3,503 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
How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
Estimating terrain traversability in off-road environments requires reasoning
about complex interaction dynamics between the robot and these terrains.
However, it is challenging to build an accurate physics model, or create
informative labels to learn a model in a supervised manner, for these
interactions. We propose a method that learns to predict traversability
costmaps by combining exteroceptive environmental information with
proprioceptive terrain interaction feedback in a self-supervised manner.
Additionally, we propose a novel way of incorporating robot velocity in the
costmap prediction pipeline. We validate our method in multiple short and
large-scale navigation tasks on a large, autonomous all-terrain vehicle (ATV)
on challenging off-road terrains, and demonstrate ease of integration on a
separate large ground robot. Our short-scale navigation results show that using
our learned costmaps leads to overall smoother navigation, and provides the
robot with a more fine-grained understanding of the interactions between the
robot and different terrain types, such as grass and gravel. Our large-scale
navigation trials show that we can reduce the number of interventions by up to
57% compared to an occupancy-based navigation baseline in challenging off-road
courses ranging from 400 m to 3150 m
Terrain Aware Traverse Planning for Mars Rovers
NASA is proposing a Mars Sample Return mission, to be completed within one Martian year, that will require enhanced autonomy to perform its duties faster, safer, and more efficiently. With its main purpose being to retrieve samples possibly tens of kilometers away, it will need to drive beyond line-of-sight to get to its target more quickly than any rovers before. This research proposes a new methodology to support a sample return mission and is divided into three compo-nents: map preparation (map of traversability, i.e., ability of a terrain to sustain the traversal of a vehicle), path planning (pre-planning and replanning), and terrain analysis. The first component aims at creating a better knowledge of terrain traversability to support planning, by predicting rover slip and drive speed along the traverse using orbital data. By overlapping slope, rock abundance and terrain types at the same location, the expected drive velocity is obtained. By combining slope and thermal data, additional information about the experienced slip is derived, indicating whether it will be low (less than 30%) or medium to high (more than 30%). The second component involves planning the traverse for one Martian day (or sol) at a time, based on the map of expected drive speed. This research proposes to plan, offline, several paths traversable in one sol. Once online, the rover chooses the fastest option (the path cost being calculated using the distance divided by the expected velocity). During its drive, the rover monitors the terrain via analysis of its experienced wheel slip and actual speed. This information is then passed along the different pre-planned paths over a given distance (e.g., 25 m) and the map of traversability is locally updated given this new knowledge. When an update occurs, the rover calculates the new time of arrival of the various paths and replans its route if necessary. When tested in a simulation study on maps of the Columbia Hills, Mars, the rover successfully updates the map given new information drawn from a modified map used as ground truth for simulation purposes and replans its traverse when needed. The third component describes a method to assess the soil in-situ in case of dangerous terrain detected during the map update, or if the monitoring is not enough to confirm the traversability predicted by the map. The rover would deploy a shear vane instrument to compute intrinsic terrain parameters, information then propagated ahead of the rover to update the map and replan if necessary. Experiments in a laboratory setting as well as in the field showed promising results, the mounted shear vane giving values close to the expected terrain parameters of the tested soils
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