642 research outputs found
Construction Site Safety Monitoring and Excavator Activity Analysis System
With the recent advancements in deep learning and computer vision, the
AI-powered construction machine such as autonomous excavator has made
significant progress. Safety is the most important section in modern
construction, where construction machines are more and more automated. In this
paper, we propose a vision-based excavator perception, activity analysis, and
safety monitoring system. Our perception system could detect multi-class
construction machines and humans in real-time while estimating the poses and
actions of the excavator. Then, we present a novel safety monitoring and
excavator activity analysis system based on the perception result. To evaluate
the performance of our method, we collect a dataset using the Autonomous
Excavator System (AES) including multi-class of objects in different lighting
conditions with human annotations. We also evaluate our method on a benchmark
construction dataset. The results showed our YOLO v5 multi-class objects
detection model improved inference speed by 8 times (YOLO v5 x-large) to 34
times (YOLO v5 small) compared with Faster R-CNN/ YOLO v3 model. Furthermore,
the accuracy of YOLO v5 models is improved by 2.7% (YOLO v5 x-large) while
model size is reduced by 63.9% (YOLO v5 x-large) to 93.9% (YOLO v5 small). The
experimental results show that the proposed action recognition approach
outperforms the state-of-the-art approaches on top-1 accuracy by about 5.18%.
The proposed real-time safety monitoring system is not only designed for our
Autonomous Excavator System (AES) in solid waste scenes, it can also be applied
to general construction scenarios
Testing for Common Trends in Semiparametric Panel Data Models with Fixed Effects
This paper proposes a nonparametric test for common trends in semiparametric panel data models with fixed effects based on a measure of nonparametric goodness-of-fit (R^2). We first estimate the model under the null hypothesis of common trends by the method of profile least squares, and obtain the augmented residual which consistently estimates the sum of the fixed effect and the disturbance under the null. Then we run a local linear regression of the augmented residuals on a time trend and calculate the nonparametric R^2 for each cross section unit. The proposed test statistic is obtained by averaging all cross sectional nonparametric R^2's, which is close to zero under the null and deviates from zero under the alternative. We show that after appropriate standardization the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives. We prove test consistency and propose a bootstrap procedure to obtain p-values. Monte Carlo simulations indicate that the test performs well in finite samples. Empirical applications are conducted exploring the commonality of spatial trends in UK climate change data and idiosyncratic trends in OECD real GDP growth data. Both applications reveal the fragility of the widely adopted common trends assumption.Common trends, Local polynomial estimation, Nonparametric goodness-of-fit, Panel data, Profile least squares
Hybridization of Decomposition and Local Search for Multiobjective Optimization
Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population PLfor recording the current solution to each subproblem; 2) population PPfor storing starting solutions for Pareto local search; and 3) an external population PEfor maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in PPto update PLand PE. Then a single objective local search is applied to each perturbed solution in PLfor improving PLand PE, and reinitializing PP. The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems
Detecting latent communities in network formation models
This paper proposes a logistic undirected network formation model which
allows for assortative matching on observed individual characteristics and the
presence of edge-wise fixed effects. We model the coefficients of observed
characteristics to have a latent community structure and the edge-wise fixed
effects to be of low rank. We propose a multi-step estimation procedure
involving nuclear norm regularization, sample splitting, iterative logistic
regression and spectral clustering to detect the latent communities. We show
that the latent communities can be exactly recovered when the expected degree
of the network is of order log n or higher, where n is the number of nodes in
the network. The finite sample performance of the new estimation and inference
methods is illustrated through both simulated and real datasets.Comment: 63 page
Learning Excavation of Rigid Objects with Offline Reinforcement Learning
Autonomous excavation is a challenging task. The unknown contact dynamics
between the excavator bucket and the terrain could easily result in large
contact forces and jamming problems during excavation. Traditional model-based
methods struggle to handle such problems due to complex dynamic modeling. In
this paper, we formulate the excavation skills with three novel manipulation
