Potential Field Research Articles

Clinico-physiological correlates of Parkinson’s disease from multi-resolution basal ganglia recordings

Parkinson’s disease (PD) has been associated with pathological neural activity within the basal ganglia. Herein, we analyzed resting-state single-neuron and local field potential (LFP) activities from people with PD who underwent awake deep brain stimulation surgery of the subthalamic nucleus (STN; n = 125) or globus pallidus internus (GPi; n = 44), and correlated rate-based and oscillatory features with UPDRSIII off-medication subscores. Rate-based single-neuron features did not correlate with PD symptoms. STN single-neuron and LFP low-beta (12–21 Hz) power and burst dynamics showed modest correlations with bradykinesia and rigidity severity, while STN spiketrain theta (4–8 Hz) power correlated modestly with tremor severity. GPi low- and high-beta (21–30 Hz) power and burst dynamics correlated moderately with bradykinesia and axial symptom severity. These findings suggest that elevated single-neuron and LFP oscillations may be linked to symptoms, though modest correlations imply that the pathophysiology of PD may extend beyond resting-state beta oscillations.

Regulation of Kv1.2 redox-sensitive gating by the transmembrane lectin LMAN2.

Kv1.2 potassium channels influence excitability and action potential propagation in the nervous system. Unlike closely-related Kv1 channels, Kv1.2 exhibits highly variable voltage-dependence of gating, attributed to regulation by unidentified extrinsic factors. Variability of Kv1.2 gating is strongly influenced by the extracellular redox potential, and we demonstrate that Kv1.2 currents in dorsal root ganglion sensory neurons exhibit similar variability and redox sensitivity as observed when the channel is heterologously expressed in cell lines. We used a functional screening approach to test the effects of candidate regulatory proteins on Kv1.2 gating, using patch clamp electrophysiology. Among 52 candidate genes tested, we observed that co-expression with the transmembrane lectin LMAN2 led to a pronounced gating shift of Kv1.2 activation to depolarized voltages in CHO and L(tk-) cell lines, accompanied by deceleration of activation kinetics. Overexpression of LMAN2 promoted a slow gating mode of Kv1.2 that mimics the functional outcomes of extracellular reducing conditions, and enhanced sensitivity to extracellular reducing agents. In contrast, shRNA-mediated knockdown of endogenous LMAN2 in cell lines reduced Kv1.2 redox sensitivity and gating variability. Kv1.2 sensitivity to LMAN2 is abolished by mutation of neighboring residues F251 and T252 in the intracellular S2-S3 linker, and these also abolish redox-dependent gating changes, suggesting that LMAN2 influences the same pathway as redox for Kv1.2 modulation. In conclusion, we identified LMAN2 as a candidate regulatory protein that influences redox-dependent modulation of Kv1.2, and clarified the structural elements of the channel that are required for sensitivity.

Towards Autonomous Operation of UAVs Using Data-Driven Target Tracking and Dynamic, Distributed Path Planning Methods

A hybrid real-time path planning method has been developed that employs data-driven target UAV trajectory tracking methods. It aims to autonomously manage the distributed operation of multiple UAVs in dynamically changing environments. The target tracking methods include a Gaussian mixture model, a long short-term memory network, and extended Kalman filters with pre-specified motion models. Real-time vehicle-to-vehicle communication is assumed through a cloud-based system, enabling virtual, dynamic local networks to facilitate the high demand of vehicles in airspace. The method generates optimal paths by adaptively employing the dynamic A* algorithm and the artificial potential field method, with minimum snap trajectory smoothing to enhance path trackability during real flights. For validation, software-in-the-loop testing is performed in a dynamic environment composed of multiple quadrotors. The results demonstrate the framework’s ability to generate real-time, collision-free flight paths at low computational costs.

