202 research outputs found
NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments
Balancing the trade-off between safety and efficiency is of significant
importance for path planning under uncertainty. Many risk-aware path planners
have been developed to explicitly limit the probability of collision to an
acceptable bound in uncertain environments. However, convex obstacles or
Gaussian uncertainties are usually assumed to make the problem tractable in the
existing method. These assumptions limit the generalization and application of
path planners in real-world implementations. In this article, we propose to
apply deep learning methods to the sampling-based planner, developing a novel
risk bounded near-optimal path planning algorithm named neural risk-aware RRT
(NR-RRT). Specifically, a deterministic risk contours map is maintained by
perceiving the probabilistic nonconvex obstacles, and a neural network sampler
is proposed to predict the next most-promising safe state. Furthermore, the
recursive divide-and-conquer planning and bidirectional search strategies are
used to accelerate the convergence to a near-optimal solution with guaranteed
bounded risk. Worst-case theoretical guarantees can also be proven owing to a
standby safety guaranteed planner utilizing a uniform sampling distribution.
Simulation experiments demonstrate that the proposed algorithm outperforms the
state-of-the-art remarkably for finding risk bounded low-cost paths in seen and
unseen environments with uncertainty and nonconvex constraints
Control and Optimization for Aerospace Systems with Stochastic Disturbances, Uncertainties, and Constraints
The topic of this dissertation is the control and optimization of aerospace systems under the influence of stochastic disturbances, uncertainties, and subject to chance constraints. This problem is motivated by the uncertain operating environments of many aerospace systems, and the ever-present push to extract greater performance from these systems while maintaining safety. Explicitly accounting for the stochastic disturbances and uncertainties in the constrained control design confers the ability to assign the probability of constraint satisfaction depending on the level of risk that is deemed acceptable and allows for the possibility of theoretical constraint satisfaction guarantees.
Along these lines, this dissertation presents novel contributions addressing four different problems: 1) chance-constrained path planning for small unmanned aerial vehicles in urban environments, 2) chance-constrained spacecraft relative motion planning in low-Earth orbit, 3) stochastic optimization of suborbital launch operations, and 4) nonlinear model predictive control for tracking near rectilinear halo orbits and a proposed stochastic extension. For the first problem, existing dynamic and informed rapidly-expanding random trees algorithms are combined with a novel quadratic programming-based collision detection algorithm to enable computationally efficient, chance-constrained path planning. For the second problem, a previously proposed constrained relative motion approach based on chained positively invariant sets is extended in this dissertation to the case where the spacecraft dynamics are controlled using output feedback on noisy measurements and are subject to stochastic disturbances. Connectivity between nodes is determined through the use of chance-constrained admissible sets, guaranteeing that constraints are met with a specified probability. For the third problem, a novel approach to suborbital launch operations is presented. It utilizes linear covariance propagation and stochastic clustering optimization to create an effective software-only method for decreasing the probability of a dangerous landing with no physical changes to the vehicle and only minimal changes to its flight controls software. For the fourth problem, the use of suboptimal nonlinear model predictive control (NMPC) coupled with low-thrust actuators is considered for station-keeping on near rectilinear halo orbits. The nonlinear optimization problems in NMPC are solved with time-distributed sequential quadratic programming techniques utilizing the FBstab algorithm. A stochastic extension for this problem is also proposed. The results are illustrated using detailed numerical simulations.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162992/1/awbe_1.pd
BITKOMO: Combining Sampling and Optimization for Fast Convergence in Optimal Motion Planning
Optimal sampling based motion planning and trajectory optimization are two
competing frameworks to generate optimal motion plans. Both frameworks have
complementary properties: Sampling based planners are typically slow to
converge, but provide optimality guarantees. Trajectory optimizers, however,
are typically fast to converge, but do not provide global optimality guarantees
in nonconvex problems, e.g. scenarios with obstacles. To achieve the best of
both worlds, we introduce a new planner, BITKOMO, which integrates the
asymptotically optimal Batch Informed Trees (BIT*) planner with the K-Order
Markov Optimization (KOMO) trajectory optimization framework. Our planner is
anytime and maintains the same asymptotic optimality guarantees provided by
