Accurate Trajectory Following for Automated Vehicles in Dynamic Environments

In this paper, a hierarchical framework for task assignment and path planning of multiple unmanned aerial vehicles (UAVs) in a dynamic environment is presented. For multi-agent scenarios in dynamic environments, a candidate algorithm should be able to replan for a new path to perform the updated tasks without any collision with obstacles or other agents during the mission. In this paper, we propose an intersection-based algorithm for path generation and a negotiation-based algorithm for task assignment since these algorithms are able to generate admissible paths at a smaller computing cost. The path planning algorithm is also augmented with a potential field-based trajectory replanner, which solves for a detouring trajectory around other agents or pop-up obstacles. For validation, test scenarios for multiple UAVs to perform cooperative missions in dynamic environments are considered. The proposed algorithms are implemented on a fixed-wing UAVs testbed in outdoor environment and showed satisfactory performance to accomplish the mission in the presence of static and pop-up obstacles and other agents.

The evaluation of collision avoidance strategies for special-purpose unmanned ground vehicles (UGVs) operating in dynamic environments with human presence is presented in this study. The autonomous wheeled vehicle equipped with an armament module for combat and reconnaissance tasks is described considering mission specifications and off-road usage scenarios. The main operational parameters are outlined, and the obstacle detection features integrated into the PERUN vehicle are introduced. The control system architecture is presented, along with installed sensors and their perception capabilities of surrounding object detection. Safety considerations in operational conditions where human presence force appropriate collision avoidance actions are discussed. Real field tests conducted in diverse weather and environmental conditions, such as industrial area and unstructured terrains, are shown to demonstrate the system's performance. Carried out tests show that the collision avoidance system enables obstacle detection and proving the effectiveness of steering strategies for the platform's navigation. The safe operation of unmanned systems in environments with human presence is crucial for the successful deployment and scalability of UGV technologies.

To generate safe and efficient trajectories for an automated vehicle in dynamic environments, a layered approach is usually considered, which separates path planning and speed profile planning. This paper is focused on speed profile planning for a given path that is represented by a set of waypoints. The speed profile will be generated using temporal optimization which optimizes the time stamps for all waypoints along the given path. The formulation of the problem under urban driving scenarios is discussed. To speed up the computation, the non-convex temporal optimization is approximated by a set of quadratic programs which are solved iteratively using the slack convex feasible set (SCFS) algorithm. The simulations in various urban driving scenarios validate the effectiveness of the method.

The Non-Linear Threshold Accepting (NLTA) algorithm has been successfully applied in electrical and electronic systems, particularly in optimizing power distribution and voltage regulation. However, its application in mechatronic systems remains largely unexplored. Given that most Unmanned Ground Vehicles (UGVs) and robotic systems used in logistics and industrial environments integrate electrical, electronic, and mechanical subsystems, an advanced adaptive control strategy is essential to ensure optimal performance in dynamic environment be it in engineering/manufacturing or last-mile delivery environments. Traditional Proportional-Integral-Derivative (PID) controllers, while widely adopted, can still be improve for real-time adaptability, leading to improved efficiency in trajectory control, response time, and energy consumption. These possibilities necessitate needs for a more robust control framework with better and improved capabilities of dynamically adjusting to operational uncertainties. In this research a comparative performance evaluation was conducted using Mathematical models and MATLAB/Simulink simulations, benchmarking the NLTA algorithm against conventional PID controllers and other heuristic optimization techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE). Unlike some of the traditional PID controllers that require manual tuning and often fail to adapt to non-linear system variations, NLTA employs an adaptive threshold mechanism to iteratively optimize control gains, ensuring improved operational stability, energy efficiency, and trajectory accuracy. The NLTA algorithm’s ability to better self-adjust in real time provides a significant advantage over existing control methods, making it a better and more viable alternative for enhancing the performance and reliability of mechatronics or other systems in need of optimization. A comparative analysis was conducted using MATLAB/Simulink simulations to benchmark NLTA against conventional PID controllers and other heuristic-based optimization methods, including Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE). The results demonstrate that NLTA outperforms traditional control strategies by achieving faster response times, reduced settling time, enhanced robustness against environmental disturbances, and improved overall system efficiency. While the NLTA model also holds promise for warehouse layout optimization where dynamic reconfiguration could enhance operational efficiency this research focuses solely on evaluating its effectiveness as a control strategy for mechatronic systems compared to existing PID-based approaches. The findings reinforce the potential of NLTA as an advanced control framework for system and operational optimization also, bridging the gap between electrical, electronic, and mechanical control integration. Future work will explore real-world deployment, AI-driven predictive modeling for enhanced adaptability, and the extension of NLTA’s capabilities to logistics facility layout optimization. By validating NLTA’s effectiveness against traditional PID controllers, this research is aimed to contribute to the ongoing evolution of intelligent control mechanisms in logistics engineering especially autonomous last-mile delivery systems.

