Vehicle Path Planning Research Articles

Wireless communication system throughput maximization based on UAV path planning

This paper studies the path planning of unmanned aerial vehicle (UAV) in wireless information and energy transmission system of wireless sensor network. UAV transmits radio frequency energy to enable ground sensors, and the ground sensors transmit information to the UAV. In order to solve the non-convex optimization problem in path planning, a new alternate optimization framework is proposed to optimize the fly trajectory of the UAV and maximize the throughput of the entire system. The proposed framework firstly utlizes the lagrangian method to obtain relaxation solution without maximum speed constraint, then implements genetic algorithm and continuous convex optimization algorithm to obtain optimal trajectory and energy allocation strategy. Numerical simulations results show that the UAV-assisted wireless information and energy transmission system can realize self-powered sensor network and data collection in special environments, and improve the throughput of the sensor network system.

An Improved Artificial Potential Field Method for Path Planning and Formation Control of the Multi-UAV Systems

Path planning and formation control are both challenging and critical issues in robotics, which involve computing an optimal path from the initial position to target while keeping the desired formation. This brief studies the path planning and formation control problem for multiple unmanned aerial vehicles (multi-UAVs) in 3-D constrained space. Considering the local minimum of artificial potential function (APF), an effective improved artificial potential function (IAPF) based path planning approach is proposed for the multi-UAV systems. By introducing a rotating potential field, the UAVs can escape from the common local minimum and oscillations efficiently. Afterwards, by using the leader-follower model, a formation controller based on potential function method is developed to ensure that the follower UAVs keep the desired angles and distances with the leader, and a Lyapunov function is designed to analyze the closed-system stability. Finally, simulation studies under different environmental constraints confirm the efficiency of the proposed approaches in addressing the path planning and formation control issues in 3-D space.

Sampling-based unmanned aerial vehicle air traffic integration, path planning, and collision avoidance

Unmanned aircraft or drones as they are sometimes called are continuing to become part of more real-life applications. The integration of unmanned aerial vehicles in public airspace is becoming an important issue that should be addressed. As the number of unmanned aerial vehicles and their applications are largely increasing, air traffic with more unmanned aircraft has to be given more attention to prevent collisions and maintain safe skies. Unmanned aerial vehicle air traffic integration and regulation has become a priority to different regulatory agencies and has become of greater interest for many researchers all around the world. In this research, a sampling-based air traffic integration, path planning, and collision avoidance approach is presented. The proposed algorithm expands an existing 2D sampling-based approach. The original 2D approach deals with only two unmanned aircraft. Each of the two aircraft shares location information with a ground-based path planner computer, which would send back the avoidance waypoints after performing the 2D sampling. The algorithm proposed in this article can handle any number of drones in the 3D space by performing either 2D or 3D sampling. The proposed work shows a 10-fold enhancement in terms of the number of unmanned aerial vehicle collisions. The presented results also contribute to enabling a better understanding of what is expected of integrating more drones in dense skies.

Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion

Local path planning is a key task for the motion planners of autonomous vehicles since it commands the vehicle across its environment while avoiding any obstacles. To perform this task, the local path planner generates a trajectory and a velocity profile, which are then sent to the vehicle’s actuators. This paper proposes a new local path planner for autonomous vehicles based on the Attractor Dynamic Approach (ADA), which was inspired by the behavior of movement of living beings, along with an algorithm that takes into account four acceleration policies, the ST dynamic vehicle model, and several constraints regarding the comfort and security. The original functions that define the ADA were modified in order to adapt it to the non-holonomic vehicle’s constraints and to improve its response when an impact scenario is detected. The present approach is validated in a well-known simulator for autonomous vehicles under three representative cases of study where the vehicle was capable of generating local paths that ensure the security of the vehicle in such cases. The results show that the approach proposed in this paper is a promising tool for the local path planning of autonomous vehicles since it is able to generate trajectories that are both safe and efficient.

Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles.

