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Altitude-controlled balloons hold great promise for performing high-priority scientific investigations of Venus's atmosphere and geological phenomena, including tectonic and volcanic activity, as demonstrated by a number of recent Earth-based experiments. In this paper, we explore a concept of operations where multiple autonomous, altitude-controlled balloons monitor explosive volcanic activity on Venus through infrasound microbarometers, and autonomously navigate the uncertain wind field to perform follow-on observations of detected events of interest. We propose a novel autonomous guidance technique for altitude-controlled balloons in Venus's uncertain wind field, and show the approach can result in an increase of up to 63% in the number of close-up observations of volcanic events compared to passive drifters, and a 16% increase compared to ground-in-the-loop guidance. The results are robust to uncertainty in the wind field, and hold across large changes in the frequency of explosive volcanic events, sensitivity of the microbarometer detectors, and numbers of aerial platforms., 44 pages, 19 figures. Accepted for publication by Acta Astronautica

We present a final overview of the efforts by the Naval Air Warfare Center Weapons Division (NAWCWD) and the Jet Propulsion Laboratory to automate the operation of the largest fleet of autonomous maritime vehicles. The vehicles are intended for large-scale demonstrations and tests of US Navy systems or tactics. This review covers a preexisting distributed architecture for human-in-the-loop control of several autonomous high-speed boats with a main focus on the planning, formation assignment, and formation switching pipeline. Algorithm capabilities are described and validated in simulation and field tests using real-world vehicles. Theoretical lower bounds on the time required to change formations are also derived and used to bound our experimental performance. We are able to present data from the 2019 “final exam” that pitted this architecture in a head-to-head competition, which was won after a perfect run with no hazardous maneuvers recorded under autonomous control. The effort concluded successfully on December 31, 2020, with the delivery of code and documentation after successful integration and testing exercises in 2019 and 2020. We conclude the paper with discussions of limitations, extensions, and suggestions for future work.

We study the use of distributed computation in a representative multi-robot planetary exploration mission. We model a network of small rovers with access to computing resources from a static base station based on current design efforts and extrapolation from the Mars 2020 rover autonomy. The key algorithmic problem is simultaneous scheduling of computation, communication, and caching of data, as informed by an autonomous mission planner. We consider scheduling of a dependency chain of required and optional (but rewarding) tasks and present a consensus-backed scheduler for sharedworld, distributed scheduling based on an Integer Linear Program. We validate the pipeline with simulation and field results. Our results are intended to provide a baseline comparison and motivating application domain for future research into network-aware decentralized scheduling and resource allocation.

This paper explores the ability to ferry data between Mars and Earth, and Jupiter and Earth, using a network of small spacecraft (termed “data mules”) placed in phase-shifted cycler orbits between both planets. These cycler orbits enable the data mules to periodically visit the destination planet without requiring large amounts of fuel. However, their long periodicity also limits the cadence of visits since several years may elapse between consecutive data mule flybys. To increase total data return to Earth, we compare two alternative concepts of operations. First, we assume that each data mule carries an optical terminal capable of establishing a very high-rate inter-satellite link with a spacecraft orbiting Mars and Jupiter (where all data to be returned is stored). This inter-satellite link operates for a short period of time, nominally during 1 day around closest approach of the data mule to the planetary body of interest. This is compared to normal Deep Space Network (DSN) operations, in which we optimistically assume a continuous direct-to-Earth link with a ground antenna. Through our analysis, we show that the concept of data mules can substantially increase the total amount of data returnable from planetary bodies when used as a complement to current DSN support. We also show how the system performance, measured in terms of total returnable data volume or amortized yearly data rate, depends on technological constraints to implement the deep space and proximity links, as well as geometrical constraints imposed by cycler orbits and the solar system.

We present the Pluggable Distributed Resource Allocator (PDRA), a middleware for distributed computing in heterogeneous mobile robotic networks. PDRA enables autonomous robotic agents to share computational resources for computationally expensive tasks such as localization and path planning. It sits between an existing single-agent planner/executor and existing computational resources (e.g. ROS packages), intercepts the executor's requests and, if needed, transparently routes them to other robots for execution. PDRA is pluggable: it can be integrated in an existing single-robot autonomy stack with minimal modifications. Task allocation decisions are performed by a mixed-integer programming algorithm, solved in a shared-world fashion, that models CPU resources, latency requirements, and multi-hop, periodic, bandwidth-limited network communications; the algorithm can minimize overall energy usage or maximize the reward for completing optional tasks. Simulation results show that PDRA can reduce energy and CPU usage by over 50% in representative multi-robot scenarios compared to a naive scheduler; runs on embedded platforms; and performs well in delay- and disruption-tolerant networks (DTNs). PDRA is available to the community under an open-source license., Extended version of manuscript presented at IROS 2020. In v2, numerical results are updated and parts of the paper are rewritten and expanded for clarity. In v3, a minor author metadata error is fixed. All code is available under Apache license at https://github.com/nasa/mosaic