In this paper, we propose the minimum energy control input based on Hamiltonian strategy for the motor-table system. The energy equation of the motor-table system contains of the input energy, dissipation energy, potential energy and output energy, and includes electrical and mechanical energies. The methods of the minimum control effort (MCE), minimum input energy control (MIEC), minimum dissipation energy control (MDEC) and trapezoidal trajectory energy control are compared for the motor-table system. From the numerical results, we can find that the MIEC and MDEC approaches can obtain the same minimum input energy. Traditionally, the MCE approach was regarded as the minimum energy control in the previous study. However, we have demonstrated that the MCE approach is not the minimum energy control in this paper.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase throughput, and optimize energy efficiency and emissions in complicated traffic scenarios. This paper presents a mixed-integer linear programming (MILP) method for scheduling and coordination of CAVs in a highly dynamic environment that consists of multiple human-driven vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a centralized high-level decision making problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. The performance and computational load of the proposed method are assessed in closed-loop simulations on an illustrative scenario. Despite MILP having combinatorial complexity, the proposed formulation appears feasible for real-time implementation, e.g., in mobile edge computers (MECs).

An adaptive Monte Carlo tree search (MCTS) framework is presented for long-range, energy-aware trajectory planning and online re-planning of fixed-wing uncrewed aerial vehicles in dynamic environments. Mission planning is posed as a finite-horizon Markov decision process with a four-degree-of-freedom kinematic and energy model, forecast wind fields, terrain, and time-varying no-fly zones. The MCTS planner searches over waypoint references generated by pre-stabilizing feedback controllers and a constraint-aware navigation field, yielding dynamically feasible trajectories that approximately satisfy altitude, obstacle, and airspace constraints. Simulations for a coastal medical delivery mission show that, relative to a Batch Informed Trees baseline, the method trades modest path-length increases for reduced energy consumption and, under emerging storm-front no-fly regions, rapidly computes safe diversion trajectories that preserve most nominal energy performance under fixed computational budgets.

The original article to which this Erratum refers was published in IEEJ Transactions on Electrical and Electronic Engineering Vol.4, No. 4, 2009, pp.561‐569.

Efficient trajectory planning for intelligent vehicles in dynamic environments is a non-trivial task due to the diversity and complexity of driving scenarios. It requires the planner to be capable of responding to the changes in driving scenarios in real-time. This paper proposes a hybrid trajectory planning framework by combining the sampling- and numerical optimization-based approaches to cope with the complex driving scenarios. First, a risk field model is introduced to assess the risks with the static and moving obstacles. Then, the sampling-based approach is used to generate collision-free trajectory candidates via the Path Velocity Decomposition method. Thus, the optimal behavior trajectory can be obtained by considering curve smoothness, collision risk, and travel time. The optimization-based method is adopted to optimize the behavior trajectory to guarantee safety, vehicle dynamics stability, and driving comfort using the Sequential Quadratic Programming within the spatio-temporal boundaries. Finally, the proposed framework is examined in typical dynamic driving scenarios through simulation, and the results verify its competency in generating high-quality trajectories in real-time.

In complex urban road conditions, stationary or illegally parked vehicles present a considerable risk to the overall traffic system. In safety-critical applications like autonomous driving, the detection of stopped vehicles is of utmost importance. Previous methods for detecting stopped vehicles have been designed for stationary viewpoints, such as security cameras, which consistently monitor fixed locations. However, these methods for detecting stopped vehicles based on stationary views cannot address blind spots and are not applicable from driving vehicles. To address these limitations, we propose a novel deep learning-based framework for detecting stopped vehicles in dynamic environments, particularly those recorded by dashcams. The proposed framework integrates a deep learning-based object detector and tracker, along with movement estimation using the dense optical flow method. We also introduced additional centerline detection and inter-vehicle distance measurement. The experimental results demonstrate that the proposed framework can effectively identify stopped vehicles under real-world road conditions.

Formation flight holds significant potential for various applications involving aerial robot swarms. However, current methodologies lack the capability to autonomously execute large-scale formation flights in densely populated environments. To bridge the gap, a decentralized and asynchronous formation flight planner is proposed based on a graph-based formation metric and tight representations of kinodynamically feasible trajectories for collision avoidance. The planner handles formation path planning along with dynamic obstacles and intervehicle collision avoidance using minimum volume convex hulls for agent trajectories. The employed formation metric is invariant to rotation, translation, and scaling, granting greater flexibility in formation coordination. A decoupled and distributed trajectory optimization framework is proposed to enhance the computational feasibility of large-scale formation flights. Moreover, to mitigate issues relating to communication delays between agents, asynchronous execution of a finite horizon navigation framework with usage of sparse trajectory control points for trajectory segments is employed. Simulations with multiple agents, static and dynamic obstacles support the robustness of the planner to formation flights in real world. The planner demonstrates goal/waypoints achievement and formation adherence capabilities that are assessed and compared using a popular quantifiable formation similarity metric. Furthermore, the paper also serves as a guideline to build upon trajectory planning frameworks for tight formation control in cluttered, dynamic environments.