The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route’s accuracy but at the expense of greater planning complexity. In this paper, a new Parallel Cooperative Coevolutionary Grey Wolf Optimizer (PCCGWO) is proposed to solve such a planning problem. The proposed PCCGWO metaheuristic applies cooperative coevolutionary concepts to ensure an efficient partition of the original search space into multiple sub-spaces with reduced dimensions. The decomposition of the decision variables vector into several sub-components is achieved and multi-swarms are created from the initial population. Each sub-swarm is then assigned to optimize a part of the LSGO problem. To form the complete solution, the representatives from each sub-swarm are combined. To reduce the computation time, an efficient parallel master-slave model is introduced in the proposed parameters-free PCCGWO. The master will be responsible for decomposing the original problem and constructing the context vector which contains the complete solution. Each slave is designed to evolve a sub-component and will send the best individual as its representative to the master after each evolutionary cycle. Demonstrative results show the effectiveness and superiority of the proposed PCCGWO-based planning technique in terms of several metrics of performance and nonparametric statistical analyses. These results show that the increase in the number of slaves leads to a more efficient result as well as a further improved computational time.

Modified continuous Ant Colony Optimisation for multiple Unmanned Ground Vehicle path planning

Path planning for multiple Unmanned Ground Vehicles (UGVs) is a critical problem for UGV autonomy and is increasingly attracting attention due to its wide applications. This paper presents a continuous ant colony-based multi-UGV path planner, which consists of UGV path planning and multi-UGV coordination. A continuous Ant Colony Optimisation with a Probability-based random-walk strategy and an Adaptive waypoints-repair method (ACOPAR) is proposed to optimise the path for each UGV. Collision avoidance among the UGVs for the multi-agent coordination problem is then resolved via a velocity shifting optimisation algorithm. In ACOPAR, exploration and exploitation are balanced using a probability-based random-walk strategy switching between a Brownian and a Cauchy motion to modify the construction process of new solutions. An adaptive waypoints-repair strategy and a re-initialisation strategy are designed to improve the algorithm’s performance in finding feasible paths. A test suite of multi-UGV path planning with 12 cases is proposed to evaluate the search capability and scalability of the proposed ACOPAR compared to other algorithms. Experimental results validate the superiority of ACOPAR, especially when solving complex, high-dimensional problems.

Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field

Different driving styles should be considered in path planning for autonomous vehicles that are travelling alongside other traditional vehicles in the same traffic scene. Based on the drivers’ characteristics and artificial potential field (APF), an improved local path planning algorithm is proposed in this paper. A large amount of driver data are collected through tests and classified by the K-means algorithm. A Keras neural network model is trained by using the above data. APF is combined with driver characteristic identification. The distances between the vehicle and obstacle are normalized. The repulsive potential field functions are designed according to different driver characteristics and road boundaries. The designed local path planning method can adapt to different surrounding manual driving vehicles. The proposed human-like decision path planning method is compared with the traditional APF planning method. Simulation tests of an individual driver and various drivers with different characteristics in overtaking scenes are carried out. The simulation results show that the curves of human-like decision-making path planning method are more reasonable than those of the traditional APF path planning method; the proposed method can carry out more effective path planning for autonomous vehicles according to the different driving styles of surrounding manual vehicles.

Application of ant colony and immune combined optimization algorithm in path planning of unmanned craft

The ant colony optimization (ACO) algorithm is improved and further integrated with the immune algorithm (IA) to address its problems, such as slow convergence, local optimum, and premature convergence in the path planning. An algorithm integrating IA and improved ant colony optimization (IACO) is, therefore, put forward to realize the optimal planning of global path for an unmanned surface vehicle (USV). First, the ACO algorithm was improved in three aspects, that is, generation of initial pheromones, transition probability, and update of pheromones. The proposed IA-IACO algorithm combined the advantages of IA and IACO, sped up the convergence, and enhanced the optimization capability and operational efficiency. Second, the IA-IACO algorithm was designed and applied in the global path planning of an unmanned surface vehicle, achieving great global optimization and convergence. Finally, a path smoothing algorithm was devised to achieve the implementable, economic, and stable path while guaranteeing the safe navigation of the USV. A simulation test was carried out to prove the effectiveness and superiority of the designed global path planning algorithm in the practical engineering.

