Browse Topic: Steering systems
Robot Arm Tracking Control refers to the control of robot end effectors following a prescribed trajectory as their movement in robotic systems. The work presents a combination of Kalman Filter Based Dynamic System Tracking with Reinforcement Learning Based Trajectory Planning. These two aspects of tracking and planning help the robotic manipulator dynamically track a target that is located on an arbitrary moving path. In particular, by using Kalman filtering to estimate the position of a moving target and to compensate for sensor noise and sparse sampling, we take high-precision estimation values of each point’s coordinates along the target trajectory as a reliable basis to build a policy network using reinforcement learning. Based on it, the robot manipulator could produce effective motion planning under its own dynamic capabilities and physical constraint limit. Comprehensive simulation results illustrate advantages of the new algorithm against the classical control method, confirm
In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is
Precision control in Level 4 Automated Vehicles is essential for enhancing operational efficiency, accuracy, and safety. This work, conducted as part of ARPA-E’s NEXTCAR program, focuses on developing a robust hardware and software control solution to enable drive-by-wire functionality. A previous publication by the authors presented the hardware solutions for overtaking stock vehicle controls. This paper focuses on a model-based and data-driven control algorithm to enable drive-by-wire functionality for longitudinal and lateral motion control for a 2021 Honda Clarity Plug-In Hybrid Electric Vehicle. This vehicle was equipped with a set of sensors and an onboard processing unit to enable Level 4 automation. For lateral controls, an algorithm was developed to command steering torque to the electronic power steering module, ensuring the vehicle could attain the desired steering angle position at varying speeds. The system leveraged feedforward and feedback mechanisms. Feedback controller
In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes’ intersections, and it shows improved route performance in vehicle testing. The proposed model is validated in a Comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test results
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents a novel approach to curvature prediction using deep neural networks trained on GPS-derived ground truth data, rather than model predictions, providing a more accurate training signal that reflects actual vehicle motion. We develop a multi-modal neural network architecture with temporal GRU encoders that processes vision features, driver intent signals, historical curvature, and vehicle state parameters to predict curvature. A key innovation is the use of GPS-based actual curvature measurements computed from vehicle motion data (κ = ωz/v) as training supervision, enabling the model to learn from real-world driving patterns. The model is trained on 5,322 samples from real-world driving data collected on The University of Oklahoma’s Norman Campus using a Comma 3X device and a 2025 Nissan Leaf electric vehicle. Experimental results
The Nissan Sentra has provided straightforward behavior and performance for sedan shoppers in the U.S. for over forty years. For 2026, Nissan took the solid 2025 model and made enough mechanical tweaks and visual changes to call it an all-new vehicle. This might sound like a bit of a stretch, but given how the advancements add up to an improved drive experience in a better-looking vehicle, we'll let it slide. Available in four grades - S, SV, SR and SL that range from $22,400 to $27,990, before destination fees and packages - the 2026 Sentra puts on airs like it's a simple vehicle, hiding some of its advanced technology to keep the interior clean and clear, from the driver's screen to the steering wheel buttons. Wireless device charging and wireless Apple CarPlay/Android Auto minimize wire clutter. The standard 12.3-in NissanConnect infotainment touchscreen hides its options in a selection of tiles, and it has a single round volume button that makes it easy to turn down quickly.
This SAE Recommended Practice describes a laboratory test procedure and requirements for evaluating the characteristics of heavy-truck steering control systems under simulated driver impact conditions, as well as driver entry/egress conditions. The test procedure employs a torso-shaped body block that is impacted against the steering wheel.
Nowadays, customers expect excellent cabin insulation and superior ride comfort in electric vehicles. OEMs focus on fine tuning the suspension system in electric vehicle to isolate the road induced shocks which finally offers superior ride quality. This paper focuses on enhancing the ride comfort by reducing the road excitation which originates mainly due to road inputs. Higher steering wheel vibration is perceived on the test vehicle on rough road surfaces. To determine the predominant force transfer path, Multi reference Transfer Path Analysis (MTPA) is performed on the front and rear suspension. Based on the finding from MTPA, various recommendations are explored and the effect of each modification is discussed. Apart from this, Operational Deflection Shape (ODS) analysis is used to determine the deflection shape on the entire steering system . Based on ODS findings, recommendations like dynamic stiffness improvements on the steering column and steering wheel are explored and the
Vehicles with a high center of gravity (CG) and moderate wheel track, like compact Sport Utility Vehicles (SUVs), have a relatively low Static Stability Factor (SSF) and thus are inherently less stable and more susceptible to rollover crashes. Moreover, to be more maneuverable in highly populated urban areas, a smaller Turning Circle Diameter (TCD) is necessary. Here, Variable Gear Ratio (VGR) steering systems have major benefits over traditional Constant Gear Ratio (CGR) systems in terms of enhancing both roll stability and agility. To adapt VGR steering systems to a particular vehicle dynamic, Full Vehicle (FV) and Driver-in-the-Loop (DIL) simulations are utilized. Using this method, exact calibration is possible according to realistic driving conditions so that the VGR steering C-factor curve is properly tuned for optimal handling in on-center, off-centre, and transitional areas of the Steering Wheel Angle (SWA). Primary performance measures—e.g., SWA gradients at different lateral
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