arXiv subjects

William Melek

Publications and source records attributed to William Melek.

13 recordsLinked to original sources

arXiv2026

Hemispherical Angular Power Mapping of Installed mmWave Radar Modules Under Realistic Deployment Constraints

Characterizing the angular radiation behavior of installed millimeter-wave (mmWave) radar modules is increasingly important in practical sensing platforms, where packaging, mounting hardware, and nearby structures can significantly alter the effective emission profile. However, once a device is embedded in its host environment, conventional chamber- and turntable-based antenna measurements are often impractical. This paper presents a hemispherical angular received-power mapping methodology for in-situ EM validation of installed mmWave modules under realistic deployment constraints. The approach samples the accessible half-space around a stationary device-under-test by placing a calibrated receiving probe at prescribed (phi, theta, r) locations using geometry-consistent positioning and quasi-static acquisition. Amplitude-only received-power is recorded using standard RF instrumentation to generate hemispherical angular power maps that capture installation-dependent radiation characteristics. Proof-of-concept measurements on a 60-GHz radar module demonstrate repeatable hemi-spherical mapping with angular trends in good agreement with full-wave simulation, supporting practical on-site characterization of embedded mmWave transmitters.

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arXiv2026

Automated Angular Received-Power Characterization of Embedded mmWave Transmitters Using Geometry-Calibrated Spatial Sampling

This paper presents an automated measurement methodology for angular received-power characterization of embedded millimeter-wave transmitters using geometry-calibrated spatial sampling. Characterization of integrated mmWave transmitters remains challenging due to limited angular coverage and alignment variability in conventional probe-station techniques, as well as the impracticality of anechoic-chamber testing for platform-mounted active modules. To address these challenges, we introduce RAPTAR, an autonomous measurement system for angular received-power acquisition under realistic installation constraints. A collaborative robot executes geometry-calibrated, collision-aware hemispherical trajectories while carrying a calibrated receive probe, enabling controlled and repeatable spatial positioning around a fixed device under test. A spectrum-analyzer-based receiver chain acquires amplitude-only received power as a function of angle and distance following quasi-static pose stabilization. The proposed framework enables repeatable angular received-power mapping and power-domain comparison against idealized free-space references derived from full-wave simulation. Experimental results for a 60-GHz radar module demonstrate a mean absolute received-power error below 2 dB relative to simulation-derived references and a 36.5 % reduction in error compared to manual probe-station measurements, attributed primarily to reduced alignment variability and consistent spatial sampling. The proposed method eliminates the need for coherent field measurements and near-field transformations, enabling practical power-domain characterization of embedded mmWave modules. It is well suited for angular validation in real-world platforms where conventional anechoic measurements are impractical.

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arXiv2025

RAPTAR: Radar Radiation Pattern Acquisition through Automated Collaborative Robotics

Accurate characterization of modern on-chip antennas remains challenging, as current probe-station techniques offer limited angular coverage, rely on bespoke hardware, and require frequent manual alignment. This research introduces RAPTAR (Radiation Pattern Acquisition through Robotic Automation), a portable, state-of-the-art, and autonomous system based on collaborative robotics. RAPTAR enables 3D radiation-pattern measurement of integrated radar modules without dedicated anechoic facilities. The system is designed to address the challenges of testing radar modules mounted in diverse real-world configurations, including vehicles, UAVs, AR/VR headsets, and biomedical devices, where traditional measurement setups are impractical. A 7-degree-of-freedom Franka cobot holds the receiver probe and performs collision-free manipulation across a hemispherical spatial domain, guided by real-time motion planning and calibration accuracy with RMS error below 0.9 mm. The system achieves an angular resolution upto 2.5 degree and integrates seamlessly with RF instrumentation for near- and far-field power measurements. Experimental scans of a 60 GHz radar module show a mean absolute error of less than 2 dB compared to full-wave electromagnetic simulations ground truth. Benchmarking against baseline method demonstrates 36.5% lower mean absolute error, highlighting RAPTAR accuracy and repeatability.