In order to achieve the ambitious objectives of the Mars 2020 (M2020) mission, in particular the ability to autonomously traverse more challenging terrains more efficiently, new surface mobility software was developed for Enhanced Navigation (ENav). That decision was made early in the project, before most of the new surface flight software (FSW) existed, which created a need for a separate framework where the new navigation algorithms could be quickly prototyped and tested, before more realistic FSW-based testbeds became available. The JPL robotics team chose the Robot Operating System [1] (ROS) as the environment in which to test the new ENav algorithms. This made it possible to write the algorithms in the C language required by the FSW, so they could be directly ported over to the flight module later on, while leveraging all the C++ libraries and tools provided by ROS for simulation and testing. The ENav algorithms were developed as a separate C library, and stubs were used to replace any FSW-specific code, such as Event Reporting (EVRs) and data products (DPs). A ROS simulator was developed to generate a rich set of varied 3D terrains representative of the candidate Mars landing sites and simulate the physics of the rover motion, the point cloud perceived by the rover's stereo vision system, and the new thinking-while-driving (TWD) navigation logic which directs the rover to drive autonomously to user-specified waypoints. To simulate the rover motion and perception, a ROS node was developed that uses a software library called HyperDrive Sim (HDSim), which is a wrapper for the Rover Sequencing and Visualization Program [2] (RSVP). That library provides rover-terrain settling, realistic slip modelling, and camera rendering capability based on the rover's NavCam machine vision models. To simulate the navigation logic, a ROS node was created that initializes and runs the ENav algorithms in a way that mimics the FSW execution, while also providing the capability to load and replay data products, including re-running the recorded inputs through the ENav algorithms for testing. An engineering Graphical User Interface (GUI) was also developed to visualize various elements, such as the rover pose during the drive, the simulated and perceived terrain, the selected local and global paths to the goal, the evaluated candidate paths and the reasons why they were rejected, the keep-in and keep-out zones (KIOZs), etc. Finally, an advanced Monte Carlo (MC) framework that can run many simulations in parallel on the Cloud and automatically generate reports that capture the key ENav performance metrics was developed to evaluate the system in a statistically-meaningful way. This paper provides an overview of the ROS-based simulator used for testing the M2020 ENav algorithms.

In this paper, we describe an ongoing multi-institution study in using emplaced computational resources such as high-volume storage and fast processing to enable instruments to gather and store much more data than would normally be possible, even if it cannot be downlinked to Earth in any reasonable time. The primary focus of the study is designing science pipelines for on-site summarization, archival for future downlink, and multisensor fusion. A secondary focus is on providing support for increasingly autonomous systems, including mapping, planning, and multi-platform collaboration. Key to both of these concepts is treating the spacecraft not as an autonomous agent but as an interactive batch processor, which allows us to avoid “quantum leaps” in machine intelligence required to realize the concepts. Our goal is to discuss preliminary results and technical directions for the community, and identify promising new opportunities for multi-sensor fusion with the help of planetary researchers.

Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot's abilities, hold great promise for a number of applications, such as planetary exploration missions. Each robot in a multi-robot system that uses the shared-world coordination paradigm autonomously schedules which robot should perform a given task, and when, using its worldview--the robot's internal representation of its belief about both its own state, and other robots' states. A key problem for operators is that robots' worldviews can fall out of sync (often due to weak communication links), leading to desynchronization of the robots' scheduling decisions and inconsistent emergent behavior (e.g., tasks not performed, or performed by multiple robots). Operators face the time-consuming and difficult task of making sense of the robots' scheduling decisions, detecting de-synchronizations, and pinpointing the cause by comparing every robot's worldview. To address these challenges, we introduce MOSAIC Viewer, a visual analytics system that helps operators (i) make sense of the robots' schedules and (ii) detect and conduct a root cause analysis of the robots' desynchronized worldviews. Over a year-long partnership with roboticists at the NASA Jet Propulsion Laboratory, we conduct a formative study to identify the necessary system design requirements and a qualitative evaluation with 12 roboticists. We find that MOSAIC Viewer is faster- and easier-to-use than the users' current approaches, and it allows them to stitch low-level details to formulate a high-level understanding of the robots' schedules and detect and pinpoint the cause of the desynchronized worldviews., Comment: To appear in IEEE Conference on Visual Analytics Science and Technology (VAST) 2020

