The creation of dynamic manipulation behaviors for high degree of freedom, mobile robots will allow them to
accomplish increasingly difficult tasks in the field. We are investigating how the coordinated use of the body, legs, and
integrated manipulator, on a mobile robot, can improve the strength, velocity, and workspace when handling heavy
objects. We envision that such a capability would aid in a search and rescue scenario when clearing obstacles from a
path or searching a rubble pile quickly. Manipulating heavy objects is especially challenging because the dynamic forces
are high and a legged system must coordinate all its degrees of freedom to accomplish tasks while maintaining balance.
To accomplish these types of manipulation tasks, we use trajectory optimization techniques to generate feasible open-loop
behaviors for our 28 dof quadruped robot (BigDog) by planning trajectories in a 13 dimensional space. We apply
the Covariance Matrix Adaptation (CMA) algorithm to solve for trajectories that optimize task performance while also
obeying important constraints such as torque and velocity limits, kinematic limits, and center of pressure location. These
open-loop behaviors are then used to generate desired feed-forward body forces and foot step locations, which enable
tracking on the robot. Some hardware results for cinderblock throwing are demonstrated on the BigDog quadruped
platform augmented with a human-arm-like manipulator. The results are analogous to how a human athlete maximizes
distance in the discus event by performing a precise sequence of choreographed steps.
The creation of high degree of freedom dynamic mobile manipulation techniques and behaviors will allow robots to
accomplish difficult tasks in the field. We are investigating the use of the body and legs of legged robots to improve the
strength, velocity, and workspace of an integrated manipulator to accomplish dynamic manipulation. This is an
especially challenging task, as all of the degrees of freedom are active at all times, the dynamic forces generated are
high, and the legged system must maintain robust balance throughout the duration of the tasks. To accomplish this goal,
we are utilizing trajectory optimization techniques to generate feasible open-loop behaviors for our 28 dof quadruped
robot (BigDog) by planning the trajectories in a 13 dimensional space. Covariance Matrix Adaptation techniques are
utilized to optimize for several criteria such as payload capability and task completion speed while also obeying
constraints such as torque and velocity limits, kinematic limits, and center of pressure location. These open-loop
behaviors are then used to generate feed-forward terms, which are subsequently used online to improve tracking and
maintain low controller gains. Some initial results on one of our existing balancing quadruped robots with an additional
human-arm-like manipulator are demonstrated on robot hardware, including dynamic lifting and throwing of heavy
objects 16.5kg cinder blocks, using motions that resemble a human athlete more than typical robotic motions. Increased
payload capacity is accomplished through coordinated body motion.
Jonathan Salton, Stephen Buerger, Lisa Marron, John Feddema, Gary Fischer, Charles Little, Barry Spletzer, Patrick Xavier, Alfred Rizzi, Michael Murphy, John Giarratana, Matthew Malchano, Christian Weagle
Hopping robots provide the possibility of breaking the link between the size of a ground vehicle and the largest obstacle that it can overcome. For more than a decade, DARPA and Sandia National Laboratories have been developing small-scale hopping robot technology, first as part of purely hopping platforms and, more recently, as part of platforms that are capable of both
wheeled and hopping locomotion. In this paper we introduce the Urban Hopper robot and summarize its capabilities. The advantages of hopping for overcoming certain obstacles are discussed. Several configurations of the Urban Hopper are described, as are intelligent
capabilities of the system. Key challenges are discussed.
We discuss the gait generation and control architecture of a bioinspired climbing robot that presently climbs a variety of vertical surfaces, including carpet, cork and a growing range of stucco-like surfaces in the quasi-static regime. The initial version of the robot utilizes a collection of gaits (cyclic feed-forward motion patterns) to locomote over these surfaces, with each gait tuned for a specific surface and set of operating conditions. The need for more flexibility in gait specification (e.g., adjusting number of feet on the ground), more intricate shaping of workspace motions (e.g., shaping the details of the foot attachment and detachment trajectories), and the need to encode gait "transitions" (e.g., tripod to pentapod gait structure) has led us to separate this trajectory generation scheme into the functional composition of a phase assigning transformation of the "clock space" (the six dimensional torus) followed by a map from phase into leg joints that decouples the geometric details of a particular gait. This decomposition also supports the introduction of sensory feedback to allow recovery from unexpected event and to adapt to changing surface geometries.
In this paper, we generate gaits for mixed systems, that is, dynamic systems that are subject to a set of nonholonomic
constraints. What is unique about mixed systems is that when we express their dynamics in body
coordinates, the motion of these systems can be attributed to two decoupled terms: the geometric and dynamic
phase shifts. In our prior work, we analyzed systems whose dynamic phase shift was null by definition. Purely
mechanical and principally kinematic systems are two classes of mechanical systems that have this property. We
generated gaits for these two classes of systems by intuitively evaluating their geometric phase shift and relating
it to a volume integral under well-defined height functions.
