, data from simulated autonomous missions can be analyzed and leveraged, allowing operators to make informed decisions when selecting mission parameters and high-level goals. To this end, our paper presents a framework that gains insights from simulation data in order to help operators of autonomous spacecraft missions to predict, explain, and search for specific outcomes given a set of high-level goals. We show and discuss how our approach can help operators to better understand predictions, explore options and make informed decisions. To this end, we describe a case study that builds upon previous work on simulated autonomous spacecraft missions to the Neptune-Triton system where the spacecraft uses an automated planning and execution framework to make onboard decisions.

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ICRA Conference 2020 Conference Paper

Planetary Rover Exploration Combining Remote and In Situ Measurements for Active Spectroscopic Mapping

Alberto Candela
Suhit Kodgule
Kevin Edelson
Srinivasan Vijayarangan
David R. Thompson 0001
Eldar Noe Dobrea
David Wettergreen

Maintaining high levels of productivity for planetary rover missions is very difficult due to limited communication and heavy reliance on ground control. There is a need for autonomy that enables more adaptive and efficient actions based on real-time information. This paper presents an autonomous mapping and exploration approach for planetary rovers. We first describe a machine learning model that actively combines remote and rover measurements for mapping. We focus on spectroscopic data because they are commonly used to investigate surface composition. We then incorporate notions from information theory and non-myopic path planning to improve exploration productivity. Finally, we demonstrate the feasibility and successful performance of our approach via spectroscopic investigations of Cuprite, Nevada; a well-studied region of mineralogical and geological interest. We first perform a detailed analysis in simulations, and then validate those results with an actual rover in the field in Nevada.

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IROS Conference 2019 Conference Paper

Non-myopic Planetary Exploration Combining In Situ and Remote Measurements

Suhit Kodgule
Alberto Candela
David Wettergreen

Remote sensing measurements can provide crucial information about the material properties of a planetary surface but their application is limited by their spatial resolution, typically tens of meters per pixel, when constituent materials are mixed at much finer scale. Consequently the orbital observations must be validated with in situ measurements from a spectrometer on the ground. In planetary exploration this means that a rover must visit selected locations that jointly improve a model of the environment and satisfy mobility and sampling constraints. Conventional planning methods used in this situation follow sub-optimal greedy strategies that are not scalable to large areas. We show how the problem can be effectively defined in a Markov Decision Process framework and propose a planning algorithm based on Monte Carlo Tree Search, which is efficient but devoid of these drawbacks thereby providing superior performance. We evaluate our approach using hyperspectral imagery of a well-studied geologic site in Cuprite, Nevada.

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IROS Conference 2017 Conference Paper

Planetary robotic exploration driven by science hypotheses for geologic mapping

Alberto Candela
David R. Thompson 0001
Eldar Noe Dobrea
David Wettergreen

Planetary exploration involves frequent scientific reformulation and replanning. It is limited by communication constraints and to overcome this limitation, this paper formulates the process as a collaboration in which the human scientist and the robot work together to fill in gaps in knowledge to make discoveries. It introduces the science hypothesis map as the probabilistic structure in which scientists initially describe their abstract beliefs and hypotheses, and in which the state of this belief evolves as the robot makes raw measurements. It discusses how to incorporate path planning for maximizing scientific information gain, which is efficiently computed. As proof of concept, this paper describes a geologic exploration problem where a robot uses a spectrometer to infer the geologic composition of different regions in a mining district at Cuprite, Nevada. It shows that the science hypothesis map can infer geologic units with high accuracy, and that exploration using information gain-based path planning has better performance than exploration with conventional science-blind algorithms.

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IROS Conference 2017 Conference Paper

Science-aware exploration using entropy-based planning

Shivam Gautam
Bishwamoy Sinha Roy
Alberto Candela
David Wettergreen

Efficient exploration of unknown terrains by extraterrestrial rovers requires the development of strategies that reduce the entropy in the geological classification of a given terrain. Without such intelligent strategies, teleoperation of the rover is reliant either on human intuition or on the exhaustive exploration of the entire terrain. This paper highlights the use of low-resolution reconnaissance using satellite imagery to generate plans for rovers that reduce the overall uncertainty in the various geological classes. This becomes pivotal when exploration to collect diverse samples is resource constrained through exploration budgets and transmission bandwidths. We put forward two major contributions - a science-aware planner that uses information gain and a novel method of estimating this information gain. We propose an exploration strategy, based on the Multi-Heuristic A*, to solve the trade-off between optimizing path lengths and geological exploration through Pareto-optimal solutions. We show that our algorithm, which explicitly uses projected entropy-reduction in planning, significantly outperforms science-agnostic approaches and other science-aware strategies like greedy best-first searches. We further propose a feature-space based entropy formulation in contrast to the frequently used differential entropy formulation and show superior results when reconstructing the unsampled data from the set of sampled points.

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