GeoPro-VO: Dynamic Obstacle Avoidance with Geometric Projector Based on Velocity Obstacle

Optimization-based approaches are widely employed to generate optimal robot motions while considering various constraints, such as robot dynamics, collision avoidance, and physical limitations. It is crucial to efficiently solve the optimization problems in practice, yet achieving rapid computations remains a great challenge for optimization-based approaches with nonlinear constraints. In this paper, we propose a geometric projector for dynamic obstacle avoidance based on velocity obstacle (GeoPro-VO) by leveraging the projection feature of the velocity cone set represented by VO. Furthermore, with the proposed GeoPro-VO and the augmented Lagrangian spectral projected gradient descent (ALSPG) algorithm, we transform an initial mixed integer nonlinear programming problem (MINLP) in the form of constrained model predictive control (MPC) into a sub-optimization problem and solve it efficiently. Numerical simulations are conducted to validate the fast computing speed of our approach and its capability for reliable dynamic obstacle avoidance.

Unraveling the Key of Machine Learning Solutions for Android Malware Detection

Android malware detection serves as the front line against malicious apps. With the rapid advancement of machine learning (ML), ML-based Android malware detection has attracted increasing attention due to its capability of automatically capturing malicious patterns from Android APKs. These learning-driven methods have reported promising results in detecting malware. However, the absence of an in-depth analysis of current research progress makes it difficult to gain a holistic picture of the state of the art in this area. This paper presents a comprehensive investigation to date into ML-based Android malware detection with empirical and quantitative analysis. We first survey the literature, categorizing contributions into a taxonomy based on the Android feature engineering and ML modeling pipeline. Then, we design a general-propose framework for ML-based Android malware detection, re-implement 12 representative approaches from different research communities, and evaluate them from three primary dimensions, i.e., effectiveness, robustness, and efficiency. The evaluation reveals that ML-based approaches still face open challenges and provides insightful findings like more powerful ML models are not the silver bullet for designing better malware detectors. We further summarize our findings and put forth recommendations to guide future research.

Fast Path Planning for Autonomous Vehicle Parking with Safety-Guarantee using Hamilton-Jacobi Reachability

We present a fast planning architecture called Hamilton-Jacobi-based bidirectional A* (HJBA*) to solve general tight parking scenarios. The algorithm is a two-layer composed of a high-level HJ-based reachability analysis and a lower-level bidirectional A* search algorithm. In high-level reachability analysis, a backward reachable tube (BRT) concerning vehicle dynamics is computed by the HJ analysis and it intersects with a safe set to get a safe reachable set. The safe set is defined by constraints of positive signed distances for obstacles in the environment and computed by solving QP optimization problems offline. For states inside the intersection set, i.e., the safe reachable set, the computed backward reachable tube ensures they are reachable subjected to system dynamics and input bounds, and the safe set guarantees they satisfy parking safety with respect to obstacles in different shapes. For online computation, randomized states are sampled from the safe reachable set, and used as heuristic guide points to be considered in the bidirectional A* search. The bidirectional A* search is paralleled for each randomized state from the safe reachable set. We show that the proposed two-level planning algorithm is able to solve different parking scenarios effectively and computationally fast for typical parking requests. We validate our algorithm through simulations in large-scale randomized parking scenarios and demonstrate it to be able to outperform other state-of-the-art parking planning algorithms.

Obstacle Avoidance for Unicycle-Modelled Mobile Robots with Time-varying Control Barrier Functions

In this paper, we propose a safety-critical controller based on time-varying control barrier functions (CBFs) for a robot with an unicycle model in the continuous-time domain to achieve navigation and dynamic collision avoidance. Unlike previous works, our proposed approach can control both linear and angular velocity to avoid collision with obstacles, overcoming the limitation of confined control performance due to the lack of control variable. To ensure that the robot reaches its destination, we also design a control Lyapunov function (CLF). Our safety-critical controller is formulated as a quadratic program (QP) optimization problem that incorporates CLF and CBFs as constraints, enabling real-time application for navigation and dynamic collision avoidance. Numerical simulations are conducted to verify the effectiveness of our proposed approach.