Transient Increases in Neural Oscillations and Motor Deficits in a Mouse Model of Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms like tremors and bradykinesia. PD's pathology involves the aggregation of α-synuclein and loss of dopaminergic neurons, leading to altered neural oscillations in the cortico-basal ganglia-thalamic network. Despite extensive research, the relationship between the motor symptoms of PD and transient changes in brain oscillations before and after motor tasks in different brain regions remain unclear. This study aimed to investigate neural oscillations in both healthy and PD model mice using local field potential (LFP) recordings from multiple brain regions during rest and locomotion. The histological evaluation confirmed the significant dopaminergic neuron loss in the injection side in 6-OHDA lesioned mice. Behavioral tests showed motor deficits in these mice, including impaired coordination and increased forelimb asymmetry. The LFP analysis revealed increased delta, theta, alpha, beta, and gamma band activity in 6-OHDA lesioned mice during movement, with significant increases in multiple brain regions, including the primary motor cortex (M1), caudate-putamen (CPu), subthalamic nucleus (STN), substantia nigra pars compacta (SNc), and pedunculopontine nucleus (PPN). Taken together, these results show that the motor symptoms of PD are accompanied by significant transient increases in brain oscillations, especially in the gamma band. This study provides potential biomarkers for early diagnosis and therapeutic evaluation by elucidating the relationship between specific neural oscillations and motor deficits in PD.

Car-Following Model Based on Artificial Potential Field with Consideration of Horizontal Curvature in Connected Vehicles Environment

Connected vehicles (CVs) will gradually replace traditional vehicles to become the main components of traffic flow. Studying the car-following behavior characteristics is crucial for improving traffic flow stability and safety in CVs environment. Additionally, the radius of road curvature significantly impacts vehicle driving behavior, making it necessary to consider it for the car-following models of CVs. The artificial potential field (APF) theory can more accurately and comprehensively depict various microscopic driving behaviors, offering a new approach for modeling vehicle microscopic behavior. Firstly, this paper constructs the attractive and repulsive potential fields considering horizontal curve curvature based on a road coordinate transformation model. Secondly, an Artificial Potential Field-Based Car-Following Model Considering Curvature (APFCCM) in connected vehicles environment is proposed. Finally, the model is calibrated and validated using the Hangzhou - Xifu Freeway dataset from the Tongji Road Trajectory Sharing (TJRD TS) platform, and compared with the full velocity difference model(FVDM), the Intelligent Driver Model (IDM) and the Driving Risk Potential Field Model (DRPFM). The results show that the APFCCM performs well in trajectory simulation, model accuracy, and scenario adaptability, and it has the lowest mean absolute error(MAE) and root mean square error(RMSE) in position, speed, and acceleration metrics.

Adaptive Closed-Loop Modulation of Cortical Theta Oscillations: Insights into the Neural Dynamics of Navigational Decision-Making

Navigational decision-making tasks, such as spatial working memory (SWM), rely highly on information integration from several cortical and sub-cortical regions. Performance in SWM tasks is associated with theta rhythm, including low-frequency oscillations related to movement and memory. The interaction of the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC), reflected in theta synchrony, is essential in various steps of information processing during SWM. We used a closed-loop neurofeedback (CLNF) system to upregulate theta power in the mPFC and investigate its effects on circuit dynamics and behavior in animal models. Specifically, we hypothesized that enhancing the power of the theta rhythm in the mPFC might improve SWM performance. Animals were divided into three groups: closed-loop (CL), random-loop (RL), and OFF (without stimulation). We recorded local field potential (LFP) in the mPFC while electrical reward stimulation contingent on cortical theta activity was delivered to the lateral hypothalamus (LH), which is considered one of the central reward-associated regions. We also recorded LFP in the vHPC to evaluate the related subcortical neural changes. Results revealed a sustained increase in the theta power in both mPFC and vHPC for the CL group. Our analysis also revealed an increase in mPFC-vHPC synchronization in the theta range over the stimulation sessions in the CL group, as measured by coherence and cross-correlation in the theta frequency band. The reinforcement of this circuit improved spatial decision-making performance in the subsequent behavioral results. Our findings provide direct evidence of the relationship between specific theta upregulation and SWM performance and suggest that theta oscillations are integral to cognitive processes. Overall, this study highlights the potential of adaptive CLNF systems in investigating neural dynamics in various brain circuits.

