16,187 research outputs found
Path Planning for Simple Robots using Soft Subdivision Search
The concept of resolution-exact path planning is a theoretically sound alternative to the standard exact algorithms, and provides much stronger guarantees than probabilistic or sampling algorithms. It opens the way for the introduction of soft predicates in the context of subdivision algorithm. Taking a leaf from the great success of the Probabilistic Road Map (PRM) framework, we formulate an analogous framework for subdivision, called Soft Subdivision Search (SSS). In this video, we illustrate the SSS framework for a trio of simple planar robots: disc, triangle and 2-links. These robots have, respectively, 2, 3 and 4 degrees of freedom. Our 2-link robot can also avoid self-crossing. These algorithms operate in realtime and are relatively easy to implement
A modal approach to hyper-redundant manipulator kinematics
This paper presents novel and efficient kinematic modeling techniques for “hyper-redundant” robots. This approach is based on a “backbone curve” that captures the robot's macroscopic geometric features. The inverse kinematic, or “hyper-redundancy resolution,” problem reduces to determining the time varying backbone curve behavior. To efficiently solve the inverse kinematics problem, the authors introduce a “modal” approach, in which a set of intrinsic backbone curve shape functions are restricted to a modal form. The singularities of the modal approach, modal non-degeneracy conditions, and modal switching are considered. For discretely segmented morphologies, the authors introduce “fitting” algorithms that determine the actuator displacements that cause the discrete manipulator to adhere to the backbone curve. These techniques are demonstrated with planar and spatial mechanism examples. They have also been implemented on a 30 degree-of-freedom robot prototype
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
This paper describes an architecture for robots that combines the
complementary strengths of probabilistic graphical models and declarative
programming to represent and reason with logic-based and probabilistic
descriptions of uncertainty and domain knowledge. An action language is
extended to support non-boolean fluents and non-deterministic causal laws. This
action language is used to describe tightly-coupled transition diagrams at two
levels of granularity, with a fine-resolution transition diagram defined as a
refinement of a coarse-resolution transition diagram of the domain. The
coarse-resolution system description, and a history that includes (prioritized)
defaults, are translated into an Answer Set Prolog (ASP) program. For any given
goal, inference in the ASP program provides a plan of abstract actions. To
implement each such abstract action, the robot automatically zooms to the part
of the fine-resolution transition diagram relevant to this action. A
probabilistic representation of the uncertainty in sensing and actuation is
then included in this zoomed fine-resolution system description, and used to
construct a partially observable Markov decision process (POMDP). The policy
obtained by solving the POMDP is invoked repeatedly to implement the abstract
action as a sequence of concrete actions, with the corresponding observations
being recorded in the coarse-resolution history and used for subsequent
reasoning. The architecture is evaluated in simulation and on a mobile robot
moving objects in an indoor domain, to show that it supports reasoning with
violation of defaults, noisy observations and unreliable actions, in complex
domains.Comment: 72 pages, 14 figure
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