We present algorithms for environment exploration in the context of a habitat monitoring task, where the goal is to track radio-tagged invasive fish with autonomous surface or ground robots. The first task is navigation around an unknown obstacle using an input from a front-facing sonar. This capability is important for navigation on inland lakes, because plants and shallow shorelines are hard to map in advance. The second task involves energy harvesting for long-term operation. We address the problem of exploring the solar map of the environment which is used for energy-efficient navigation. For both problems, we present online algorithms and examine their performance using competitive analysis. In competitive analysis, the performance of an online algorithm is compared against the optimal offline algorithm. For obstacle avoidance, the offline algorithm knows the shape of the obstacle. For solar exploration, the offline algorithm knows the geometry of the shadow-casting objects. We obtain an $O(1)$ competitive ratio for obstacle avoidance and an $O(\log n)$ competitive ratio for solar exploration, where $n$ is the number of critical points to observe. The strategies for obstacle avoidance are validated through extensive field experiments, and the strategies for exploration are validated with simulations.

Substantial improvements in material processing and manufacturing techniques in recent years necessitate the introduction of effective and efficient nondestructive testing (NDT) methods that can seamlessly integrate into day-to-day aircraft and aerospace operations. Lamb wave-based methods have been identified as one of the most promising candidates for the inspection of large-scale structures. At the same time, there is presently a high level of research in the field of autonomous mobile robotics, especially in simultaneous localization and mapping (SLAM). Thus, this paper investigates a means to automate Lamb wave-based NDT by positioning sensors along a planar structure through mobile service robots. To this end, a generalized method for the mapping of plate structures using scattered Lamb waves by means of frontier exploration is presented such that an autonomous SLAM-capable NDT system can become realizable. The performance of this novel Lamb wave-based frontier exploration is first evaluated in simulation. It is shown that it generally outperforms a random frontier exploration and may even perform near-optimal in the case of an isotropic, square panel. These findings are then validated in laboratory experiments, confirming the general feasibility of utilizing Lamb waves for SLAM. Furthermore, the versatility of the developed methodology is successfully demonstrated on a more complexly shaped stiffened panel.

This paper addresses a fundamental search problem in which a searcher subject to time and energy constraints tries to find a mobile target. The target's motion is modeled as a random walk on a discrete set of points on a line segment. At each time step, the target chooses one of the adjacent nodes at random and moves there. We study two detection models. In the no-crossing model, the searcher detects the target if it is on the same node or if it takes the same edge at the same time. In the crossing model, detection happens only if the target lands on the same node at the same time. For the no-crossing model, where move and stay actions may have different costs, we present an optimal search strategy under energy and time constraints. For the crossing model, we formulate the problem of designing an optimal strategy as a partially observable Markov decision process (POMDP) and solve it using methods that reduce the state–space representation of the belief. The POMDP solution reveals structural properties of the optimal solution. We use this structure to design an efficient strategy and analytically study its performance. Finally, we present preliminary experimental results to demonstrate the applicability of our model to our tracking system, which is used for finding radio-tagged invasive fish.

Energy harvesting using solar panels can significantly increase the operational life of mobile robots. If a map of expected solar power is available, energy efficient paths can be computed. However, estimating this map is a challenging task, especially in complex environments. In this paper, we show how the problem of estimating solar power can be decomposed into the steps of magnitude estimation and solar classification. Then, we provide two methods to classify a position as sunny or shaded: a simple data-driven Gaussian Process method and a method that estimates the geometry of the environment as a latent variable. Both of these methods are practical when the training measurements are sparse, such as with a simple robot that can only measure solar power at its own position. We demonstrate our methods on simulated randomly generated environments. We also justify our methods with measured solar data by comparing the constructed height maps with satellite images of the test environments, and in a cross-validation step where we examine the accuracy of predicted shadows and solar current.

We consider the problem of gathering bearing data to localize targets. We start with a commonly used notion of uncertainty based on geometric dilution of precision (GDOP) and study the following bicriteria problem. Given a set of potential target areas and an uncertainty level $U$ , compute an ordered set of measurement locations for a single robot which 1) minimizes the total cost given by the travel time plus the time spent in taking measurements and 2) ensures that the uncertainty in estimating the target’s location is at most $U$ regardless of the targets’ locations. We present an approximation algorithm and prove that its cost is at most 28.9 times the optimal cost while guaranteeing that the uncertainty is at most $5.5U$ . In addition to theoretical analysis, we validate the results in simulation and experiments performed with a directional antenna used for tracking invasive fish.