Bipartite Cooperative Coevolution for Energy-Aware Coverage Path Planning of UAVs

The coverage path planning of unmanned aerial vehicles (UAVs) is a complex optimization problem in practice, especially for those involving multiple target areas. It is challenging to comprehensively plan the inter-area visiting order and the intra-area coverage paths simultaneously. Due to the battery limitation, usually the task can hardly be finished by a single UAV, and instead a fleet of UAVs are required. In this article, we first formulate an energy-aware multi-UAV multi-area coverage path planning (EM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CPP) model, in order to characterize the practical path planning requirements of UAVs in complex conditions. Subsequently, to accomplish the optimization task, we propose a bipartite cooperative coevolution (BiCC) algorithm that coevolves an inter-area path planning and an intra-area path planning components to obtain good solutions. The basic operators in BiCC, such as the initialization and the reproduction operators, are tailored for the task of EM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CPP. Besides, we also develop a fast heuristic algorithm for EM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CPP, which is able to produce approximate solutions in a short time. Simulations on real-world datasets validate the good performance of the proposed methods.

A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm

Path planning is one of the key technologies for unmanned driving of underground intelligent vehicles. Due to the complexity of the drift environment and the vehicle structure, some improvements should be made to adapt to underground mining conditions. This paper proposes a path planning method based on an improved RRT* (Rapidly-Exploring Random Tree Star) algorithm for solving the problem of path planning for underground intelligent vehicles based on articulated structure and drift environment conditions. The kinematics of underground intelligent vehicles are realized by vectorized map and dynamic constraints. The RRT* algorithm is selected for improvement, including dynamic step size, steering angle constraints, and optimal tree reconnection. The simulation case study proves the effectiveness of the algorithm, with a lower path length, lower node count, and 100% steering angle efficiency.

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

The transportation system of those countries has a huge traffic flow is bearing great pressure on transportation planning and management. Vehicle path planning is one of the effective ways to alleviate such pressure. Deep reinforcement learning (DRL), as a state‐of‐the‐art solution method in vehicle path planning, can better balance the ability and complexity of the algorithm to reflect the real situation. However, DRL has its own disadvantages of higher search cost and earlier convergence to the local optimum, as vehicle path planning issues are usually in a complex environment, and their action set can be diverse. In this paper, a mixed policy gradient actor‐critic (AC) model with random escape term and filter operation is proposed, in which the policy weight is both data driven and model driven. The empirical data‐driven method is used to improve the poor asymptotic performance, and the model‐driven method is used to ensure the convergence speed of the whole model. At the same time, in order to avoid the model converging local optimum, a random escape term has been added in the policy weight update process to overcome the problem that it is difficult to optimize the non‐convex loss function, and the random escape term can help to explore the policy in more directions. In addition, filter optimization has been innovatively introduced in this paper, and the step size of each iteration of the model is selected through the filter optimization algorithm to achieve the better iterative effect. Numerical experiment results have shown that the model proposed in this paper can not only improve the accuracy of the solution without losing the accuracy but also speed up the convergence speed and improve the utilization of data.