A reason why to use the Gaussian-type-orbital is not suitable for the relativistic calculation of the nuclear-magnetic-resonance spectra with using the restricted magnetic balance

Numerical calculations are performed for a physical quantity which is appearing in the Hamiltonian of the matrix Dirac equation with containing the vector potential of the magnetic field due to the nuclear spin and with considering the restricted magnetic balance. This quantity is necessary for the relativistic calculation of the nuclear-magnetic-resonance spectra. It is shown that to use Gaussian-type orbital is not suitable for the calculation of this quantity.

Dunkl Algebra and Vacuum Pair Creation: Exact Analytical Results via Bogoliubov Method

This paper examines the generation of scalar particle pairs resulting from the application of an external electric field's Coulomb potential in both three- and two-dimensional scenarios, employing Dunkl's formalism. In doing this, we present two distinct sets of exact solutions to the Dunkl-Klein-Gordon equation. Subsequently, we employ the canonical approach based on the Bogoliubov transformation to link the “in” and “out” states in order to compute both the probability of pair creation and the density of particle creation. It is shown that the conditions necessary for the creation of pairs of spinless particles depend on several factors: nucleus charge Z, quantum numbers, and Dunkl parameter. It is also shown that the Dunkl formalism enhances particle pair production.

Design and Investigation of Electrostatic Doped Heterostructure Vertical Si(1-x)Gex/Si Nanotube TFET

Researchers are inclining toward heterostructures suitable lattice matching, in Tunnel FETs to eliminate the difficulties of decreased On-Current, subthreshold swings, and ambipolar behavior. Nowadays, Electrostatic doping (ED) is a scrutinized substitute device for creating areas with a high electron or hole density to the conventional doped devices. This manuscript proposes an Electrostatic Doped Heterostructure Vertical Si(1-x)Gex Nanotube Tunnel Field Effect with performance scanned by analyzing the different device parameters, considering the energy band diagram, concentrations of electrons, holes, potential, and electric field. In comparison to the Nanowire TFETs, the area, and rate of tunneling of the proposed device stand superior with a better ION/IOFF ratio of 1.56*1013 and a lower OFF-current of about ∼10-18A/μm. The device exhibits a Drain current (IDS) of 2.39*105A/μm. The architecture of the suggested device possessing Si(1-x)Gex/Si structure exhibits enhanced characteristics like improved steepness of the sub-threshold slope, Ion/Ioff ratio, drain current, and lowered OFF-current.

A study of nonglaucomatous optic neuropathies in a teaching hospital in Western Maharashtra

Background: Nonglaucomatous optic neuropathy (NGON) is defined as clinical evidence of optic nerve impairment without glaucomatous field/disc changes. Etiology is diverse and elusive in many patients. Causes of NGON include traumatic, compressive, inflammatory, demyelinating, toxic, nutritional, hereditary, vascular, infiltrative, and idiopathic. NGON disorders can be misdiagnosed as glaucoma and can be preventable or life-threatening. In some, fellow eye involvement is common. This can lead to inadequate management and influence prognosis which can be prevented by multispecialty approach. Physicians and ophthalmologists must be vigilant against threatening NGON forms. It is essential to track burden as most literature focused on particular NGON subtype. Aim: The aim was to study the clinical presentation of NGON in a teaching hospital. Setting and Design: This was a prospective, hospital-based, cross-sectional, observational study. Materials and Methods: A total of 86 eyes of 61 NGON patients were studied from January 2021 to June 2022 in a tertiary care hospital. Detailed history and comprehensive ophthalmic examination were performed in ambulatory patients. Nonambulatory patients were examined using torchlight. Ancillary investigations were performed, to confirm the clinical diagnosis wherever applicable. Statistical Analysis: Microsoft Excel was used for statistical analysis. Results: The mean age of study patients was 44.87 ± 16.05 years with a male: female ratio of 2.8:1. Unilateral eye involvement was seen in 36 (59.02%). Best-corrected visual acuity = 3/60–1/60 was noted in 41 (47.67%) patients. Red-green defect and contrast sensitivity were reduced by 59.30% and 66.28%, respectively. Relative afferent pupillary defect (RAPD) was noted in 52.46% of patients with Grade 5 RAPD in 40.63%. The most common visually evoked potential type and visual field defect noted were axonal and central scotoma (n = 31 [36.05%] and n = 26 [30.23%]), respectively. Traumatic optic neuropathy was diagnosed in 15 (24.59%) followed by inflammatory optic neuritis in 11 (18.04%) patients. Conclusion: Diagnosing cause of NGON in clinical practice is challenging, but emerging burden cannot be neglected. Knowledge of various NGON types helps narrow differential diagnoses and execute efficient, targeted workup. Timely recognition alters visual, neurological prognosis and saves the lives of patients sometimes.

