3,775 research outputs found
Planning Graph Heuristics for Belief Space Search
Some recent works in conditional planning have proposed reachability
heuristics to improve planner scalability, but many lack a formal description
of the properties of their distance estimates. To place previous work in
context and extend work on heuristics for conditional planning, we provide a
formal basis for distance estimates between belief states. We give a definition
for the distance between belief states that relies on aggregating underlying
state distance measures. We give several techniques to aggregate state
distances and their associated properties. Many existing heuristics exhibit a
subset of the properties, but in order to provide a standardized comparison we
present several generalizations of planning graph heuristics that are used in a
single planner. We compliment our belief state distance estimate framework by
also investigating efficient planning graph data structures that incorporate
BDDs to compute the most effective heuristics.
We developed two planners to serve as test-beds for our investigation. The
first, CAltAlt, is a conformant regression planner that uses A* search. The
second, POND, is a conditional progression planner that uses AO* search. We
show the relative effectiveness of our heuristic techniques within these
planners. We also compare the performance of these planners with several state
of the art approaches in conditional planning
Improving Automated Driving through Planning with Human Internal States
This work examines the hypothesis that partially observable Markov decision
process (POMDP) planning with human driver internal states can significantly
improve both safety and efficiency in autonomous freeway driving. We evaluate
this hypothesis in a simulated scenario where an autonomous car must safely
perform three lane changes in rapid succession. Approximate POMDP solutions are
obtained through the partially observable Monte Carlo planning with observation
widening (POMCPOW) algorithm. This approach outperforms over-confident and
conservative MDP baselines and matches or outperforms QMDP. Relative to the MDP
baselines, POMCPOW typically cuts the rate of unsafe situations in half or
increases the success rate by 50%.Comment: Preprint before submission to IEEE Transactions on Intelligent
Transportation Systems. arXiv admin note: text overlap with arXiv:1702.0085
Improving the Flexibility and Robustness of Machine Tending Mobile Robots
While traditional manufacturing production cells consist of a fixed base robot repetitively performing tasks, the Industry 5.0 flexible manufacturing cell (FMC) aims to bring Autonomous Industrial Mobile Manipulators (AIMMs) to the factory floor. Composed of a wheeled base and a robot arm, these collaborative robots (cobots) operate alongside people while autonomously performing tasks at different workstations. AIMMs have been tested in real production systems, but the development of the control algorithms necessary for automating a robot that is a combination of two cobots remains an open challenge before the large scale adoption of this technology occurs in industry. Currently popular docking based methods require a mount point for the docking station and considerable time for the robot to locate and park. These limitations necessitate the consideration and implementation of more modern robot control and path planning techniques. This work proposes and implements a simulation testbed that uses a contemporary whole-body control, OCS2, to perform more flexible pick-and-place tasks. Within this testbed, an Industry 5.0 based pick-and-place framework is deployed, fine-tuned and tested. This system supports the one-shot lead-through based assignment of a prepick position by an operator, thus enabling the cobot to drive to this position and successfully pick up the part agnostic of base orientation and/or position. The proposed system allows robot path planning experimentation and assessment against a variety of cost and constraint values, and is capable of being modified to support various vision based part locating algorithms
Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation
Long-term operations of resource-constrained robots typically require hard decisions be made about which data to process and/or retain. The question then arises of how to choose which data is most useful to keep to achieve the task at hand. As spacial scale grows, the size of the map will grow without bound, and as temporal scale grows, the number of measurements will grow without bound. In this work, we present the first known approach to tackle both of these issues. The approach has two stages. First, a subset of the variables (focused variables) is selected that are most useful for a particular task. Second, a task-agnostic and principled method (focused inference) is proposed to select a subset of the measurements that maximizes the information over the focused variables. The approach is then applied to the specific task of robot navigation in an obstacle-laden environment. A landmark selection method is proposed to minimize the probability of collision and then select the set of measurements that best localizes those landmarks. It is shown that the two-stage approach outperforms both only selecting measurement and only selecting landmarks in terms of minimizing the probability of collision. The performance improvement is validated through detailed simulation and real experiments on a Pioneer robot.United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-11-1-0391)United States. Office of Naval Research (Grant N00014-11-1-0688)National Science Foundation (U.S.) (Award IIS-1318392
Theseus: A Library for Differentiable Nonlinear Optimization
We present Theseus, an efficient application-agnostic open source library for
differentiable nonlinear least squares (DNLS) optimization built on PyTorch,
providing a common framework for end-to-end structured learning in robotics and
vision. Existing DNLS implementations are application specific and do not
always incorporate many ingredients important for efficiency. Theseus is
application-agnostic, as we illustrate with several example applications that
are built using the same underlying differentiable components, such as
second-order optimizers, standard costs functions, and Lie groups. For
efficiency, Theseus incorporates support for sparse solvers, automatic
vectorization, batching, GPU acceleration, and gradient computation with
implicit differentiation and direct loss minimization. We do extensive
performance evaluation in a set of applications, demonstrating significant
efficiency gains and better scalability when these features are incorporated.
Project page: https://sites.google.com/view/theseus-a
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics
Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854
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