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Senast publicerade

Inverse kinematic optimization and MPC on a four-link hydraulic manipulator
(2026) Billing, Konrad; Djäknegren, Filip
Operating individual joints on a hydraulic manipulator can be tedious and unproductive with limited experience. Therefore, this thesis presents a method to ease operator strain and lessen experience requirements by enabling cartesian control of the robot tool. This is achieved with differential closed-loop inverse kinematics and linear model predictive motion control. References are generated with a pseudo-inverse Jacobian algorithm formulated as a quadratic program with a secondary null-space objective to exploit redundant joints. A sensitivity analysis of a non-linear hydraulic model resulted in a linear first-order model which was used to design an MPC that tracks the references. Results from a simulated robot show robust tracking performance even when the robot is exposed to noise and unmodeled disturbances. The designed null-space objective manages to control null-space motion and the CLIK is shown to reject unmodeled disturbances.
Analysis of AEB False Positives in Urban Bus Operations
(2026) Gong, Youli; Wang, Jingyu
False positive (FP) activations of Automatic Emergency Braking (AEB) in urban buses can cause unnecessary harsh braking and reduce system acceptance for situations involving both passengers and vulnerable road users. This report implements a traceable, criteria-based pipeline to identify candidate FP events from raw ASAM MDF 4 (MF4) recordings, structured as file-level screening, qualified brake-event detection, and FP classification. The implementation uses core Controller Area Network (CAN) signals to detect AEB-active braking events and applies collisionrisk and driver-response criteria for classification, where time-to-collision (TTC) is computed from recorded signals and used as one decision input. The TTC computation matches a ground-truth TTC reference provided by a colleague within the Group Trucks Technology team on the evaluated data, and the end-to-end detection pipeline runs on an internal dataset to automatically extract candidate FP events for further attribution and performance analysis.
Stochastic Charge Planning with Dynamic Programming
(2026) Höglund, Oskar; Sandström, Filip
The development of charge planning algorithms which extend further than those considering uncorrelated disturbance models and produce robust policies is an important subject. The freight sector is moving towards battery electric trucks where uncertainties can have a major impact on missions due to state of charge constraints. Therefore, this thesis investigates dynamic programming algorithms for use in charge planning. Disturbances are modeled as Gaussian processes, which for certain structures admits an equivalent transformation to an LTI SDE system. Using this transformation, the distribution along state trajectories are estimated using an unscented Kalman filter. The UKF showed good performance for the modeled disturbances, with a largest mean bias of 1.106% in a worst-case scenario. The proposed approximate dynamic based charge planning algorithm became robust under stochastic external uncertainties from wind and traffic by implementing chance constraints. The proposed planning algorithms achieved better performance than both a simpler deterministic dynamic programming algorithm and a simple heuristic planner. Computational complexity remains a key concern for real time implementations and is a crucial challenge when designing stochastic charge planning algorithms.
Electromagnetic Simulation of a CATR for Digital Twin Development
(2026) Lennernäs, Christoffer; Gyllenhammar, Rasmus
This thesis presents the development of a simulation model of an Over-The-Air (OTA) Compact Antenna Test Range (CATR) chamber as an initial step toward a digital twin of Ericsson’s OTA environment. The work investigates how scattering objects and different chamber configurations affect Quiet Zone (QZ) performance. Electromagnetic simulations were performed using Physical Optics (PO) and Method of Moments (MoM) formulations to evaluate field distributions and to study QZ characteristics such as amplitude, phase, and ripple variations. The results show that the model can reproduce some qualitative features of OTA chamber behavior, particularly QZ degradation due to scattering objects and reflector geometry. Comparison between PO and MoM highlights limitations of the PO approximation, especially where edge effects dominate, leading to discrepancies in field distribution and ripple behavior near reflector edges. Despite these limitations, the model enables useful comparative analysis of chamber configurations. Variations in reflector geometry and the presence of scattering objects consistently affect QZ uniformity, influencing both amplitude and phase distributions. The study also indicates that improvements from serrated reflector edges are mainly linked to an increased effective reflecting area. Overall, the simulation framework provides a first modelling step toward a future digital twin of the OTA chamber. While absolute accuracy is limited by the current electromagnetic formulation, the model is useful for studying relative QZ degradation and guiding future improvements in chamber design and simulation fidelity.
Non-Visual Human Eye Gaze Tracking Using Radar and Artificial Intelligence for Robust Driver Monitoring Systems
(2026) Jiang, Yuqing; Zhou, Mengyuan
Estimating gaze direction from in-cabin sensors is central to driver distraction monitoring. Existing methods rely on cameras, which degrade under adverse lighting and collect identifiable facial imagery. Millimetre-wave radar avoids both limitations, and prior work at 60 GHz has demonstrated sensitivity to eye-region micro-motion such as blinks. Whether radar can support directional gaze estimation, rather than binary event detection, remains an open question. This thesis develops a radar-only gaze estimation pipeline built on a 60 GHz FMCW sensor. Amplitude and inter-receiver phase-difference cues are extracted from the radar return, and candidate eye-movement events are detected from the radar signal alone, removing the need for camera or stimulus timing at inference. A lightweight dual-stream temporal architecture, DualStreamGazeNet, encodes the two cue types separately, fuses them through cross-modal self-attention, and produces both a direction label and a continuous gaze angle through a modality-aware hierarchical classifier and an auxiliary regression head. Experiments on a multi-session dataset show that the model achieves 85.7% balanced accuracy for four-direction classification with azimuth and elevation errors of 7.82◦ and 3.15◦. Under strict cross-session evaluation, few-shot calibration with three to five labelled events per direction yields 82.1% balanced accuracy, demonstrating that the learned representation generalises effectively with minimal target-session adaptation. These results establish that close-range mmWave radar is a viable modality for event-level gaze estimation and can serve as a privacy-preserving, illuminationindependent complement to driver monitoring system.