104,883 research outputs found
FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning
Fast and safe navigation of dynamical systems through a priori unknown
cluttered environments is vital to many applications of autonomous systems.
However, trajectory planning for autonomous systems is computationally
intensive, often requiring simplified dynamics that sacrifice safety and
dynamic feasibility in order to plan efficiently. Conversely, safe trajectories
can be computed using more sophisticated dynamic models, but this is typically
too slow to be used for real-time planning. We propose a new algorithm
FaSTrack: Fast and Safe Tracking for High Dimensional systems. A path or
trajectory planner using simplified dynamics to plan quickly can be
incorporated into the FaSTrack framework, which provides a safety controller
for the vehicle along with a guaranteed tracking error bound. This bound
captures all possible deviations due to high dimensional dynamics and external
disturbances. Note that FaSTrack is modular and can be used with most current
path or trajectory planners. We demonstrate this framework using a 10D
nonlinear quadrotor model tracking a 3D path obtained from an RRT planner.Comment: Submitted to IEEE Conference on Decision and Control, 201
A constrained control-planning strategy for redundant manipulators
This paper presents an interconnected control-planning strategy for redundant
manipulators, subject to system and environmental constraints. The method
incorporates low-level control characteristics and high-level planning
components into a robust strategy for manipulators acting in complex
environments, subject to joint limits. This strategy is formulated using an
adaptive control rule, the estimated dynamic model of the robotic system and
the nullspace of the linearized constraints. A path is generated that takes
into account the capabilities of the platform. The proposed method is
computationally efficient, enabling its implementation on a real multi-body
robotic system. Through experimental results with a 7 DOF manipulator, we
demonstrate the performance of the method in real-world scenarios
Unifying Map and Landmark Based Representations for Visual Navigation
This works presents a formulation for visual navigation that unifies map
based spatial reasoning and path planning, with landmark based robust plan
execution in noisy environments. Our proposed formulation is learned from data
and is thus able to leverage statistical regularities of the world. This allows
it to efficiently navigate in novel environments given only a sparse set of
registered images as input for building representations for space. Our
formulation is based on three key ideas: a learned path planner that outputs
path plans to reach the goal, a feature synthesis engine that predicts features
for locations along the planned path, and a learned goal-driven closed loop
controller that can follow plans given these synthesized features. We test our
approach for goal-driven navigation in simulated real world environments and
report performance gains over competitive baseline approaches.Comment: Project page with videos: https://s-gupta.github.io/cmpl
A Framework for Planning and Controlling Non-Periodic Bipedal Locomotion
This study presents a theoretical framework for planning and controlling
agile bipedal locomotion based on robustly tracking a set of non-periodic apex
states. Based on the prismatic inverted pendulum model, we formulate a hybrid
phase-space planning and control framework which includes the following key
components: (1) a step transition solver that enables dynamically tracking
non-periodic apex or keyframe states over various types of terrains, (2) a
robust hybrid automaton to effectively formulate planning and control
algorithms, (3) a phase-space metric to measure distance to the planned
locomotion manifolds, and (4) a hybrid control method based on the previous
distance metric to produce robust dynamic locomotion under external
disturbances. Compared to other locomotion frameworks, we have a larger focus
on non-periodic gait generation and robustness metrics to deal with
disturbances. Such focus enables the proposed control framework to robustly
track non-periodic apex states over various challenging terrains and under
external disturbances as illustrated through several simulations. Additionally,
it allows a bipedal robot to perform non-periodic bouncing maneuvers over
disjointed terrains.Comment: 33 pages, 18 figures, journa
Provably Safe and Robust Drone Routing via Sequential Path Planning: A Case Study in San Francisco and the Bay Area
Provably safe and scalable multi-vehicle path planning is an important and
urgent problem due to the expected increase of automation in civilian airspace
in the near future. Hamilton-Jacobi (HJ) reachability is an ideal tool for
analyzing such safety-critical systems and has been successfully applied to
several small-scale problems. However, a direct application of HJ reachability
to large scale systems is often intractable because of its
exponentially-scaling computation complexity with respect to system dimension,
also known as the "curse of dimensionality". To overcome this problem, the
sequential path planning (SPP) method, which assigns strict priorities to
vehicles, was previously proposed; SPP allows multi-vehicle path planning to be
done with a linearly-scaling computation complexity. In this work, we
demonstrate the potential of SPP algorithm for large-scale systems. In
particular, we simulate large-scale multi-vehicle systems in two different
urban environments, a city environment and a multi-city environment, and use
the SPP algorithm for trajectory planning. SPP is able to efficiently design
collision-free trajectories in both environments despite the presence of
disturbances in vehicles' dynamics. To ensure a safe transition of vehicles to
their destinations, our method automatically allocates space-time reservations
to vehicles while accounting for the magnitude of disturbances such as wind in
