http://yetl.yabesh.ir/yetl1/handle/yetl/4274528 2026-08-27T01:51:36Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315929 Forecast of the Feasibility of Aviation-to-Automotive Methods Transfer by Empirical Quantification Akkus, Yusuf; Annighöfer, Björn Abstract. This study introduces an empirical approach to quantify the feasibility of transferring development methods across domains. The approach evaluates transfer proposals using an overall feasibility score and specific criteria such as cost, quality, and time. The Project Management Triangle was selected as the basis for this decision tool because its constraints can be quantified. The model produces a score on a scale from 0 to 10, based on two elements: the impact on automotive development and the relevance for automotive practices. Relevance was determined through a survey of 126 engineers from a commercial vehicle manufacturer, focusing on preferences regarding methods, processes, and tools. Survey results were used to weight the three constraints, improving forecast accuracy for the automotive domain. Model validation employed a previous study on common cause analysis for redundant chassis control systems, where aviation best practices were adapted for automotive. Engineers who applied gave their observed score. The forecasted feasibility score of 6.758 was compared with an observed score of 5.833, yielding an accuracy of 14%, which classifies the model as good. The proposed method offers three contributions: it provides a quantified feasibility assessment for cross-domain transfers, combines qualitative and quantitative indicators, and customizes weighting based on target group relevance. Limitations include reliance on data from a single manufacturer and validation based on one-method transfer. Overall, the approach demonstrates potential as a decision-support tool for reducing risks in cross-domain technology adoption. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315924 Deep Reinforcement Learning for Navigation and Collision Avoidance of Multi-Robot Systems By Constructive Network Expansion Lin, Rong-Yuan; Huang, Chu-Wei; Yeh, T.-J. Abstract. This article presents a navigation and obstacle avoidance policy network for multi-robot systems using deep reinforcement learning. The network is first designed and trained for a dual-robot setup. By incorporating nonholonomic constraints and priority rules, reinforcement learning is used to train the network with respect to the kinematics of mobile robots, enabling effective navigation and collision avoidance. An innovative expansion architecture is introduced, leveraging the social-force model to extend the dual-robot policy to multi-robot scenarios with moderate computational cost. Although the network is trained in an open environment, it can be applied to general map environments by using virtual robots to simulate walls and compartments. Simulations and indoor experiments validate the feasibility and performance of the proposed multi-robot navigation and obstacle avoidance policy. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315922 Model Predictive Control-Based Optimal Energy Management of Autonomous Electric Vehicles Under Cold Temperatures Padisala, Shanthan K.; Dey, Satadru Abstract. In autonomous electric vehicles (AEVs), battery energy must be judiciously allocated to satisfy primary propulsion demands and secondary auxiliary demands, particularly the heating, ventilation, and air conditioning (HVAC) system. This becomes especially critical when the battery is in a low state of charge under cold ambient conditions, and cabin heating and battery preconditioning (prior to actual charging) can consume a significant percentage of available energy, directly impacting the driving range. In such cases, one usually prioritizes propulsion or applies heuristic rules for thermal management, often resulting in suboptimal energy utilization. There is a pressing need for a principled approach that can dynamically allocate battery power in a way that balances thermal comfort, battery health, and preconditioning, along with range preservation. This article attempts to address this issue using model predictive control to optimize the power consumption between the propulsion, HVAC, and battery temperature preparation so that it can be charged immediately once the destination is reached. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315905 Wildfire Tracking by Fixed-Wing Unmanned Aerial Vehicles Using Receding-Horizon Guidance Patnaik, Karishma; Ratnoo, Ashwini Abstract. Unmanned aerial vehicles (UAVs) can be used to track growing and moving boundaries such as those of wildfires where the boundary cannot be pre-specified. Toward this, we first present a model predictive control (MPC) formulation for this task, which systematically incorporates vehicle dynamics, evolving boundary models, and input constraints to enable precise tracking. While effective, solving the nonlinear optimization online incurs high computational cost, limiting real-time deployment. To address this, we propose a novel receding-horizon guidance law that replaces the optimization step with a closed-form solution based on steady-turn motion primitives embedded in a receding-horizon framework. This approach generates circular-arc trajectories in lieu of the computationally expensive optimization routine, while preserving the predictive nature of the formulation and enabling real-time onboard implementation. Simulation studies validate the method across varying UAV initial conditions, prediction horizons, and fire model parameters, demonstrating that it achieves tracking performance comparable to MPC while reducing computation time by several orders of magnitude. 2026-01-01T00:00:00Z