274 research outputs found
Leveraging Distributional Bias for Reactive Collision Avoidance under Uncertainty: A Kernel Embedding Approach
Many commodity sensors that measure the robot and dynamic obstacle's state
have non-Gaussian noise characteristics. Yet, many current approaches treat the
underlying-uncertainty in motion and perception as Gaussian, primarily to
ensure computational tractability. On the other hand, existing planners working
with non-Gaussian uncertainty do not shed light on leveraging distributional
characteristics of motion and perception noise, such as bias for efficient
collision avoidance.
This paper fills this gap by interpreting reactive collision avoidance as a
distribution matching problem between the collision constraint violations and
Dirac Delta distribution. To ensure fast reactivity in the planner, we embed
each distribution in Reproducing Kernel Hilbert Space and reformulate the
distribution matching as minimizing the Maximum Mean Discrepancy (MMD) between
the two distributions. We show that evaluating the MMD for a given control
input boils down to just matrix-matrix products. We leverage this insight to
develop a simple control sampling approach for reactive collision avoidance
with dynamic and uncertain obstacles.
We advance the state-of-the-art in two respects. First, we conduct an
extensive empirical study to show that our planner can infer distributional
bias from sample-level information. Consequently, it uses this insight to guide
the robot to good homotopy. We also highlight how a Gaussian approximation of
the underlying uncertainty can lose the bias estimate and guide the robot to
unfavorable states with a high collision probability. Second, we show tangible
comparative advantages of the proposed distribution matching approach for
collision avoidance with previous non-parametric and Gaussian approximated
methods of reactive collision avoidance
UrbanFly: Uncertainty-Aware Planning for Navigation Amongst High-Rises with Monocular Visual-Inertial SLAM Maps
We present UrbanFly: an uncertainty-aware real-time planning framework for
quadrotor navigation in urban high-rise environments. A core aspect of UrbanFly
is its ability to robustly plan directly on the sparse point clouds generated
by a Monocular Visual Inertial SLAM (VINS) backend. It achieves this by using
the sparse point clouds to build an uncertainty-integrated cuboid
representation of the environment through a data-driven monocular plane
segmentation network. Our chosen world model provides faster distance queries
than the more common voxel-grid representation, and UrbanFly leverages this
capability in two different ways leading to as many trajectory optimizers. The
first optimizer uses a gradient-free cross-entropy method to compute
trajectories that minimize collision probability and smoothness cost. Our
second optimizer is a simplified version of the first and uses a sequential
convex programming optimizer initialized based on probabilistic safety
estimates on a set of randomly drawn trajectories. Both our trajectory
optimizers are made computationally tractable and independent of the nature of
underlying uncertainty by embedding the distribution of collision violations in
Reproducing Kernel Hilbert Space. Empowered by the algorithmic innovation,
UrbanFly outperforms competing baselines in metrics such as collision rate,
trajectory length, etc., on a high fidelity AirSim simulator augmented with
synthetic and real-world dataset scenes.Comment: Submitted to IROS 2022, Code available at
https://github.com/sudarshan-s-harithas/UrbanFl
Remote ID for separation provision and multi-agent navigation
In this paper, we investigate the integration of drone identification data
(Remote ID) with collision avoidance mechanisms to improve the safety and
efficiency of multi-drone operations. We introduce an improved Near Mid-Air
Collision (NMAC) definition, termed as UAV NMAC (uNMAC), which accounts for
uncertainties in the drone's location due to self-localization errors and
possible displacements between two location reports. Our proposed uNMAC-based
Reciprocal Velocity Obstacle (RVO) model integrates Remote ID messages with RVO
to enable enhanced collision-free navigation. We propose modifications to the
Remote ID format to include data on localization accuracy and drone airframe
size, facilitating more efficient collision avoidance decisions. Through
extensive simulations, we demonstrate that our approach halves mission
execution times compared to a conservative standard Remote ID-based RVO.
Importantly, it ensures collision-free operations even under localization
uncertainties. By integrating the improved Remote ID messages and uNMAC-based
RVO, we offer a solution to significantly increase airspace capacity while
adhering to strict safety standards. Our study emphasizes the potential to
augment the safety and efficiency of future drone operations, thereby
benefiting industries reliant on drone technologies.Comment: 10 pages, 8 figures, 2023 IEEE/AIAA 42nd Digital Avionics Systems
Conference (DASC
Probabilistic Inference for Model Based Control
Robotic systems are essential for enhancing productivity, automation, and performing hazardous tasks. Addressing the unpredictability of physical systems, this thesis advances robotic planning and control under uncertainty, introducing learning-based methods for managing uncertain parameters and adapting to changing environments in real-time.
Our first contribution is a framework using Bayesian statistics for likelihood-free inference of model parameters. This allows employing complex simulators for designing efficient, robust controllers. The method, integrating the unscented transform with a variant of information theoretical model predictive control, shows better performance in trajectory evaluation compared to Monte Carlo sampling, easing the computational load in various control and robotics tasks.
Next, we reframe robotic planning and control as a Bayesian inference problem, focusing on the posterior distribution of actions and model parameters. An implicit variational inference algorithm, performing Stein Variational Gradient Descent, estimates distributions over model parameters and control inputs in real-time. This Bayesian approach effectively handles complex multi-modal posterior distributions, vital for dynamic and realistic robot navigation.
Finally, we tackle diversity in high-dimensional spaces. Our approach mitigates underestimation of uncertainty in posterior distributions, which leads to locally optimal solutions. Using the theory of rough paths, we develop an algorithm for parallel trajectory optimisation, enhancing solution diversity and avoiding mode collapse. This method extends our variational inference approach for trajectory estimation, employing diversity-enhancing kernels and leveraging path signature representation of trajectories. Empirical tests, ranging from 2-D navigation to robotic manipulators in cluttered environments, affirm our method's efficiency, outperforming existing alternatives
Of Priors and Particles: Structured and Distributed Approaches to Robot Perception and Control
Applications of robotic systems have expanded significantly in their scope, moving beyond the caged predictability of industrial automation and towards more open, unstructured environments. These agents must learn to reliably perceive their surroundings, efficiently integrate new information and quickly adapt to dynamic perturbations. To accomplish this, we require solutions which can effectively incorporate prior knowledge while maintaining the generality of learned representations. These systems must also contend with uncertainty in both their perception of the world and in predicting possible future outcomes. Efficient methods for probabilistic inference are then key to realizing robust, adaptive behavior.
This thesis will first examine data-driven approaches for learning and combining perceptual models for both visual and tactile sensor modalities, common in robotics. Modern variational inference methods will then be examined in the context of online optimization and stochastic optimal control. Specifically, this thesis will contribute (1) data-driven visual and tactile perceptual models leveraging kinematic and dynamic priors, (2) a framework for joint inference with visuo-tactile sensing, (3) a family of particle-based, variational model predictive control and planning algorithms, and (4) a distributed inference scheme for online model adaptation.Ph.D
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