a provably safe way. Our simulation results show an intuitive multi-lane
structure in airspace, where the number of lanes and the distance between the
lanes depend on the size of disturbances and other problem parameters.Comment: Submitted to AIAA Journal of Guidance, Control, and Dynamics. arXiv
admin note: substantial text overlap with arXiv:1611.0836
Non-Gaussian SLAP: Simultaneous Localization and Planning Under Non-Gaussian Uncertainty in Static and Dynamic Environments
Simultaneous Localization and Planning (SLAP) under process and measurement
uncertainties is a challenge. It involves solving a stochastic control problem
modeled as a Partially Observed Markov Decision Process (POMDP) in a general
framework. For a convex environment, we propose an optimization-based open-loop
optimal control problem coupled with receding horizon control strategy to plan
for high quality trajectories along which the uncertainty of the state
localization is reduced while the system reaches to a goal state with minimum
control effort. In a static environment with non-convex state constraints, the
optimization is modified by defining barrier functions to obtain collision-free
paths while maintaining the previous goals. By initializing the optimization
with trajectories in different homotopy classes and comparing the resultant
costs, we improve the quality of the solution in the presence of action and
measurement uncertainties. In dynamic environments with time-varying
constraints such as moving obstacles or banned areas, the approach is extended
to find collision-free trajectories. In this paper, the underlying spaces are
continuous, and beliefs are non-Gaussian. Without obstacles, the optimization
is a globally convex problem, while in the presence of obstacles it becomes
locally convex. We demonstrate the performance of the method on different
scenarios.Comment: 10 page
Perception-Aware Motion Planning via Multiobjective Search on GPUs
In this paper we describe a framework towards computing well-localized,
robust motion plans through the perception-aware motion planning problem,
whereby we seek a low-cost motion plan subject to a separate constraint on
perception localization quality. To solve this problem we introduce the
Multiobjective Perception-Aware Planning (MPAP) algorithm which explores the
state space via a multiobjective search, considering both cost and a perception
heuristic. This framework can accommodate a large range of heuristics, allowing
those that capture the history dependence of localization drift and represent
complex modern perception methods. We present two such heuristics, one derived
from a simplified model of robot perception and a second learned from
ground-truth sensor error, which we show to be capable of predicting the
performance of a state-of-the-art perception system. The solution trajectory
from this heuristic-based search is then certified via Monte Carlo methods to
be well-localized and robust. The additional computational burden of
perception-aware planning is offset by GPU massive parallelization. Through
numerical experiments the algorithm is shown to find well-localized, robust
solutions in about a second. Finally, we demonstrate MPAP on a quadrotor flying
perception-aware and perception-agnostic plans using Google Tango for
localization, finding the quadrotor safely executes the perception-aware plan
every time, while crashing in over 20% of the perception-agnostic runs due to
loss of localization
Proceedings of the 1st Workshop on Robotics Challenges and Vision (RCV2013)
Proceedings of the 1st Workshop on Robotics Challenges and Vision (RCV2013)Comment: http://compbio.cs.wayne.edu/robotics/rcv2013/proceedings-emb.pd
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
Self-driving vehicles are a maturing technology with the potential to reshape
mobility by enhancing the safety, accessibility, efficiency, and convenience of
automotive transportation. Safety-critical tasks that must be executed by a
self-driving vehicle include planning of motions through a dynamic environment
shared with other vehicles and pedestrians, and their robust executions via
feedback control. The objective of this paper is to survey the current state of
the art on planning and control algorithms with particular regard to the urban
setting. A selection of proposed techniques is reviewed along with a discussion
of their effectiveness. The surveyed approaches differ in the vehicle mobility
model used, in assumptions on the structure of the environment, and in
computational requirements. The side-by-side comparison presented in this
survey helps to gain insight into the strengths and limitations of the reviewed
approaches and assists with system level design choices
Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions
This paper proposes a sample-efficient yet simple approach to learning
closed-loop policies for nonprehensile manipulation. Although reinforcement
learning (RL) can learn closed-loop policies without requiring access to
underlying physics models, it suffers from poor sample complexity on
challenging tasks. To overcome this problem, we leverage rearrangement planning
to provide an informative physics-based prior on the environment's optimal
state-visitation distribution. Specifically, we present a new technique,
Learning with Planned Episodic Resets (LeaPER), that resets the environment's
state to one informed by the prior during the learning phase. We experimentally
show that LeaPER significantly outperforms traditional RL approaches by a
factor of up to 5X on simulated rearrangement. Further, we relax dynamics from
quasi-static to welded contacts to illustrate that LeaPER is robust to the use
of simpler physics models. Finally, LeaPER's closed-loop policies significantly
improve task success rates relative to both open-loop controls with a planned
path or simple feedback controllers that track open-loop trajectories. We
demonstrate the performance and behavior of LeaPER on a physical 7-DOF
manipulator in https://youtu.be/feS-zFq6J1c
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