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arXiv2025

CRADMap: Applied Distributed Volumetric Mapping with 5G-Connected Multi-Robots and 4D Radar Perception

Sparse and feature SLAM methods provide robust camera pose estimation. However, they often fail to capture the level of detail required for inspection and scene awareness tasks. Conversely, dense SLAM approaches generate richer scene reconstructions but impose a prohibitive computational load to create 3D maps. We present a novel distributed volumetric mapping framework designated as CRADMap that addresses these issues by extending the state-of-the-art (SOTA) ORBSLAM3 system with the COVINS on the backend for global optimization. Our pipeline for volumetric reconstruction fuses dense keyframes at a centralized server via 5G connectivity, aggregating geometry, and occupancy information from multiple autonomous mobile robots (AMRs) without overtaxing onboard resources. This enables each AMR to independently perform mapping while the backend constructs high-fidelity real-time 3D maps. To operate Beyond the Visible (BtV) and overcome the limitations of standard visual sensors, we automated a standalone 4D mmWave radar module that functions independently without sensor fusion with SLAM. The BtV system enables the detection and mapping of occluded metallic objects in cluttered environments, enhancing situational awareness in inspection scenarios. Experimental validation in Section~\ref{sec:IV} demonstrates the effectiveness of our framework.

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arXiv2024

Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions

Autonomous driving at unsignalized intersections is still considered a challenging application for machine learning due to the complications associated with handling complex multi-agent scenarios characterized by a high degree of uncertainty. Automating the decision-making process at these safety-critical environments involves comprehending multiple levels of abstractions associated with learning robust driving behaviors to enable the vehicle to navigate efficiently. In this survey, we aim at exploring the state-of-the-art techniques implemented for decision-making applications, with a focus on algorithms that combine Reinforcement Learning (RL) and deep learning for learning traversing policies at unsignalized intersections. The reviewed schemes vary in the proposed driving scenario, in the assumptions made for the used intersection model, in the tackled challenges, and in the learning algorithms that are used. We have presented comparisons for these techniques to highlight their limitations and strengths. Based on our in-depth investigation, it can be discerned that a robust decision-making scheme for navigating real-world unsignalized intersection has yet to be developed. Along with our analysis and discussion, we recommend potential research directions encouraging the interested players to tackle the highlighted challenges. By adhering to our recommendations, decision-making architectures that are both non-overcautious and safe, yet feasible, can be trained and validated in real-world unsignalized intersections environments.

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arXiv2024

Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning

Learning-based methods have proven useful at generating complex motions for robots, including humanoids. Reinforcement learning (RL) has been used to learn locomotion policies, some of which leverage a periodic reward formulation. This work extends the periodic reward formulation of locomotion to skateboarding for the REEM-C robot. Brax/MJX is used to implement the RL problem to achieve fast training. Initial results in simulation are presented with hardware experiments in progress.

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arXiv2024

Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJX

Humanoid locomotion is a key skill to bring humanoids out of the lab and into the real-world. Many motion generation methods for locomotion have been proposed including reinforcement learning (RL). RL locomotion policies offer great versatility and generalizability along with the ability to experience new knowledge to improve over time. This work presents a velocity-based RL locomotion policy for the REEM-C robot. The policy uses a periodic reward formulation and is implemented in Brax/MJX for fast training. Simulation results for the policy are demonstrated with future experimental results in progress.

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arXiv2024

5G Virtual Reality Manipulator Teleoperation using a Mobile Phone

This paper presents an approach to teleoperate a manipulator using a mobile phone as a leader device. Using its IMU and camera, the phone estimates its Cartesian pose which is then used to to control the Cartesian pose of the robot's tool. The user receives visual feedback in the form of multi-view video - a point cloud rendered in a virtual reality environment. This enables the user to observe the scene from any position. To increase immersion, the robot's estimate of external forces is relayed using the phone's haptic actuator. Leader and follower are connected through wireless networks such as 5G or Wi-Fi. The paper describes the setup and analyzes its performance.

