Vehicles In Dynamic Environments Research Articles

Adaptive Energy Management Strategy for Hybrid Electric Vehicles in Dynamic Environments Based on Reinforcement Learning

Energy management strategies typically employ reinforcement learning algorithms in a static state. However, during vehicle operation, the environment is dynamic and laden with uncertainties and unforeseen disruptions. This study proposes an adaptive learning strategy in dynamic environments that adapts actions to changing circumstances, drawing on past experience to enhance future real-world learning. We developed a memory library for dynamic environments, employed Dirichlet clustering for driving conditions, and incorporated the expectation maximization algorithm for timely model updating to fully absorb prior knowledge. The agent swiftly adapts to the dynamic environment and converges quickly, improving hybrid electric vehicle fuel economy by 5–10% while maintaining the final state of charge (SOC). Our algorithm’s engine operating point fluctuates less, and the working state is compact compared with Deep Q-Network (DQN) and Deterministic Policy Gradient (DDPG) algorithms. This study provides a solution for vehicle agents in dynamic environmental conditions, enabling them to logically evaluate past experiences and carry out situationally appropriate actions.

An Empirical Approach to Rerouting Visible Light Pathways Using an Adjustable-Angle Mirror to Sustain Communication between Vehicles on Curvy Roads

In this paper, a novel method is demonstrated to sustain vehicle-to-vehicle (V2V) communication on curvy roads via the arrangement of the lateral position of a self-angle-adjustable mirror–reflective road sign (SAAMRS) and light-direction-sensing wide-angle complementary photodiodes (CPDs). Visible light communication (VLC) between vehicles attracts attention as a complementary technology to radio-frequency-based (RF-based) communication technologies due to its wide, license-free spectrum and immunity to interferences. However, V2V VLC may be interrupted on curvy roads due to the limited field of view (FOV) of the receiver or the line of sight (LOS) being interrupted. To solve this problem, an experiment was developed using an SAAMRS along with wide-angle light-direction-sensing CPDs that used a precise peak detection (PPD) method to sustain communication between vehicles in dynamic environments by rerouting the incident light with the highest signal intensity level to the receiver vehicle on curvy roads. We also used real images of curvy roads simulated as polynomials to calculate the necessary rotation angles for the SAAMRS and regions where communication exist. Our experimental results overlapped almost completely with our simulations, with small errors of approximately 4.8% and 4.4% for the SAAMRS angle and communication region, respectively.

Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach.

Automated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path planning for AGVs in complex and dynamic environments remains challenging due to the computation of obstacle avoidance and efficient transport. This study proposes a novel approach that combines multi-objective particle swarm optimization (MOPSO) and the dynamic-window approach (DWA) to enhance AGV path planning. Optimal AGV trajectories considering energy consumption, travel time, and collision avoidance were used to model the multi-objective functions for dealing with the outcome-feasible optimal solution. Empirical findings and results demonstrate the approach's effectiveness and efficiency, highlighting its potential for improving AGV navigation in real-world scenarios.

Decentralized Asynchronous Formation Planning of Multirotor Aerial Vehicles in Dynamic Environments Using Flexible Formation Graphs and Tight Trajectory Hulls

Decentralized Asynchronous Formation Planning of Multirotor Aerial Vehicles in Dynamic Environments Using Flexible Formation Graphs and Tight Trajectory Hulls

A Collision-Free Planning and Control Framework for a Biomimetic Underwater Vehicle in Dynamic Environments

A Collision-Free Planning and Control Framework for a Biomimetic Underwater Vehicle in Dynamic Environments

A Hybrid Trajectory Planning Strategy for Intelligent Vehicles in On-Road Dynamic Scenarios

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.

Sine Resistance Network-Based Motion Planning Approach for Autonomous Electric Vehicles in Dynamic Environments

This article proposes a motion planning approach for autonomous electric vehicles to generate an appropriate planned path according to the time-varying surrounding information. This approach utilizes the proposed novel sine resistance network to mesh the road with the aim of improving the planned path smoothness, which has the capability of generating a continuous-curvature planned path that contributes to tracking and reducing the jerkiness. Meanwhile, considering that the classical artificial potential field (APF) method is only suitable for the static scenarios, a bias oval APF is constructed to predict the change of relative distance between the ego vehicle and each obstacle by taking the speed information into account. The proposed planning approach can ensure that the planned path is collision-free in dynamic environments and the generated path is smooth simultaneously. Cosimulation results in CarSim and MATLAB/Simulink are provided to prove the advantage and feasibility of the proposed motion planning approach for autonomous electric vehicles.

Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC.

Autonomous Underwater Vehicle are widely used in industries, such as marine resource exploitation and fish farming, but they are often subject to a large amount of interference which cause poor control stability, while performing their tasks. A decoupling control algorithm is proposed and A single control volume–single attitude angle model is constructed for the problem of severe coupling in the control system of attitude of six degrees of freedom Autonomous Underwater Vehicle. Aiming at the problem of complex Active Disturbance Rejection Control (ADRC) adjustment relying on manual experience, the PSO-ADRC algorithm is proposed to realize the automatic adjustment of its parameters, which improves the anti-interference ability and control accuracy of Autonomous Underwater Vehicle in dynamic environment. The anti-interference ability and control accuracy of the method were verified through experiments.

Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments

Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle autonomy. However, covering an entire mission region is a challenge to HAUVs because of the possibility of a multidomain environment. This paper presents an informative trajectory planning framework for planning paths and generating trajectories for HAUVs performing multidomain missions in dynamic environments. We introduce the novel heuristic generalized extensive neighborhood search GLNS–k-means algorithm that uses k-means to cluster information into several sets; then through the heuristic GLNS algorithm, it searches the best path for visiting these points, subject to various constraints regarding path budgets and the motion capabilities of the HAUV. With this approach, the HAUV is capable of sampling and focusing on regions of interest. Our method provides a significantly more optimal trajectory (enabling collection of more information) than ant colony optimization (ACO) solutions. Moreover, we introduce an efficient online replanning scheme to adapt the trajectory according to the dynamic obstacles during the mission. The proposed replanning scheme based on KD tree enables significantly shorter computational times than the scapegoat tree methods.

Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments

A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other agents in dynamic environments can lead to risky or overconservative plans. In this work, we model the motion planning problem as a partially observable Markov decision process (POMDP) and propose an online system that combines an intent recognition algorithm and a POMDP solver to generate risk-bounded plans for the ego vehicle navigating with a number of dynamic agent vehicles. The intent recognition algorithm predicts the probabilistic hybrid motion states of each agent vehicle over a finite horizon using Bayesian filtering and a library of pre-learned maneuver motion models. We update the POMDP model with the intent recognition results in real time and solve it using a heuristic search algorithm which produces policies with upper-bound guarantees on the probability of near colliding with other dynamic agents. We demonstrate that our system is able to generate better motion plans in terms of efficiency and safety in a number of challenging environments including unprotected intersection left turns and lane changes as compared to the baseline methods.

Reactive Collision Avoidance for Nonholonomic Vehicles in Dynamic Environments with Obstacles of Arbitrary Shape

Abstract Over the recent years, there has been an increasing interest in autonomous systems. Consequently, the problem of avoiding static and dynamic obstacles without human interference has gained a lot of attention. Avoiding collision, even in completely static environments, is significantly more challenging when the vehicle is subject to nonholonomic constraints. This paper presents a reactive algorithm for collision avoidance of dynamic, arbitrarily shaped obstacles, which is suitable for unicycle-type, nonholonomic vehicles. Unlike most reactive methods, we consider the exact shape of the obstacle, which allows the vehicle to utilize any space that is not occupied by the obstacle. This is an advantage over circle and ellipse approximations, as they can lead to overly conservative maneuvers. We provide explicit conditions under which collision avoidance is mathematically proven and validate the analysis by numerical simulations.

On the Distributed Path Planning of Multiple Autonomous Vehicles Under Uncertainty Based on Model-Predictive Control and Convex Optimization

In this article, a fully distributed control structure named distributed linear time-varying model predictive controller is proposed for motion control of multiple vehicles in dynamic environments with uncertainty resources based on local communication and local controllers. Efficient operation of this structure in such real-world conditions needs reasoning about the future and the uncertainties over environmental objects; therefore, future evolution of vehicles and environmental objects is accounted as a planning strategy. A general planning framework is proposed, which integrates estimation, prediction, and planning. Simulation results for different dynamic scenarios demonstrate the benefit and applicability of this approach.

