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Toupet, Olivier

Publications and source records attributed to Toupet, Olivier.

16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

First 210 solar days of Mars 2020 Perseverance Robotic Operations – Mobility, Robotic Arm, Sampling, and Helicopter

This paper includes the summary, lessonslearned, and upcoming plans for the first 210 Mars solar days(sols) of the mission. The focus of the paper is on roboticoperations which has the primary responsibility for strategicplanning, uplink commanding and downlink analysis forrover mobility and navigation, robotic arm operation, thesampling and caching capability including coring, theadaptive caching assembly and the 2nd sample handlingrobotic arm, and interface to the Mars helicopter Ingenuity.As of Sol 210 the rover has driven 2663.65 meters, executed20764 robotic arm and sampling commands, and hassuccessfully completed 13 helicopter flights covering 2382meters horizontal distance. It includes the OperationsReadiness Tests in preparation for landing, landing and initialcheckouts, strategic route planning to the science destinationand waypoints, surface checkout of all of the roboticscapability of the rover. It also discusses the strategic planningand tactical agility needed for interleaving scienceinvestigation and technology demonstration of the Marshelicopter flights where a minimum distance had to bemaintained between the rover and helicopter during flights. Itdiscusses the challenges with planning robotic operations andaddressing anomalies with the larger uncertainty presentduring early mission operations. It also discusses the impacton robotic operations from lessons incorporated fromprevious missions.

Ono, Hiro↗
NASA NTRS2022

Mid-Air Helicopter Delivery at Mars Using a Jetpack

Mid-Air Helicopter Delivery (MAHD) is a new Entry, Descent and Landing (EDL) architecture to enable in situ mobility for Mars science at lower cost than previous rover missions. It uses a jetpack to slow down a Mars Science Helicopter (MSH) after separation from the backshell, and reach aerodynamic conditions suitable for helicopter take-off in mid air. MAHD's lander-free approach leaves enough room in the aeroshell to accommodate larger rotors. This drastically improves flight performance compared to heritage EDL approaches, notably +60\% science payload mass. MAHD also brings cost savings, a simpler architecture, improved surface access and can reach higher elevations on Mars. This paper introduces a design for the MAHD system architecture and operations. We present a mechanical configuration which fits both MSH and the jetpack within the 2.65-m Mars heritage aeroshell, and a jetpack control architecture which fully leverages the available helicopter avionics. We discuss preliminary numerical models of the flow dynamics resulting from the interaction between the jets, the rotors and the side winds. We define a force-torque sensing architecture capable of handling the wind and trimming the rotors to prepare for safe take-off. Finally, we analyze the dynamic environment and closed-loop control simulation results to demonstrate the preliminary feasibility of MAHD.

Balaram, J.↗
NASA NTRS2021

Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions

We propose a framework for resilient autonomous navigation in perceptuallychallenging unknown environments with mobility-stressing elements such asuneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffsand holes, and narrow passages. Environments are GPS-denied and perceptuallydegradedwith variable lighting from dark to lit and obscurants (dust, fog, smoke).Lack of prior maps and degraded communication eliminates the possibility of prioror off-board computation or operator intervention. This necessitates real-time onboardcomputation using noisy sensor data. To address these challenges, we proposea resilient architecture that exploits redundancy and heterogeneity in sensing modalities.Further resilience is achieved by triggering recovery behaviors upon failure.We propose a fast settling algorithm to generate robust multi-fidelity traversabilityestimates in real-time. The proposed approach was deployed on multiple physicalsystems including skid-steer and tracked robots, high-speed RC car and legged robotsand as a part of Team CoSTAR’s effort to theDARPASubterranean Challenge, wherethe team won 2nd and 1st place in the Tunnel and Urban Circuit, respectively.

Agha-mohammadi, Ali-akbar↗
NASA NTRS2021

Machine Learning Based Path Planning for Improved Rover Navigation

Enhanced AutoNav (ENav), the baseline surface navigation software for NASA’s Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.

Yue, Yisong↗
NASA NTRS2020

Driving Curiosity: Mars Rover Mobility Trends During the First Seven Years

NASA’s Mars Science Laboratory (MSL) mission landed the Curiosity rover on Mars on August 6, 2012. As of August 6, 2019 (sol 2488), Curiosity has driven 21,318.5 meters over a variety of terrain types and slopes, employing multiple drive modes with varying amounts of onboard autonomy. Curiosity’s drive distances each sol have ranged from its shortest drive of 2.6 centimeters to its longest drive of 142.5 meters, with an average drive distance of 28.9 meters. Real-time human intervention during Curiosity drives on Mars is not possible due to the latency in uplinking commands and downlinking telemetry, so the operations team relies on the rover’s flight software to prevent an unsafe state during driving. Over the first seven years of the mission, Curiosity has attempted 738 drives. While 622 drives have completed successfully, 116 drives were prevented or stopped early by the rover’s fault protection software. The primary risks to mobility success have been wheel wear, wheel entrapment, progressive wheel sinkage (which can lead to rover embedding), and terrain interactions or hardware or cabling failures that result in an inability to command one or more steer or drive actuators. In this paper, we describe mobility trends over the first 21.3km of the mission, operational aspects of the mobility fault protection, and risk mitigation strategies that will support continued mobility success for the remainder of the mission.

Rankin, Arturo↗
NASA NTRS2020

A ROS-based Simulator for Testing the Enhanced Autonomous Navigation of the Mars 2020 Rover

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 roverterrain 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 statisticallymeaningful way. This paper provides an overview of the ROSbased simulator used for testing the M2020 ENav algorithms.

Toupet, Olivier↗
NASA NTRS2018

Traction Control Design and Integration Onboard the Mars Science Laboratory Curiosity Rover

The Mars Science Laboratory (MSL) Curiosity rover experienced increasing wheel damage beginning in October 2013. While the wheels were designed to operate with considerable damage, the rate at which damage was occurring was unexpected and raised concerns regarding wheel lifetime. The Jet Propulsion Laboratory (JPL) has now developed and deployed new software on Curiosity that reduces the forces acting on the wheels. Our new Traction Control algorithm adapts each wheel’s speed to fit the terrain it drives over. It does not rely on any a priori knowledge of the terrain, and instead leverages the rover’s measured attitude rates and suspension angles, together with a rigid-body kinematics model, to estimate the real-time wheel-terrain contact angles and ideal, no-slip wheel angular rates. In addition, free-floating “wheelies” are detected and autonomously corrected. In this paper, we describe the algorithm, its ground testing campaign and associated challenges, and finally its validation and performance in flight. Ground test data demonstrates reductions in the forces acting on the wheels and validates the wheelie-damping capability. Secondary benefits in some terrains include a reduction in heading deviations while climbing rocks, with a reduction in slip in certain sandy terrains. Preliminary validation from flight data confirms these findings.

Maimone, Mark↗
NASA NTRS2015

Road-Following Formation Control of Autonomous Ground Vehicles

This work presents a novel cooperative path planning for formation keeping robots traversing along a road with obstacles and possible narrow passages. A unique challenge in this problem is a requirement for spatial and temporal coordination between vehicles while ensuring collision and obstacle avoidance.

Ono, Masahiro↗