A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning

The traditional seagull optimization algorithm cannot handle multi-objective optimization problems, so a multi-objective quantum-inspired seagull optimization algorithm based on decomposition (MOQSOA/D) is proposed. Multi-objective computing and quantum computing are introduced into MOQSOA/D. MOQSOA/D transforms the multi-objective problem into multiple scalar optimization sub-problems, and establishes a dynamic archive and a leadership archive at the same time. The Pareto solution of each sub-problem is stored in a dynamic archive, and the non-dominated Pareto solution is stored in the leader archive. While processing each sub-problem, each seagull is represented by a string of qubits, which is used to calculate the current seagull direction of flight, and a variable angular-distance rotation (VAR) gate is used to change the probability amplitude of the qubits, thereby updating the direction of flight. Penalty-based boundary intersection approach is introduced to determine whether the generated Pareto solution is retained. The proposed algorithm and six different algorithms were tested on 69 indicators, and the results show that the algorithm achieved better results in 40 indicators. In addition, a Unmanned Aerial Vehicle (UAV) path planning model in three-dimensional environment is designed to test the utility of MOQSOA/D, and the algorithm is compared with the other algorithm to demonstrate its effectiveness.

An autonomous obstacle avoidance method based on artificial potential field and improved A* algorithm for UAV

The main purpose of autonomous obstacle avoidance path planning of unmanned aerial vehicles (UAVs) is to find a collision-free flight path from the starting point to the target point in the threatened airspace. This paper mainly studies and designs an autonomous obstacle avoidance algorithm for UAVs. Firstly, the research status of obstacle avoidance is reviewed. Secondly, the problem scene is set up, and the basic algorithm used is described. Furthermore, a modified algorithm is designed to solve the problems mentioned above. Finally, the effectiveness of the method is simulated and verified.

Smooth Three-Dimensional Route Planning for Fixed-Wing Unmanned Aerial Vehicles With Double Continuous Curvature

This paper presents a smooth flight path planner for maneuvering in a 3D Euclidean space, which is based on two new space curves. The first one is called “Elementary Clothoid-based 3D Curve (ECb3D)”, which is built by concatenating two symmetric Clothoid-based 3D Curves (Cb3D). The combination of these curves allows to reach an arbitrary orientation in 3D Euclidean space. This new curve allows to generate continuous curvature and torsion profiles that start and finish with a null value, which means that they can be concatenated with other curves, such as straight segments, without generating discontinuities on those variables. The second curve is called “Double Continuous Curvature 3D Curve (DCC3D)” which is built as a concatenation of three straight line segments and two ECb3D curves, allowing to reach an arbitrary configuration in position and orientation in the 3D Euclidean space without discontinuities in curvature and torsion. This trajectory is applied for autonomous path planning and navigation of unmanned aerial vehicles (UAVs) such as fixed-wing aircrafts. Finally, the results are validated on the FlightGear 2018 flight simulator with the UAV kadett 2400 platform.

Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

In order to solve the “minimum trap” of artificial potential field method and the limitation of traditional path planning algorithm in dynamic obstacle environment, a path planning control algorithm based on improved artificial potential field method is proposed. Firstly, a virtual potential field detection circle model (VPFDCM) with adjustable radius is proposed to detect the “minimum trap” formed by the repulsion field of obstacles in advance. And the motion model of unmanned vehicle is established. Combined with the improved reinforcement learning algorithm based on Long Short-Term Memory(LSTM), the radius of virtual potential field detection circle is adjusted to achieve effective avoidance of dynamic obstacles, The reliable online collision free path planning of unmanned vehicle in semi closed dynamic obstacle environment is realized. Finally, the reliability and robustness of the algorithm are verified by MATLAB simulation. The simulation results show that the improved artificial potential field method can effectively solve the problem of unmanned vehicle falling into the “minimum trap” and improve the reliability of unmanned vehicle movement. Compared with the traditional artificial potential field method, the improved artificial potential field method can achieve more than 90% success rate in obstacle avoidance.

Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position

The local path planning, as one of the navigation stages, plays a significant role in the energy consumption of Self-Guided Vehicles (SGV). Since SGV must operate for several hours on a single battery charge to transport loads, its energy consumption is a critical issue. Therefore, this article puts forward an approach for boosting the energy efficiency of the local path planning stage using load position. Unlike other similar works which solely use robots’ kinematic and kinetic constraints to develop energy-efficient local path planners, this article considers the effect of load position on SGV’s dynamic. In this regard, first, the kinetic model of the differential drive SGV is developed to consider the change of SGV’s Center of Mass (CoM) affected by load properties. Second, machine learning methods are used to create two learning models for online estimation of the position of CoM (PoCoM) and prediction of required energy of sample trajectories. Hence, the generated SGV’s kinetic model is used to train the learning models. Finally, estimated parameters are employed to add a new constraint to extend the cost function of the local path planner. The outcomes of the study show that the proposed planner generates smoother and shorter paths to pass obstacles and corridors than a general one. Thus, SGV’s energy consumption decreases by considering the load effect.