Path planning of multiple UAVs using MMACO and DE algorithm in dynamic environment

Cooperative path planning of multiple unmanned aerial vehicles is a complex task. The collision avoidance and coordination between multiple unmanned aerial vehicles is a global optimal issue. This research addresses the path planning of multi-colonies with multiple unmanned aerial vehicles in dynamic environment. To observe the model of whole scenario, we combine maximum–minimum ant colony optimization and differential evolution to make metaheuristic optimization algorithm. Our designed algorithm, controls the deficiencies of present classical ant colony optimization and maximum–minimum ant colony optimization, has the contradiction among the excessive information and global optimization. Moreover, in our proposed algorithm, maximum–minimum ant colony optimization is used to lemmatize the pheromone and only best ant of each colony is able to construct the path. However, the path escape by maximum–minimum ant colony optimization and it treated as the object for differential evolution constraints. Now, it is ensuring to find the best global colony, which provides optimal solution for the entire colony. Furthermore, the proposed approach has an ability to increase the robustness while preserving the global convergence speed. Finally, the simulation experiment results are performed under the rough dynamic environment containing some high peaks and mountains.

Synchronous Maneuver Searching and Trajectory Planning for Autonomous Vehicles in Dynamic Traffic Environments

In the real-time decision-making and local planning process of autonomous vehicles in dynamic environments, the autonomous driving system may fail to find a reasonable policy or even gets trapped in some situation due to the complexity of global tasks and the incompatibility between upper-level maneuver decisions with the low-level lower level trajectory planning. To solve this problem, this paper presents a synchronous maneuver searching and trajectory planning (SMSTP) algorithm based on the topological concept of homotopy. Firstly, a set of alternative maneuvers with boundary limits are enumerated on a multi-lane road. Instead of sampling numerous paths in the whole spatio-temporal space, we, for the first time, propose using Trajectory Profiles (TPs) to quickly construct the topological maneuvers represented by different routes, and put forward a corridor generation algorithm based on graph-search. The bounded corridor further constrains the maneuver's space in the spatial space. A step-wise heuristic optimization algorithm is then proposed to synchronously generate a feasible trajectory for each maneuver. To achieve real-time performance, we initialize the states to be optimized with the boundary constraints of maneuvers, and we set some heuristic states as terminal targets in the quadratic cost function. The solution of a feasible trajectory is always guaranteed only if a specific maneuver is given. The simulation and realistic driving-test experiments verified that the proposed SMSTP algorithm has a short computation time which is less than 37ms, and the experimental results showed the validity and effectiveness of the SMSTP algorithm.

SLAM based on Adaptive SVSF for Cooperative Unmanned Vehicles in Dynamic environment

Abstract Autonomous navigation in unknown environments has attracted a special attention from the robotic community over the last three decades. In order to gather accurate information about the environment, wheeled mobile robots are equipped with a variety of sensors (e.g. laser, vision, sonar, GPS, etc.), allowing accurate localization and reconstruction of reliable and consistent representations of the environment. When neither the location of the robot nor the map of its surrounding known, localization and mapping become highly interdependent tasks that must be performed concurrently. This problem is known as Simultaneous Localization and Mapping (SLAM). Many techniques have been developed to make the utmost of the robot sensors to solve the SLAM problem. A solution to SLAM in dynamic environments would expand robotic applications and open up a vast range of potential applications. In this context, we propose the adaptive smooth variable structure filter (ASVSF) based approach to solve the SLAM problem and reconstruct a reliable 2D environment representation. The proposed algorithm is validated in real-world and the obtained results confirm the efficiency and robustness of our approach in dynamic environment.

A 3D Rotating Laser-Based Navigation Solution for Micro Aerial Vehicles in Dynamic Environments

In this paper, we present a 3D rotating laser-based navigation framework for micro aerial vehicles (MAVs) to fly autonomously in dynamic environments. It consists of a 6-degree of freedom (DoF) localization module and a 3D dynamic mapping module. A self-designed rotating laser scanner generates dense point clouds in which 3D features are extracted and aligned. The localization module is able to solve scan distortion issue while estimating the 6-DoF pose of MAVs. At the same time, the dynamic mapping module can further eliminate dynamic trails so that a clear dense 3D map is reconstructed. The dynamic targets are detected based on the spatial constraints and therefore without the need of dense point cloud clustering. Through filtering the detected dynamic obstacles, the localization approach can be robust to the dynamic environment variations. To verify the robustness and effectiveness of our proposed framework, we have tested our system in both real indoor environment with dynamic obstacles and outdoor foliage condition using a customized MAV platform.

Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller

PurposeThe motion control of unmanned ground vehicles (UGV) is a challenge in the industry of automation. The purpose of this paper is to propose a fuzzy inference system (FIS) based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments.Design/methodology/approachThe representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system. If dynamic obstacles move randomly in the operation field, the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations. This paper considers the construction of the FIS, which consists of two controllers. The first controller uses three sensors based on the obstacles distances from the front, right and left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay. The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.FindingsExperimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS. The reported simulation results are obtained using MATLAB software package. The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently, even in a complex environment that is populated with dynamic obstacles. The paper demonstrates that the destination was reached optimally using the shortest free route.Research limitations/implicationsThe paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions. The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles. However, the methodology is approached using two-dimensional analyses. Hence, the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.Originality/valueThis paper presents the design of a FIS and its characterizations in dynamic environments, specifically for obstacles that move at different velocities. This facilitates an improved functionality of the operation of UGV.

Market-Based Task Assignment for Cooperative Timing Missions in Dynamic Environments

New market-based decentralized algorithms are proposed for the task assignment of multiple unmanned aerial vehicles in dynamic environments with a limited communication range. In particular, a cooperative timing mission that cannot be performed by a single vehicle is considered. The baseline algorithms for a connected network are extended to deal with time-varying network topology including isolated subnetworks due to a limited communication range. The mathematical convergence and scalability analyses show that the proposed algorithms have a polynomial time complexity, and numerical simulation results support the scalability of the proposed algorithm in terms of the runtime and communication burden. The performance of the proposed algorithms is demonstrated via Monte Carlo simulations for the scenario of the suppression of enemy air defenses.

一种基于模糊运算的车辆防撞预警系统设计

本文提出一种基于模糊运算的智能车辆防撞预警系统。针对动态环境不易侦测的特点建立动态避障规则库，利用鱼群算法刻画和解答智能车辆在动态环境下的避障问题。进而，利用智能车辆和障碍物之间的距离和操作者的反应时间建立模糊函数模型。操作者如果没有在预警条件下及时做出反应，该系统将会采用最优路线。实验结果证明，该系统可以根据环境条件做出相对合理的选择，准确地做出预警或者主动避障。 This article puts forward a kind of intelligent vehicle collision warning system based on fuzzy logic. Firstly, according to the characteristics of dynamic environment, which are not easy to detect, the dynamic obstacle avoidance rule base is established. The problem of obstacle avoidance of intelligent vehicle in dynamic environment is described and answered by using Fish Swarm Algorithm. Secondly, the fuzzy function model is established on basis of two factors: the distance between the intelligent vehicle and the obstacle and the time of the driver’s reflection. If the driver does not respond to warning timely, the system will adopt the best optimal route to stop the vehicle. The experimental result shows that the system can make a reasonable choice in accord with changes of the environment in making a warning or triggering an active obstacle avoidance.

Efficient collision-free path planning for autonomous underwater vehicles in dynamic environments with a hybrid optimization algorithm

This paper presents an efficient path-planner based on a hybrid optimization algorithm for autonomous underwater vehicles (AUVs) operating in cluttered and uncertain environments. The algorithm integrates particle swarm optimization (PSO) algorithm with Legendre pseudospectral method (LPM), which is named as hybrid PSO-LPM algorithm. PSO is first employed as an initialization generator with its strong global searching ability and robustness to random initial values. Then, the searching algorithm is switched to LPM with the initialization obtained by PSO algorithm to accelerate the following searching process. The flatness property of AUV is also utilized to reduce the computational cost for planning, making the optimization algorithm valid for local re-planning to efficiently solve the collision avoidance problem. Simulation results show that the hybrid PSO-LPM algorithm is able to find a better trajectory than standard PSO algorithm and with the re-planning scheme it also succeeds in real-time collision avoidance from both static obstacles and moving obstacles with varying levels of position uncertainty. Finally, 100-run Monte Carlo simulations are carried out to check robustness of the proposed re-planner. The results demonstrate that the hybrid optimization algorithm is robust to random initializations and it is effective and efficient for collision-free path planning.