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. 

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

Path planning is one of the core functions of autonomous navigation. A* algorithm is widely used in the study of path planning for robots. Traditional A* algorithm is limited by the direction of node expansion. In some cases, it would generate some unnecessary bends, which makes the path not suit the motion characteristics of the robot and results in bad affection on its path. An improved A* algorithm with search strategy is proposed. The strategy of neighborhood point searching for A* algorithm is presented, so that A* algorithm has a more reasonable node search direction in a wider area. To further solve the unfavorable bending problem, the heuristic function takes the turns of the path into account in the cost function, which can reduce certain types of path turning that affect the robot’s motion. Finally, the improved A* algorithm is verified under simulation environment. Comparison tests with traditional A* algorithm, the path length of the proposed algorithm is reduced by 13.0 %, and the number of turns is reduced by 42.9 %. Compared with Dijkstra algorithm, the total time cost of the proposed algorithm is shortened by 12.3 %. 

Path Planning Algorithm for Mobile Robots based on A* Algorithm with Adaptive Search Strategy

This paper proposes a performance evaluation method of public administration departments based on the improved DEA algorithm, which solves the quality problem of performance evaluation of public administration departments and lays a foundation for the long-term development and performance improvement of public administration departments. The DEA model is the core to sort out the performance evaluation methods of public administration departments. From the perspective of DEA algorithm, whether DEA is effective is an important content that public administration departments must consider when carrying out performance administration, and public service satisfaction is the core parameter obtained by DEA algorithm. That is to say, by optimizing public services and improving service satisfaction, the public administration department can further improve the quality of performance evaluation. Therefore, we can carry out performance evaluation of public administration departments from the satisfaction of public services. The empirical research conclusion shows that according to the effective judgment theorem of DEA, it can be concluded that among the eight social security departments, there are five departments that can achieve DEA effectiveness, namely department 2, department 3, department 4, department 5, and department 6. There are three non-DEA valid ones, namely, department 1, department 7 and department 8. The public satisfaction and the total service cost will affect the performance quality to a certain extent, and only by balancing various influencing factors can the performance evaluation quality of the public administration department be maximized.

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Performance Evaluation Method of Public Administration Department Based on Improved DEA Algorithm

Global path planning is one of the key technologies for airport energy station inspection robots to achieve autonomous navigation. Due to the complexity of airport energy station buildings with numerous mechanical and electrical equipment and narrow areas, planning an optimal global path remains a challenge. This paper aimed to study global path planning for airport energy station inspection robots using an improved version of the Grey Wolf Optimizer (IGWO) algorithm. Firstly, the initialization process of the Grey Wolf Optimizer algorithm selects several grey wolf individuals closer to the optimal solution as the initial population through the lens imaging reverse learning strategy. The algorithm introduces nonlinear convergence factors in the control parameters, and adds an adaptive adjustment strategy and an elite individual reselection strategy to the location update to improve the search capability and to avoid falling into local optima. Benchmark function and global path planning simulation experiments were carried out in MATLAB to test the proposed algorithm’s effectiveness. The results showed that compared to other swarm intelligent optimization algorithms, the proposed algorithm outperforms them in terms of higher convergence speed and optimization accuracy. Friedman’s test ranked this algorithm first overall. The algorithm outperforms others in terms of average path length, standard deviation of path length, and running time.

Global path planning for airport energy station inspection robots based on improved grey wolf optimization algorithm

PurposeThe goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.Design/methodology/approachThe path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.FindingsThe algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.Originality/valueAn improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

Recently, the path planning has become one of the key research hot issues in the field of autonomous vehicles, which has attracted the attention of more and more related researchers. When RRT (Rapidly-exploring Random Tree) algorithm is used for path planning in complex environment with a large number of random obstacles, the obtained path is twist and the algorithm cannot converge quickly, which cannot meet the requirements of autonomous vehicles’ path planning. This paper presents an improved path planning algorithm based on RRT algorithm. Firstly, random points are generated using the circular sampling strategy, which ensures the randomness of the original RRT algorithm and improves the sampling efficiency. Secondly, an extended random point rule based on cost function is designed to filter random points. Then consider the vehicle corner range when choosing the adjacent points, select the appropriate adjacent points. Finally, the B-spline curve is used to simplify and smooth the path. The experimental results show that the quality of the path planned by the improved RRT algorithm in this paper is significantly improved compared with the RRT algorithm and the B-RRT (Bidirectional RRT) algorithm. This can be seen from the four aspects of the time required to plan the path, mean curvature, mean square deviation of curvature and path length. Compared with the RRT algorithm, they are reduced by 55.3 %, 68.78 %, 55.41 % and 19.5 %; compared with the B-RRT algorithm, they are reduced by 29.5 %, 64.02 %, 39.51 % and 11.25 %. The algorithm will make the planned paths more suitable for autonomous vehicles to follow.

Guanghao Huang

Papers

Research on Path Planning Algorithm of Autonomous Vehicles Based on Improved RRT Algorithm