Energy spectrum and magnetic susceptibility of the improved Pöschl-Teller potential

This study presents an analytical representation of the internal motion of a diatomic system using the improved Pöschl-Teller potential. The ro-vibrational energy spectrum is derived by solving the radial Schrödinger time independent wave equation (STIWE) under a 2D electromagnetic potential field. Additionally, an explicit equation for magnetic susceptibility is formulated from the internal partition function. The analytical models are used to investigate physical properties of CO (X1∑+), K2 (a3∑u+), BCl (X1∑+), and NaK (c3∑+) molecules. Average percentage absolute deviations (APAD) for the ro-vibrational energy spectrum are calculated as 0.1001 %, 0.5306 %, 1.2070 %, and 0.9779 %, respectively, compared to observed data. Numerical results are consistent with existing literature. Furthermore, the study reveals that the magnetic susceptibility of the system increases with temperature and decreases with an increase in magnetic field intensity, indicating its paramagnetic nature.

Autonomous Robot Navigation Using Adaptive Potential Field

This work presents a novel approach to mobile robot autonomous navigation, utilizing enhanced artificial potential fields in conjunction with path planning,obstacle avoidance and SLAM . In traditional adaptive potential fields, which integrate sensor data to enable robots to navigate safely and efficiently through various environments. The robot's perception of its surroundings, obtained through sensors like cameras, ultrasonic devices, is processed to create a dynamic potential field that reflects the spatial distribution of obstacles, goals, and other relevant features.We propose the design and implementation of an autonomous robot-based real-time vision-based system for detecting and tracking features in a structured environment ensuring robust obstacle avoidance and path planning. Keywords- Adaptive Potential Fields, Autonomous Navigation, Autonomous Systems, Machine Learning in Robotics, Motor driver, Obstacle Avoidance, Path Planning, Raspberry Pi, Real-time Adaptation, ROS (Robot Operating System), Sensor-based Navigation, Servo Motor, SLAM (Simultaneous Localization and Mapping),Ultrasonic Sensors

Inhibition of the P38 MAPK/NLRP3 Pathway Mitigates Cognitive Dysfunction and Mood Alterations in Aged Mice after Abdominal Surgery Plus Sevoflurane

