23 research outputs found
Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems
We present a light-weight body-terrain clearance evaluation algorithm for the
automated path planning of NASA's Mars 2020 rover. Extraterrestrial path
planning is challenging due to the combination of terrain roughness and severe
limitation in computational resources. Path planning on cluttered and/or uneven
terrains requires repeated safety checks on all the candidate paths at a small
interval. Predicting the future rover state requires simulating the vehicle
settling on the terrain, which involves an inverse-kinematics problem with
iterative nonlinear optimization under geometric constraints. However, such
expensive computation is intractable for slow spacecraft computers, such as
RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover.
We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains
conservative bounds on vehicle clearance, attitude, and suspension angles
without iterative computation. It obtains those bounds by estimating the lowest
and highest heights that each wheel may reach given the underlying terrain, and
calculating the worst-case vehicle configuration associated with those extreme
wheel heights. The bounds are guaranteed to be conservative, hence ensuring
vehicle safety during autonomous navigation. ACE is planned to be used as part
of the new onboard path planner of the Mars 2020 rover. This paper describes
the algorithm in detail and validates our claim of conservatism and fast
computation through experiments
惑星探査ローバの画像航法誘導における知能化に関する研究
学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 久保田 孝, 東京大学教授 橋本 樹明, 東京大学教授 古関 隆章, 東京大学准教授 大石 岳史, 東京大学教授 伊庭 斉志, 東京大学准教授 矢入 健久University of Tokyo(東京大学
Where to Map? Iterative Rover-Copter Path Planning for Mars Exploration
In addition to conventional ground rovers, the Mars 2020 mission will send a
helicopter to Mars. The copter's high-resolution data helps the rover to
identify small hazards such as steps and pointy rocks, as well as providing
rich textual information useful to predict perception performance. In this
paper, we consider a three-agent system composed of a Mars rover, copter, and
orbiter. The objective is to provide good localization to the rover by
selecting an optimal path that minimizes the localization uncertainty
accumulation during the rover's traverse. To achieve this goal, we quantify the
localizability as a goodness measure associated with the map, and conduct a
joint-space search over rover's path and copter's perceptual actions given
prior information from the orbiter. We jointly address where to map by the
copter and where to drive by the rover using the proposed iterative
copter-rover path planner. We conducted numerical simulations using the map of
Mars 2020 landing site to demonstrate the effectiveness of the proposed
planner.Comment: 8 pages, 7 figure
Toward Specification-Guided Active Mars Exploration for Cooperative Robot Teams
As a step towards achieving autonomy in space exploration missions, we consider a cooperative robotics system consisting of a copter and a rover. The goal of the copter is to explore an unknown environment so as to maximize knowledge about a science mission expressed in linear temporal logic that is to be executed by the rover. We model environmental uncertainty as a belief space Markov decision process and formulate the problem as a two-step stochastic dynamic program that we solve in a way that leverages the decomposed nature of the overall system. We demonstrate in simulations that the robot team makes intelligent decisions in the face of uncertainty
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
In order for an autonomous robot to efficiently explore an unknown
environment, it must account for uncertainty in sensor measurements, hazard
assessment, localization, and motion execution. Making decisions for maximal
reward in a stochastic setting requires value learning and policy construction
over a belief space, i.e., probability distribution over all possible
robot-world states. However, belief space planning in a large spatial
environment over long temporal horizons suffers from severe computational
challenges. Moreover, constructed policies must safely adapt to unexpected
changes in the belief at runtime. This work proposes a scalable value learning
framework, PLGRIM (Probabilistic Local and Global Reasoning on Information
roadMaps), that bridges the gap between (i) local, risk-aware resiliency and
(ii) global, reward-seeking mission objectives. Leveraging hierarchical belief
space planners with information-rich graph structures, PLGRIM addresses
large-scale exploration problems while providing locally near-optimal coverage
plans. We validate our proposed framework with high-fidelity dynamic
simulations in diverse environments and on physical robots in Martian-analog
lava tubes
Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain
Navigating off-road with a fast autonomous vehicle depends on a robust
perception system that differentiates traversable from non-traversable terrain.
Typically, this depends on a semantic understanding which is based on
supervised learning from images annotated by a human expert. This requires a
significant investment in human time, assumes correct expert classification,
and small details can lead to misclassification. To address these challenges,
we propose a method for predicting high- and low-risk terrains from only past
vehicle experience in a self-supervised fashion. First, we develop a tool that
projects the vehicle trajectory into the front camera image. Second, occlusions
in the 3D representation of the terrain are filtered out. Third, an autoencoder
trained on masked vehicle trajectory regions identifies low- and high-risk
terrains based on the reconstruction error. We evaluated our approach with two
models and different bottleneck sizes with two different training and testing
sites with a fourwheeled off-road vehicle. Comparison with two independent test
sets of semantic labels from similar terrain as training sites demonstrates the
ability to separate the ground as low-risk and the vegetation as high-risk with
81.1% and 85.1% accuracy