842 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
Axel: A Minimalist Tethered Rover for Exploration of Extreme Planetary Terrains
Recent scientific findings suggest that some of the most interesting sites for future exploration of planetary surfaces lie in terrains that are currently inaccessible to conventional robotic rovers. To provide robust and flexible access to these terrains, we have been developing Axel, the robotic rover. Axel is a lightweight two-wheeled vehicle that can access steep terrains and negotiate relatively large obstacles because of its actively managed tether and novel wheel design. This article reviews the Axel system and focuses on those system components that affect Axel's steep terrain mobility. Experimental demonstrations of Axel on sloped and rocky terrains are presented
Stability of Surface Contacts for Humanoid Robots: Closed-Form Formulae of the Contact Wrench Cone for Rectangular Support Areas
Humanoid robots locomote by making and breaking contacts with their
environment. A crucial problem is therefore to find precise criteria for a
given contact to remain stable or to break. For rigid surface contacts, the
most general criterion is the Contact Wrench Condition (CWC). To check whether
a motion satisfies the CWC, existing approaches take into account a large
number of individual contact forces (for instance, one at each vertex of the
support polygon), which is computationally costly and prevents the use of
efficient inverse-dynamics methods. Here we argue that the CWC can be
explicitly computed without reference to individual contact forces, and give
closed-form formulae in the case of rectangular surfaces -- which is of
practical importance. It turns out that these formulae simply and naturally
express three conditions: (i) Coulomb friction on the resultant force, (ii) ZMP
inside the support area, and (iii) bounds on the yaw torque. Conditions (i) and
(ii) are already known, but condition (iii) is, to the best of our knowledge,
novel. It is also of particular interest for biped locomotion, where undesired
foot yaw rotations are a known issue. We also show that our formulae yield
simpler and faster computations than existing approaches for humanoid motions
in single support, and demonstrate their consistency in the OpenHRP simulator.Comment: 14 pages, 4 figure
A Discrete Geometric Optimal Control Framework for Systems with Symmetries
This paper studies the optimal motion control of
mechanical systems through a discrete geometric approach. At
the core of our formulation is a discrete Lagrange-d’Alembert-
Pontryagin variational principle, from which are derived discrete
equations of motion that serve as constraints in our optimization
framework. We apply this discrete mechanical approach to
holonomic systems with symmetries and, as a result, geometric
structure and motion invariants are preserved. We illustrate our
method by computing optimal trajectories for a simple model of
an air vehicle flying through a digital terrain elevation map, and
point out some of the numerical benefits that ensue
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