BackgroundCognitive dysfunction, encompassing perioperative psychological distress and cognitive impairment, is a prevalent postoperative complication within the elderly population, and in severe cases, it may lead to dementia. Building upon our prior research that unveiled a connection between postoperative mood fluctuations and cognitive dysfunction with the phosphorylation of P38, this present investigation aims to delve deeper into the involvement of the P38 MAPK/NLRP3 pathway in perioperative neurocognitive disorders (PND) in an abdominal exploratory laparotomy (AEL) aged mice model. MethodsC57BL/6 mice (male, 18-month-old) underwent AEL with 3% anesthesia. Then, inhibitors targeting P38 MAPK (SB202190, 1mg/kg) and GSK3β (TWS119, 10mg/kg) were administered multiple times daily for 7 days post-surgery. The NLRP3-cKO AEL and WT AEL groups only underwent the AEL procedure. Behavioral assessments, including the open field test (OFT), novel object recognition (NOR), force swimming test (FST), and fear conditioning (FC), were initiated on postoperative day 14. Additionally, mice designated for neuroelectrophysiological monitoring had electrodes implanted on day 14 before surgery and underwent novel object recognition while their local field potential (LFP) was concurrently recorded on postoperative day 14. Lastly, after they were euthanasized, pathological analysis and western blot were performed. ResultsSB202190, TWS119, and astrocyte-conditional knockout NLRP3 all ameliorated the cognitive impairment behaviors induced by AEL in mice and increased mean theta power during novel location exploration. However, it is worth noting that SB202190 may exacerbate postoperative depressive and anxiety-like behaviors in mice, while TWS119 may induce impulsive behaviors. ConclusionsOur study suggests that anesthesia and surgical procedures induce alterations in mood and cognition, which may be intricately linked to the P38 MAPK/NLRP3 pathway.

Surrogate based design space exploration and exploitation for an efficient airfoil optimization under uncertainties using transition models

The pursuit of sustainable, zero-emission air travel is heavily dependent on the creation of energy-efficient aircraft. Key strategies for achieving this sustainability in aviation include reducing fuel consumption through low-drag designs harnessing laminar flow. However, designing aircraft with laminar flow characteristics is complex due to their sensitivity to environmental and operational factors. This study tackles the challenge of developing energy-efficient aircraft by using computational fluid dynamics models and sophisticated optimization techniques that account for uncertainty. Our approach demonstrates the effectiveness of surrogate-based optimization and uncertainty quantification in optimizing airfoil drag for a natural laminar airfoil (NLF) design. We use surrogate models, trained with data from detailed airfoil simulations, which include a boundary layer code coupled with a linear stability method and a newly developed transition transport model. Transition location predicted using transition models facilitate an accurate drag prediction used in the optimization process. The accuracy of these surrogate models is enhanced through active sampling strategies. Our robust optimization method considers uncertainties in environmental and operational conditions, offering a deeper insight into their effects on crucial design parameters. Unlike traditional deterministic aerodynamic design optimization, our findings highlight the efficacy and precision of uncertainty-based optimization in achieving robust NLF airfoil designs over large (exploration mode) and small (exploitation mode) design spaces. Investigating design space parameterization based on the size of design variables reveals significant differences in optimal airfoil configurations. The optimized designs we propose favor delayed transition, in contrast to deterministic designs which often result in significant loss of laminarity when facing uncertainties. This study represents a significant advancement in aerospace engineering, providing a practical and effective methodology for creating energy-efficient airfoil designs. The application of these advanced optimization and uncertainty quantification techniques shows great potential for the wider field of aerospace engineering, paving the way for more resilient and robust aircraft designs.

A deep residual reinforcement learning algorithm based on Soft Actor-Critic for autonomous navigation

The problem of autonomous navigation has attracted significant attention from robotics research community in the last few decades. In this paper, we address the problem of low data utilization due to the large amount of episode experience value data. A maximum entropy algorithm based on prioritized experience replay (Learning Good Experience based on Soft Actor-Criti, LGE-SAC) is proposed to quickly reproduce past good experience episodes. As the deep reinforcement learning method is susceptible to failure to plan ahead and explore the target position in a long sequence environment, a deep Residual Soft Actor-Critic (RSAC) is proposed to alleviate this problem. The reinforcement learning policy is fused with the Artificial Potential Field method to improve the generalization ability of the proposed algorithm, thus improving robot adaptation in new test environments. In order to validate the effectiveness of the proposed algorithm, we conducted simulation experiments in Gazebo simulator environment and real experiments on a Turtlebot3 robot equipped with LiDAR sensor. Simulation and experiment results show that the proposed algorithm effectively avoids obstacles and succeeds in reaching the goal compared to other obstacle avoidance algorithms. In comparison with the Artificial Potential Field method, the planning success rate of the proposed RSAC algorithm in the test environment is increased by 30%, and at the same time, the number of planning steps is reduced by half, and the generalization ability is improved.