BIT*, while also exploiting the fast convergence of the KOMO trajectory
optimizer. We experimentally evaluate our planner on manipulation scenarios
that involve high dimensional configuration spaces, with up to two 7-DoF
manipulators, obstacles and narrow passages. BITKOMO performs better than KOMO
by succeeding even when KOMO fails, and it outperforms BIT* in terms of
convergence to the optimal solution.Comment: 6 pages, Accepted at IROS 202
Risk-Averse Trajectory Optimization via Sample Average Approximation
Trajectory optimization under uncertainty underpins a wide range of
applications in robotics. However, existing methods are limited in terms of
reasoning about sources of epistemic and aleatoric uncertainty, space and time
correlations, nonlinear dynamics, and non-convex constraints. In this work, we
first introduce a continuous-time planning formulation with an
average-value-at-risk constraint over the entire planning horizon. Then, we
propose a sample-based approximation that unlocks an efficient,
general-purpose, and time-consistent algorithm for risk-averse trajectory
optimization. We prove that the method is asymptotically optimal and derive
finite-sample error bounds. Simulations demonstrate the high speed and
reliability of the approach on problems with stochasticity in nonlinear
dynamics, obstacle fields, interactions, and terrain parameters
Neural Potential Field for Obstacle-Aware Local Motion Planning
Model predictive control (MPC) may provide local motion planning for mobile
robotic platforms. The challenging aspect is the analytic representation of
collision cost for the case when both the obstacle map and robot footprint are
arbitrary. We propose a Neural Potential Field: a neural network model that
returns a differentiable collision cost based on robot pose, obstacle map, and
robot footprint. The differentiability of our model allows its usage within the
MPC solver. It is computationally hard to solve problems with a very high
number of parameters. Therefore, our architecture includes neural image
encoders, which transform obstacle maps and robot footprints into embeddings,
which reduce problem dimensionality by two orders of magnitude. The reference
data for network training are generated based on algorithmic calculation of a
signed distance function. Comparative experiments showed that the proposed
approach is comparable with existing local planners: it provides trajectories
with outperforming smoothness, comparable path length, and safe distance from
obstacles. Experiment on Husky UGV mobile robot showed that our approach allows
real-time and safe local planning. The code for our approach is presented at
https://github.com/cog-isa/NPField together with demo video
An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments
As a core part of autonomous driving systems, motion planning has received
extensive attention from academia and industry. However, real-time trajectory
planning capable of spatial-temporal joint optimization is challenged by
nonholonomic dynamics, particularly in the presence of unstructured
environments and dynamic obstacles. To bridge the gap, we propose a real-time
trajectory optimization method that can generate a high-quality whole-body
trajectory under arbitrary environmental constraints. By leveraging the
differential flatness property of car-like robots, we simplify the trajectory
representation and analytically formulate the planning problem while
maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve
efficient obstacle avoidance with a safe driving corridor for unmodelled
obstacles and signed distance approximations for dynamic moving objects. We
present comprehensive benchmarks with State-of-the-Art methods, demonstrating
the significance of the proposed method in terms of efficiency and trajectory
quality. Real-world experiments verify the practicality of our algorithm. We
will release our codes for the research communit
Sensor-Based Reactive Navigation in Unknown Convex Sphere Worlds
We construct a sensor-based feedback law that provably solves the real-time collision-free robot navigation problem in a compact convex Euclidean subset cluttered with unknown but sufficiently separated and strongly convex obstacles. Our algorithm introduces a novel use of separating hyperplanes for identifying the robot’s local obstacle-free convex neighborhood, affording a reactive (online-computed) continuous and piecewise smooth closed-loop vector field whose smooth flow brings almost all configurations in the robot’s free space to a designated goal location, with the guarantee of no collisions along the way. Specialized attention to planar navigable environments yields a necessary and sufficient condition on convex obstacles for almost global navigation towards any goal location in the environment. We further extend these provable properties of the planar setting to practically motivated limited range, isotropic and anisotropic sensing models, and the nonholonomically constrained kinematics of the standard differential drive vehicle. We conclude with numerical and experimental evidence demonstrating the effectiveness of the proposed sensory feedback motion planner
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