FPGA-based vehicle real-time path planning system

In order to realize the requirements of low latency and low power consumption in real-time path planning for private vehicles, this paper designs an FPGA accelerator based on real-time road information and Dijkstra's shortest path algorithm, which is applied to the path planning system of vehicle edge computing. The system is designed using Xilinx High-Level Synthesis (HLS) compiler and is implemented in the programming logic of a Xilinx Zynq FPGA. The experiment is carried out on a city map, and the results show that the system has lower circuit area and power consumption, and its computing performance is 3.8 times that of ARM, which is suitable for edge computing platform.

Improved ant colony algorithm for path planning of fixed wing unmanned aerial vehicle

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.

Multi-strategy fusion differential evolution algorithm for UAV path planning in complex environment

The path planning of Unmanned Aerial Vehicle (UAV) is a real-world optimization problem, and even develops into a hard optimization problem with many objectives and constraints when UAVs work in a complex environment. In a complex environment, the resulting constraints can lead to decrease the quantity of the feasible solutions, so that can bring difficulties to plan routes for UAVs. Therefore, it is necessary to design a high-quality planner for a UAV in a complex environment. In this work, we have proposed a Multi-Strategy Fusion Differential Evolution algorithm (MSFDE). The proposed algorithm integrates the multi-population strategy, the novel self-adaptive strategy and the ensemble of the interactive mutation strategy in order to balance the exploitation and exploration capabilities. The multi-population strategy is used to divide the whole population into the three indicator subpopulations and a reward subpopulation for maintaining the diversity of the whole population; the novel self-adaptive strategy is introduced to control the parameters F and CR based on the teaching-learning-based optimization method; the ensemble of the interactive mutation strategy is to exchange the information among the three indicator subpopulations on each generation for boosting the population diversity. The constraints in the UAV path planning are transformed into the objective functions by the linear weighted sum method. Scenario 1, 2, 3, and 4 are designed with different complex level, and other eight algorithms are introduced to be compared with MSFDE. The simulation results confirm that MSFDE has an outstanding performance for the UAV three-dimensional path planning in the complex environment.

An Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay for Path Planning of Unmanned Surface Vehicles

Unmanned Surface Vehicle (USV) has a broad application prospect and autonomous path planning as its crucial technology has developed into a hot research direction in the field of USV research. This paper proposes an Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay (IPD3QN) to address the slow and unstable convergence of traditional Deep Q Network (DQN) algorithms in autonomous path planning of USV. Firstly, we use the deep double Q-Network to decouple the selection and calculation of the target Q value action to eliminate overestimation. The prioritized experience replay method is adopted to extract experience samples from the experience replay unit, increase the utilization rate of actual samples, and accelerate the training speed of the neural network. Then, the neural network is optimized by introducing a dueling network structure. Finally, the soft update method is used to improve the stability of the algorithm, and the dynamic ϵ-greedy method is used to find the optimal strategy. The experiments are first conducted in the Open AI Gym test platform to pre-validate the algorithm for two classical control problems: the Cart pole and Mountain Car problems. The impact of algorithm hyperparameters on the model performance is analyzed in detail. The algorithm is then validated in the Maze environment. The comparative analysis of simulation experiments shows that IPD3QN has a significant improvement in learning performance regarding convergence speed and convergence stability compared with DQN, D3QN, PD2QN, PDQN, PD3QN. Also, USV can plan the optimal path according to the actual navigation environment with the IPD3QN algorithm.