primitives. We propose to learn the manipulation primitives with offline
reinforcement learning (RL) to avoid large amounts of online robot
interactions. The proposed method can learn efficient penetration skills from
sub-optimal demonstrations, which contain sub-trajectories that can be
``stitched" together to formulate an optimal trajectory without causing
jamming. We evaluate the proposed method with extensive experiments on
excavating a variety of rigid objects and demonstrate that the learned policy
outperforms the demonstrations. We also show that the learned policy can
quickly adapt to unseen and challenging fragmented rocks with online
fine-tuning.Comment: Submitted to IROS 202
Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles
Learning-based approaches to autonomous vehicle planners have the potential
to scale to many complicated real-world driving scenarios by leveraging huge
amounts of driver demonstrations. However, prior work only learns to estimate a
single planning trajectory, while there may be multiple acceptable plans in
real-world scenarios. To solve the problem, we propose an interpretable neural
planner to regress a heatmap, which effectively represents multiple potential
goals in the bird's-eye view of an autonomous vehicle. The planner employs an
adaptive Gaussian kernel and relaxed hourglass loss to better capture the
uncertainty of planning problems. We also use a negative Gaussian kernel to add
supervision to the heatmap regression, enabling the model to learn collision
avoidance effectively. Our systematic evaluation on the Lyft Open Dataset
across a diverse range of real-world driving scenarios shows that our model
achieves a safer and more flexible driving performance than prior works
Efficient motion planning using generalized penetration depth computation
Motion planning is a fundamental problem in robotics and also arises in other applications including virtual prototyping, navigation, animation and computational structural biology. It has been extensively studied for more than three decades, though most practical algorithms are based on randomized sampling. In this dissertation, we address two main issues that arise with respect to these algorithms: (1) there are no good practical approaches to check for path non-existence even for low degree-of-freedom (DOF) robots; (2) the performance of sampling-based planners can degrade if the free space of a robot has narrow passages. In order to develop effective algorithms to deal with these problems, we use the concept of penetration depth (PD) computation. By quantifying the extent of the intersection between overlapping models (e.g. a robot and an obstacle), PD can provide a distance measure for the configuration space obstacle (C-obstacle). We extend the prior notion of translational PD to generalized PD, which takes into account translational as well as rotational motion to separate two overlapping models. Moreover, we formulate generalized PD computation based on appropriate model-dependent metrics and present two algorithms based on convex decomposition and local optimization. We highlight the efficiency and robustness of our PD algorithms on many complex 3D models. Based on generalized PD computation, we present the first set of practical algorithms for low DOF complete motion planning. Moreover, we use generalized PD computation to develop a retraction-based planner to effectively generate samples in narrow passages for rigid robots. The effectiveness of the resulting planner is shown by alpha puzzle benchmark and part disassembly benchmarks in virtual prototyping
DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes
Despite the recent success of Neural Radiance Field (NeRF), it is still
challenging to render large-scale driving scenes with long trajectories,
particularly when the rendering quality and efficiency are in high demand.
Existing methods for such scenes usually involve with spatial warping,
geometric supervision from zero-shot normal or depth estimation, or scene
division strategies, where the synthesized views are often blurry or fail to
meet the requirement of efficient rendering. To address the above challenges,
this paper presents a novel framework that learns a density space from the
scenes to guide the construction of a point-based renderer, dubbed as DGNR
(Density-Guided Neural Rendering). In DGNR, geometric priors are no longer
needed, which can be intrinsically learned from the density space through
volumetric rendering. Specifically, we make use of a differentiable renderer to
synthesize images from the neural density features obtained from the learned
density space. A density-based fusion module and geometric regularization are
proposed to optimize the density space. By conducting experiments on a widely
used autonomous driving dataset, we have validated the effectiveness of DGNR in
synthesizing photorealistic driving scenes and achieving real-time capable
rendering
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