30,027 research outputs found
RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation
A key challenge in fast ground robot navigation in 3D terrain is balancing
robot speed and safety. Recent work has shown that 2.5D maps (2D
representations with additional 3D information) are ideal for real-time safe
and fast planning. However, the prevalent approach of generating 2D occupancy
grids through raytracing makes the generated map unsafe to plan in, due to
inaccurate representation of unknown space. Additionally, existing planners
such as MPPI do not consider speeds in known free and unknown space separately,
leading to slower overall plans. The RAMP pipeline proposed here solves these
issues using new mapping and planning methods. This work first presents ground
point inflation with persistent spatial memory as a way to generate accurate
occupancy grid maps from classified pointclouds. Then we present an MPPI-based
planner with embedded variability in horizon, to maximize speed in known free
space while retaining cautionary penetration into unknown space. Finally, we
integrate this mapping and planning pipeline with risk constraints arising from
3D terrain, and verify that it enables fast and safe navigation using
simulations and hardware demonstrations.Comment: 7 pages submitted to ICRA 202
UAV/UGV Autonomous Cooperation: UAV Assists UGV to Climb a Cliff by Attaching a Tether
This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle
(UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a
flying sensor but also as a tether attachment device. Two robots are connected
with a tether, allowing the UAV to anchor the tether to a structure located at
the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the
poor traversability of the UGV by not only providing a wider range of scanning
and mapping from the air, but also by allowing the UGV to climb steep terrains
with the winding of the tether. In addition, we present an autonomous framework
for the collaborative navigation and tether attachment in an unknown
environment. The UAV employs visual inertial navigation with 3D voxel mapping
and obstacle avoidance planning. The UGV makes use of the voxel map and
generates an elevation map to execute path planning based on a traversability
analysis. Furthermore, we compared the pros and cons of possible methods for
the tether anchoring from multiple points of view. To increase the probability
of successful anchoring, we evaluated the anchoring strategy with an
experiment. Finally, the feasibility and capability of our proposed system were
demonstrated by an autonomous mission experiment in the field with an obstacle
and a cliff.Comment: 7 pages, 8 figures, accepted to 2019 International Conference on
Robotics & Automation. Video: https://youtu.be/UzTT8Ckjz1
FLAT2D: Fast localization from approximate transformation into 2D
Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment
Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments
A key challenge in off-road navigation is that even visually similar terrains
or ones from the same semantic class may have substantially different traction
properties. Existing work typically assumes no wheel slip or uses the expected
traction for motion planning, where the predicted trajectories provide a poor
indication of the actual performance if the terrain traction has high
uncertainty. In contrast, this work proposes to analyze terrain traversability
with the empirical distribution of traction parameters in unicycle dynamics,
which can be learned by a neural network in a self-supervised fashion. The
probabilistic traction model leads to two risk-aware cost formulations that
account for the worst-case expected cost and traction. To help the learned
model generalize to unseen environment, terrains with features that lead to
unreliable predictions are detected via a density estimator fit to the trained
network's latent space and avoided via auxiliary penalties during planning.
Simulation results demonstrate that the proposed approach outperforms existing
work that assumes no slip or uses the expected traction in both navigation
success rate and completion time. Furthermore, avoiding terrains with low
density-based confidence score achieves up to 30% improvement in success rate
when the learned traction model is used in a novel environment.Comment: To appear in IROS23. Video and code:
https://github.com/mit-acl/mppi_numb
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