Current vehicle trajectory prediction methods often fail to capture the complex spatial and temporal interactions among surrounding vehicles in dynamic environments, leading to suboptimal prediction accuracy. In response to these challenges, we propose a novel Transformer-based model for vehicle trajectory prediction, denoted as the Dynamic Spatial-Temporal Interaction-aware Transformer Network (DSTIFormer). The three main parts of the DSTIFormer architecture are a spatial-temporal embedding module, a spatial-temporal dynamics interaction-aware module, and a trajectory encoderdecoder module. The spatial-temporal embedding module aims to encode vehicular trajectory interactions into latent representations that simultaneously preserve spatial dependencies and temporal features. The second module utilizes a temporal transformer network (TTN) along with a spatial graph transformer network (STN) to capture the dynamic spatial-temporal interactions between vehicles. To make trajectory predictions more accurate, the third module uses a sequence-to-sequence (Seq2Seq) architecture as the encoder and an attention-based decoder layer to effectively extract the long-range sequential dependencies in the process. The model's prediction accuracy with the root mean squared error (RMSE) metric on two publicly available datasets: NGSIM and HighD, and the results demonstrate that our proposed model outperforms several benchmark methods in long-term prediction.

This paper focuses on D2D communication for 5G/6G in dynamic environments where the D2D network topology changes through time due to D2D Devices’ mobility. Specifically, we consider a scenario within the coverage area of a Base Station (BS) serving a number of User Devices (UEs), with variations in speed and direction causing changes in the D2D network topology, either via direct connections or via D2D single-hop or D2D Multi-hop paths. Based on this scenario, we formulate a problem aiming to maximise the total Spectral Efficiency (SE) whilst minimising the total Power Consumption (PC), by selecting the best transmission mode that the D2D devices will operate. In order to address the aforementioned problem, the Distributed Artificial Intelligence Solution (DAIS) plan proposed in our earlier work and designed for static environments is extended to consider the mobility (i.e., speed, direction and indirectly link distance change) of the D2D Device, targeting the dynamic creation of stable and efficient clusters and good backhauling links towards the gateway. The enhanced DAIS performance is comparatively evaluated in terms of SE and PC against selected variations in UE speed and direction, changes in the link Transmission Power (TP), and an increase in the number of Devices in the D2D network. Overall, the results obtained demonstrated superior performance of enhanced DAIS over all the other related investigated approaches (i.e., Distributed Sum Rate (DSR), Single Hop Relay Approach (SHRA), Distributed Random (DR)), in terms of Spectral Efficiency (SE) and Power Consumption (PC). Also, the ability of enhanced DAIS to react and adapt quickly and efficiently to D2D network topology changes, with reduced signalling overhead and control delay in responding to changes, shows that it is a well-poised approach to be used in a Dynamic D2D Environment.

A prediction mechanism for Memetic Algorithm is presented in this paper. The Predictive Memetic Algorithm (PMA) uses a nonlinear regression method to estimate the parameters used by the algorithm to obtain good solutions in a dynamic and stochastic environment. The algorithm is applied to nonlinear data sets and performance is compared with genetic and simulated annealing algorithms. When compared with the existing methods, the proposed method generates a relatively small error difference after prediction thereby proving its superior performance. A dynamic stochastic environment is used for experimentation, so as to determine the efficacy of the algorithm on non-stationary problem environments.

Normal Distribution Transform (NDT) algorithm plays the role of detecting the relative pose of the vehicle in high-precision map. In this paper, a method of setting downsampling factor and grid factor for NDT relocation algorithm in dynamic environment is proposed, which can solve the problems of excessive NDT relocation error and location loss caused by dynamic objects accounting for 1 % to 35 % of the volume of scanning point cloud in vehicle environment. To simulate a real dynamic point cloud environment, the single-frame LiDAR point cloud space is voxelized into a mesh. Each grid is assigned a random number evenly distributed between 0 and 1. The threshold value for whether to add a Gaussian noise point is also set. Seven representative dynamic objects on the highway are selected. The threshold value of probability distribution function of Gaussian noise object needs to be set. Then the volume content of the dynamic object in the single frame point cloud space is calculated according to the set threshold value by using the definite integral. By changing the content and volume of dynamic obstacles in a dynamic environment, the effects of the downsampling factor and the grid factor on the accuracy of the repositioning trajectory are obtained. The resampling and mesh coefficients are optimized based on the analysis of the repositioning trajectory accuracy. The results show that When the current sampling factor is fixed, the grid factor of the NDT algorithm is proportional to the RMSE factor. When the NDT grid factor is fixed and the down sampling factor is equal to the side length of the obstacle, the NDT relocation accuracy is the highest and reaches the local optimum.