Integrating deep reinforcement learning and improved artificial potential field method for safe path planning for mobile robots

Purpose This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods. Design/methodology/approach The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point. Findings Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots. Originality/value This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

Magnetic Eruption from a Three-ribbon Flare

We present observations and analysis of an eruptive M1.5 flare (SOL2014-08-01T18:13) in NOAA active region (AR) 12127, characterized by three flare ribbons, a confined filament between ribbons, and rotating sunspot motions as observed by the Solar Dynamics Observatory. The potential field extrapolation model shows a magnetic topology involving two intersecting quasi-separatrix layers (QSLs) forming a hyperbolic flux tube (HFT), which constitutes the fishbone structure for the three-ribbon flare. Two of the three ribbons show separation from each other, and the third ribbon is rather stationary at the QSL footpoints. The nonlinear force-free field extrapolation model implies the presence of a magnetic flux rope (MFR) structure between the two separating ribbons, which was unclear in the observation. This suggests that the standard reconnection scenario for eruptive flares applies to the two ribbons, and the QSL reconnection for the third ribbon. We find rotational flows around the sunspot, which may have caused the eruption by weakening the downward magnetic tension of the MFR. The confined filament is located in the region of relatively strong strapping field. The HFT topology and the accumulation of reconnected magnetic flux in the HFT may play a role in holding it from eruption. This eruption scenario differs from the one typically known for circular ribbon flares, which is mainly driven by a successful inside-out eruption of filaments. Our results demonstrate the diversity of solar magnetic eruption paths that arises from the complexity of the magnetic configuration.

Research, Analysis, and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments.

Two-dimensional problem for an infinite body of two coaxial cylinders under the action of a solenoidal body force in the theory of thermoelastic diffusion

This study investigates the dynamic response of an infinite structure comprising two coaxial cylinders employing the one-relaxation-time generalized thermoelastic diffusion model. The inner cylinder, serving as a cavity, possessing a radius a, while the outer cylinder has radius b. The inner cylinder is insulated and experiencing a localized thermal load with thickness 2 h. The outer cylinder, with radius b, is also experiencing to a localized thermal load with width 2 h, and its surface exhibits no traction. Additionally, the structure experiences the influence of a solenoidal body force. The analysis of this problem employs a combination of Laplace transform, inverse Laplace transform, and exponential Fourier transform techniques. Numerical analysis of the resulting solutions reveals the variations in temperature, displacement, radial stress, concentration, and potential fields as a function of the radial coordinate. Results indicate a minimal impact of the time interval on the temperature distribution, while displacement and radial stress exhibit significant time-dependent behavior. These findings highlight the significance of incorporating thermoelastic diffusion theory in the analysis of real-world engineering problems.

Anti-rollover Artificial Potential Field Motion Planning Method of an Autonomous Heavy Truck Optimised by Game Theory

Anti-rollover is a critical factor to consider when planning the motion of autonomous heavy trucks. This paper proposed a method for autonomous heavy trucks to generate a path that avoids collisions and minimizes rollover risk. The corresponding rollover index is deduced from a 5-DOF heavy truck dynamic model that includes longitudinal motion, lateral motion, yaw motion, sprung mass roll motion, unsprung mass roll motion, and an anti-rollover artificial potential field (APF) is proposed based on this. The motion planning method, which is based on model predictive control (MPC), combines trajectory tracking, anti-rollover APF, and the improved obstacle avoidance APF and considers the truck dynamics constraints, obstacle avoidance, and anti-rollover. Furthermore, by using game theory, the coefficients of the two APF functions are optimised, and an optimal path is planned. The effectiveness of the optimised motion planning method is demonstrated in a variety of scenarios. The results demonstrate that the optimised motion planning method can effectively and efficiently avoid collisions and prevent rollover.