We study the problem of actively locating a static target using mobile robots equipped with bearing sensors. The goal is to reduce the uncertainty in the target's location to a value below a given threshold in minimum time. Our cost formulation explicitly models time spent in traveling, as well as taking measurements. In addition, we consider distance-based communication constraints between the robots. We provide the following theoretical results. First, we study the properties of an optimal offline strategy for one or more robots with access to the target's true location. We derive the optimal offline algorithm and bound its cost when considering a single robot or an even number of robots. In other cases, we provide a close approximation. Second, we provide a general method of converting the offline algorithm into an online adaptive algorithm (that does not have access to the target's true location), while preserving near optimality. Using these two results, we present an online strategy proven to locate the target up to a desired uncertainty level at near-optimal cost. In addition to theoretical analysis, we validate the algorithm in simulations and multiple field experiments performed using autonomous surface vehicles carrying radio antennas to localize radio tags.

Mission Modeling, Planning, and Execution Module (M2PEM) is a user friendly graphical framework for mission design and execution. It extends a subset of the Business Process Modeling and Notation (BPMN) 2.0 for robotic applications. Hierarchical abstractions fundamental to BPMN allow the mission to be naturally decomposed into interdependent parallel sequences of BPMN elements. M2PEM adapts these elements in a role based framework which uses collaborative control modalities as an atomic building block. Designed missions are able to consider situational data, external stimuli, and direct user interaction. Missions are directly executable using a resource manager and a ROS-based execution engine.

We study the problem of optimally choosing bearing measurement locations for localizing a stationary target in minimum time. The targets are transmitting radio tags, and bearing measurements are acquired from radio signal strength by a robot carrying a direction-sensitive radio antenna. Actively localizing radio tags has many applications in surveillance, search and rescue, and environmental monitoring. Our work is motivated by the task of monitoring radio-tagged invasive fish using autonomous vehicles. An active localization algorithm is provided in order to locate a target up to the desired uncertainty. The time required to locate the target includes time spent traveling as well as taking measurements. Since bearing measurements inferred from radio signals have an inherent ambiguity associated with them, the proposed algorithm chooses measurements to minimize the effect of ambiguous measurements on the target estimate. We present a closed-form bound on the time required to locate a target using the presented active localization strategy. We also present the first known lower bound on the time required by any active localization algorithm including the unknown optimal. Finally, we bound the ratio of the upper and lower bounds, showing that the expected cost of our algorithm is within a constant factor of the expected cost of the optimal solution. Robust initialization strategies that are motivated by practical sensing limitations are also provided. Our algorithm is shown to reliably locate radio tags to a desired uncertainty in simulations and multiple field experiments.

Carp is a highly invasive bottom-feeding fish that pollutes and dominates lakes by releasing harmful nutrients. Recently, biologists started studying the behavior of carp by tagging the fish with radio emitters. The biologists search for and localize the radio-tagged fish manually using a global positioning system (GPS) and a directional antenna. We are developing a novel robotic sensor system in which human effort is replaced by autonomous robots capable of finding and tracking tagged carp. In this article, we report the current state of our system. We present a new coverage algorithm for finding tagged fish and active localization algorithms for precisely localizing them. In addition to theoretical analysis and simulation results, we report results from field experiments.

A robotic boat is moving between two points when it encounters an obstacle of unknown size. The boat must find a short path around the obstacle to resume its original course. How should the boat move when it can only sense the proximity of the obstacle, and does not have prior information about the obstacle's size? We study this problem for a robotic boat with a forward-facing sonar. We study two versions of the problem. First, we solve a simplified case when the obstacle is a rectangle of known orientation but unknown dimensions. Second, we study a more general case where an arbitrarily shaped obstacle is contained between two known parallel lines. We study the performance of the algorithms analytically using competitive analysis and present results from field experiments. The experimental setup is relevant for harbor patrol or autonomous navigation in shallow water.

We study the problem of minimizing the time to accurately localize a target using radio-based telemetry. The directional nature of the antenna allows us to obtain bearing-to-target sensor measurements. There are two critical attributes that separate our setup from the majority of bearing-only tracking literature: sensing ambiguity and long measurement time. We provide a sensing strategy which mitigates the effect of ambiguity, and prove that the time required to localize a target is less than a constant times that of any bearing-based localization strategy which uses an Extended Kalman Filter.

We present a novel robotic telemetry system for localizing radio-tagged invasive fish in frozen lakes using coarse bearing measurements. We address the problem of selecting sensing locations so as to minimize the uncertainty in the location of the target. For this purpose, we propose three active localization algorithms and evaluate them both in simulations and through field experiments. We also present a novel technique for bearing-estimation from directional radio antenna which is critical for the successful execution of the active localization algorithms. Our system is able to operate on frozen lakes and localize the target to within values as low as one meter.