Nonsmooth Control Barrier Functions for Obstacle Avoidance between Convex Regions

In this paper, we focus on non-conservative obstacle avoidance between robots with control affine dynamics with strictly convex and polytopic shapes. The core challenge for this obstacle avoidance problem is that the minimum distance between strictly convex regions or polytopes is generally implicit and non-smooth, such that distance constraints cannot be enforced directly in the optimization problem. To handle this challenge, we employ non-smooth control barrier functions to reformulate the avoidance problem in the dual space, with the positivity of the minimum distance between robots equivalently expressed using a quadratic program. Our approach is proven to guarantee system safety. We theoretically analyze the smoothness properties of the minimum distance quadratic program and its KKT conditions. We validate our approach by demonstrating computationally-efficient obstacle avoidance for multi-agent robotic systems with strictly convex and polytopic shapes. To our best knowledge, this is the first time a real-time QP problem can be formulated for general non-conservative avoidance between strictly convex shapes and polytopes.

Velocity Obstacle for Polytopic Collision Avoidance for Distributed Multi-robot Systems

Obstacle avoidance for multi-robot navigation with polytopic shapes is challenging. Existing works simplify the system dynamics or consider it as a convex or non-convex optimization problem with positive distance constraints between robots, which limits real-time performance and scalability. Additionally, generating collision-free behavior for polytopic-shaped robots is harder due to implicit and non-differentiable distance functions between polytopes. In this paper, we extend the concept of velocity obstacle (VO) principle for polytopic-shaped robots and propose a novel approach to construct the VO in the function of vertex coordinates and other robot's states. Compared with existing work about obstacle avoidance between polytopic-shaped robots, our approach is much more computationally efficient as the proposed approach for construction of VO between polytopes is optimization-free. Based on VO representation for polytopic shapes, we later propose a navigation approach for distributed multi-robot systems. We validate our proposed VO representation and navigation approach in multiple challenging scenarios including large-scale randomized tests, and our approach outperforms the state of art in many evaluation metrics, including completion rate, deadlock rate, and the average travel distance.

i2LQR: Iterative LQR for Iterative Tasks in Dynamic Environments

This work introduces a novel control strategy called Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which aims to pursue optimal performance for iterative tasks in a dynamic environment. The proposed algorithm is reference-free and utilizes historical data from previous iterations to enhance the performance of the autonomous system. Unlike existing algorithms, the i2LQR computes the optimal solution in an iterative manner at each timestamp, rendering it well-suited for iterative tasks with changing constraints at different iterations. To evaluate the performance of the proposed algorithm, we conduct numerical simulations for an iterative task aimed at minimizing completion time. The results show that i2LQR achieves the optimal performance as the state-of-the-art algorithm in static environments, and outperforms the state-of-the-art algorithm in dynamic environments with both static and dynamics obstacles.

Walking in Narrow Spaces: Safety-critical Locomotion Control for Quadrupedal Robots with Duality-based Optimization

This paper presents a safety-critical locomotion control framework for quadrupedal robots. Our goal is to enable quadrupedal robots to safely navigate in cluttered environments. To tackle this, we introduce exponential Discrete Control Barrier Functions (exponential DCBFs) with duality-based obstacle avoidance constraints into a Nonlinear Model Predictive Control (NMPC) with Whole-Body Control (WBC) framework for quadrupedal locomotion control. This enables us to use polytopes to describe the shapes of the robot and obstacles for collision avoidance while doing locomotion control of quadrupedal robots. Compared to most prior work, especially using CBFs, that utilize spherical and conservative approximation for obstacle avoidance, this work demonstrates a quadrupedal robot autonomously and safely navigating through very tight spaces in the real world. (Our open-source code is available at github.com/HybridRobotics/quadruped_nmpc_dcbf_duality, and the video is available at youtu.be/p1gSQjwXm1Q.)

Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble

Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the data scarcity problem in NER by automatically generating training samples. Unfortunately, the distant supervision may induce noisy labels, thus undermining the robustness of the learned models and restricting the practical application. To relieve this problem, recent works adopt self-training teacher-student frameworks to gradually refine the training labels and improve the generalization ability of NER models. However, we argue that the performance of the current self-training frameworks for DS-NER is severely underestimated by their plain designs, including both inadequate student learning and coarse-grained teacher updating. Therefore, in this paper, we make the first attempt to alleviate these issues by proposing: (1) adaptive teacher learning comprised of joint training of two teacher-student networks and considering both consistent and inconsistent predictions between two teachers, thus promoting comprehensive student learning. (2) fine-grained student ensemble that updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise. To verify the effectiveness of our proposed method, we conduct experiments on four DS-NER datasets. The experimental results demonstrate that our method significantly surpasses previous SOTA methods.