43 research outputs found
Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments
This research aims at developing path and motion planning algorithms for a
tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated
primary robot in unstructured or confined environments. The emerging state of
the practice for nuclear operations, bomb squad, disaster robots, and other
domains with novel tasks or highly occluded environments is to use two robots,
a primary and a secondary that acts as a visual assistant to overcome the
perceptual limitations of the sensors by providing an external viewpoint.
However, the benefits of using an assistant have been limited for at least
three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground
robot assistants are considered, ignoring the rapid evolution of small unmanned
aerial systems for indoor flying, (3) introducing a whole crew for the second
teleoperated robot is not cost effective, may introduce further teamwork
demands, and therefore could lead to miscommunication. This dissertation
proposes to use an autonomous tethered aerial visual assistant to replace the
secondary robot and its operating crew. Along with a pre-established theory of
viewpoint quality based on affordances, this dissertation aims at defining and
representing robot motion risk in unstructured or confined environments. Based
on those theories, a novel high level path planning algorithm is developed to
enable risk-aware planning, which balances the tradeoff between viewpoint
quality and motion risk in order to provide safe and trustworthy visual
assistance flight. The planned flight trajectory is then realized on a tethered
UAV platform. The perception and actuation are tailored to fit the tethered
agent in the form of a low level motion suite, including a novel tether-based
localization model with negligible computational overhead, motion primitives
for the tethered airframe based on position and velocity control, and two
differentComment: Ph.D Dissertatio
Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments
This research aims at developing path and motion planning algorithms for a tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated primary robot in unstructured or confined environments. The emerging state of the practice for nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the sensors by providing an external viewpoint. However, the benefits of using an assistant have been limited for at least three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground robot assistants are considered, ignoring the rapid evolution of small unmanned aerial systems for indoor flying, (3) introducing a whole crew for the second teleoperated robot is not cost effective, may introduce further teamwork demands, and therefore could lead to miscommunication. This dissertation proposes to use an autonomous tethered aerial visual assistant to replace the secondary robot and its operating crew. Along with a pre-established theory of viewpoint quality based on affordances, this dissertation aims at defining and representing robot motion risk in unstructured or confined environments. Based on those theories, a novel high level path planning algorithm is developed to enable risk-aware planning, which balances the tradeoff between viewpoint quality and motion risk in order to provide safe and trustworthy visual assistance flight.
The planned flight trajectory is then realized on a tethered UAV platform. The perception and actuation are tailored to fit the tethered agent in the form of a low level motion suite, including a novel tether-based localization model with negligible computational overhead, motion primitives for the tethered airframe based on position and velocity control, and two different approaches to negotiate tether with complex obstacle-occupied environments. The proposed research provides a formal reasoning of motion risk in unstructured or confined spaces, contributes to the field of risk-aware planning with a versatile planner, and opens up a new regime of indoor UAV navigation: tethered indoor flight to ensure battery duration and failsafe in case of vehicle malfunction. It is expected to increase teleoperation productivity and reduce costly errors in scenarios such as safe decommissioning and nuclear operations in the Fukushima Daiichi facility
Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain
Most autonomous navigation systems assume wheeled robots are rigid bodies and
their 2D planar workspaces can be divided into free spaces and obstacles.
However, recent wheeled mobility research, showing that wheeled platforms have
the potential of moving over vertically challenging terrain (e.g., rocky
outcroppings, rugged boulders, and fallen tree trunks), invalidate both
assumptions. Navigating off-road vehicle chassis with long suspension travel
and low tire pressure in places where the boundary between obstacles and free
spaces is blurry requires precise 3D modeling of the interaction between the
chassis and the terrain, which is complicated by suspension and tire
deformation, varying tire-terrain friction, vehicle weight distribution and
momentum, etc. In this paper, we present a learning approach to model wheeled
mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan
feasible, stable, and efficient motion to drive over vertically challenging
terrain without rolling over or getting stuck. We present physical experiments
on two wheeled robots and show that planning using our learned model can
achieve up to 60% improvement in navigation success rate and 46% reduction in
unstable chassis roll and pitch angles.Comment: https://www.youtube.com/watch?v=VzpRoEZeyWk
https://cs.gmu.edu/~xiao/Research/Verti-Wheelers