One of the contributions of this paper is to present a similar intuitive approach for computing the dynamic
phase shift. We achieve this, by introducing a new scaled momentum variable that not only simplifies the
momentum evolution equation but also allows us to introduce a new set of well-defined gamma functions which
enable us to intuitively evaluate the dynamic phase shift. More specifically, by analyzing these novel gamma
functions in a similar way to how we analyzed height functions, and by analyzing the sign-definiteness of the
scaled momentum variable, we are able to ensure that the dynamic phase shift is non-zero solely along the desired
fiber direction.
Finally, we also introduce a novel mechanical system, the variableinertia snakeboard, which is a generalization
of the original snakeboard that was previously studied in the literature. Not only does this general system help
us identify regions of the base space where we can not define a certain type of gaits, but also it helps us verify
the generality and applicability of our gait generation approach.
We review a large multidisciplinary effort to develop a family of autonomous robots capable of rapid, agile maneuvers in and around natural and artificial vertical terrains such as walls, cliffs, caves, trees and rubble. Our robot designs are inspired by (but not direct copies of) biological climbers such as cockroaches, geckos, and squirrels. We are incorporating advanced materials (e.g., synthetic gecko hairs) into these designs and fabricating them using state of the art rapid prototyping techniques (e.g., shape deposition manufacturing) that permit multiple iterations of design and testing with an effective integration path for the novel materials and components. We are developing novel motion control techniques to support dexterous climbing behaviors that are inspired by neuroethological studies of animals and descended from earlier frameworks that have proven analytically tractable and empirically sound. Our near term behavioral targets call for vertical climbing on soft (e.g., bark) or rough surfaces and for ascents on smooth, hard steep inclines (e.g., 60 degree slopes on metal or glass sheets) at one body length per second.
A coverage path planning algorithm produces a path that directs a robot (or its detector range, occupied volume, etc.) to sweep out a target volume. The goal of this work is to direct an agent to cover (fill) an unknown space using different coverage patterns. We achieve coverage by dividing the target region into sub-regions, termed cells, such that coverage in each cell can be accomplished by basic maneuvers, such as simple back-and-forth motions. This approach to coverage employs a representation of the free space called an exact cellular decomposition, which already has been widely used for path planning between two points. In this paper, we define exact cellular decompositions where critical points of Morse functions indicate the locations of cell boundaries. Morse functions are those whose critical points are non-degenerate. Different Morse functions induce different coverage patterns. Between critical points, the structure of a Morse function is effectively the same, so simple control laws to achieve tasks, such as coverage, are feasible within each cell. This paper addresses the issue of how to cover a space with varying patterns, but it also suggests a common framework for many conventional path planners. A companion paper describes the sensor based implementation of this approach.
This work prescribes the procedures that are required to implement, on a conventional mobile robot, a sensor based motion planning algorithm based on the generalized Voronoi graph (GVG). The GVG is a roadmap of a static environment; recall that a roadmap is a 1D representation of an environment which the robot can use to plan a path between any two points in that environment. Once the robot has constructed the roadmap, it has in essence explored the environment. This work describes some issues in incrementally constructing the GVG with a mobile robot wit a ring of sonar sensors. Specifically, we consider some issues in specularity and dead-reckoning error reduction.
Automated product assembly systems are traditionally designed with the intent that they will be operated with few significant changes for as long as the product is being manufactured. This approach to factory design and programming has may undesirable qualities which have motivated the development of more 'flexible' systems. In an effort to improve agility, different types of flexibility have been integrated into factory designs. Specifically, automated assembly systems have been endowed with the ability to assemble differing products by means of computer-controlled robots, and to accommodate variations in parts locations and dimensions by means of sensing. The product life cycle (PLC) is a standard four-stage model of the performance of a product from the time that it is first introduced in the marketplace until the time that it is discontinued. Manufacturers can improve their return on investment by adapting the production process to the PLC. We are developing two concepts to enable manufacturers to more readily achieve this goal: the agile assembly architecture (AAA), an abstract framework for distributed modular automation; and minifactory, our physical instantation of this architecture for the assembly of precision electro-mechanical devices. By examining the requirements which each PLC stage places upon the production system, we identify characteristics of factory design and programming which are appropriate for that stage. As the product transitions from one stage to the next, the factory design and programing should also transition from one embodiment to the next in order to achieve the best return on investment. Modularity of the factory components, highly flexible product transport mechanisms, and a high level of distributed intelligence are key characteristics of minifactory that enable this adaptation.
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