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arXiv2024

RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical Flow

When applied to autonomous vehicle (AV) settings, action recognition can enhance an environment model's situational awareness. This is especially prevalent in scenarios where traditional geometric descriptions and heuristics in AVs are insufficient. However, action recognition has traditionally been studied for humans, and its limited adaptability to noisy, un-clipped, un-pampered, raw RGB data has limited its application in other fields. To push for the advancement and adoption of action recognition into AVs, this work proposes a novel two-stage action recognition system, termed RALACs. RALACs formulates the problem of action recognition for road scenes, and bridges the gap between it and the established field of human action recognition. This work shows how attention layers can be useful for encoding the relations across agents, and stresses how such a scheme can be class-agnostic. Furthermore, to address the dynamic nature of agents on the road, RALACs constructs a novel approach to adapting Region of Interest (ROI) Alignment to agent tracks for downstream action classification. Finally, our scheme also considers the problem of active agent detection, and utilizes a novel application of fusing optical flow maps to discern relevant agents in a road scene. We show that our proposed scheme can outperform the baseline on the ICCV2021 Road Challenge dataset and by deploying it on a real vehicle platform, we provide preliminary insight to the usefulness of action recognition in decision making.

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arXiv2022

Real-Time Unified Trajectory Planning and Optimal Control for Urban Autonomous Driving Under Static and Dynamic Obstacle Constraints

Trajectory planning and control have historically been separated into two modules in automated driving stacks. Trajectory planning focuses on higher-level tasks like avoiding obstacles and staying on the road surface, whereas the controller tries its best to follow an ever changing reference trajectory. We argue that this separation is (1) flawed due to the mismatch between planned trajectories and what the controller can feasibly execute, and (2) unnecessary due to the flexibility of the model predictive control (MPC) paradigm. Instead, in this paper, we present a unified MPC-based trajectory planning and control scheme that guarantees feasibility with respect to road boundaries, the static and dynamic environment, and enforces passenger comfort constraints. The scheme is evaluated rigorously in a variety of scenarios focused on proving the effectiveness of the optimal control problem (OCP) design and real-time solution methods. The prototype code will be released at https://github.com/WATonomous/control.

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arXiv2022

Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving

While supervised detection and classification frameworks in autonomous driving require large labelled datasets to converge, Unsupervised Domain Adaptation (UDA) approaches, facilitated by synthetic data generated from photo-real simulated environments, are considered low-cost and less time-consuming solutions. In this paper, we propose UDA schemes using adversarial discriminative and generative methods for lane detection and classification applications in autonomous driving. We also present Simulanes dataset generator to create a synthetic dataset that is naturalistic utilizing CARLA's vast traffic scenarios and weather conditions. The proposed UDA frameworks take the synthesized dataset with labels as the source domain, whereas the target domain is the unlabelled real-world data. Using adversarial generative and feature discriminators, the learnt models are tuned to predict the lane location and class in the target domain. The proposed techniques are evaluated using both real-world and our synthetic datasets. The results manifest that the proposed methods have shown superiority over other baseline schemes in terms of detection and classification accuracy and consistency. The ablation study reveals that the size of the simulation dataset plays important roles in the classification performance of the proposed methods. Our UDA frameworks are available at https://github.com/anita-hu/sim2real-lane-detection and our dataset generator is released at https://github.com/anita-hu/simulanes

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arXiv2021

End-Effector Stabilization of a 10-DOF Mobile Manipulator using Nonlinear Model Predictive Control

Motion control of mobile manipulators (a robotic arm mounted on a mobile base) can be challenging for complex tasks such as material and package handling. In this paper, a task-space stabilization controller based on Nonlinear Model Predictive Control (NMPC) is designed and implemented to a 10 Degrees of Freedom (DOF) mobile manipulator which consists of a 7-DOF robotic arm and a 3-DOF mobile base. The system model is based on kinematic models where the end-effector orientation is parameterized directly by a rotation matrix. The state and control constraints as well as singularity constraints are explicitly included in the NMPC formulation. The controller is tested using real-time simulations, which demonstrate high positioning accuracy with tractable computational cost.

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arXiv2020

Learning in the Sky: An Efficient 3D Placement of UAVs

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations can deliver a fast and flexible solution for serving varying traffic demand. In order to adequately benefit of UAVs deployment, their efficient placement is of utmost importance, and requires to intelligently adapt to the environment changes. In this paper, we propose a learning-based mechanism for the three-dimensional deployment of UAVs assisting terrestrial cellular networks in the downlink. The problem is modeled as a non-cooperative game among UAVs in satisfaction form. To solve the game, we utilize a low complexity algorithm, in which unsatisfied UAVs update their locations based on a learning algorithm. Simulation results reveal that the proposed UAV placement algorithm yields significant performance gains up to about 52% and 74% in terms of throughput and the number of dropped users, respectively, compared to an optimized baseline algorithm.

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