Global Path Planning Research Articles

Control algorithm of path planning and path tracking for automated valet parking

Automated Valet Parking (AVP) can relieve drivers from having to park. It enables vehicles to drive, park and charge fully automatically. This paper focuses on the first working step of the motion planning module, the global path planner. Therefore, a global path planner will be developed and evaluated based on actual research methods. A route will be computed by using an A^* algorithm based on the occupancy grid map. Subsequently, a smoothing operation will follow to obtain a drivable path. By implementing a single polar polynomial function each vertex is smoothened. The resulting global path serves as an input for path tracking. For a reasonable simulation, a vehicle controller with a lock-ahead distance and a node-check will be designed in order to keep the vehicle on the desired path. A simulation of the designed components in CarSim will be carried out to analyze the path tracking and vehicle behavior in detail.

Global path planning based on BIM and physics engine for UGVs in indoor environments

This paper proposes a global path planning (GPP) system based on building information modeling (BIM) and physics engine for unmanned ground vehicles (UGVs) operations in indoor environments. Firstly, UGV configuration is integrated into BIM as a knowledge base of GPP system by customized IFC structure, with which a multi-layer map generation method is proposed with improved logic and efficiency. Secondly, a UGV-centric A* path planning algorithm is designed by considering UGV's properties, including mobility-based primitive expansion, geometry-based collision checking, mobility-based cost setting, and mobility-based analytical expansion. Finally, a reliable trajectory generation method is developed based on physics engine, followed with a novel spatiotemporal coordination method for efficient collision avoidance. The whole GPP system is validated in a representative university building floor and a common inspection UGV. It is demonstrated that UGV-integrated BIM improves the resources interoperability for the whole system, and the map components are clarified clearly for efficient generation and maintenance. Besides, the UGV-centric A* performs a 100% success rate in congested environments where traditional A* always fail. It can even reduce almost 50% of the trajectory time and steering jerk in open scenarios than traditional A*, but still with an acceptable computation speed (less than 0.6 s). Finally, trajectory coordination saves 50% of path traveling time compared with the general queue method.

Path Planning under The Hull bottom of Painting Robot Based on Heuristic Multi-robot Cooperation in Ship Manufacturing

In the whole world, the economic loss caused by hull corrosion is enormous. Ship painting has become an important part of ship manufacturing process because it can effectively alleviate the corrosion of ship. The manual painting has disadvantages both in the quality and the efficiency. However, the research of automatic sprayers for a ship hull is not widely used because of the complex environment in the shipyard dock and the huge differences in both size and shape of ships to be repaired. Therefore, this paper pro-posed a new method: according to the ship size and blocks distribution in the blocks’ layout of ship yards, the grid method was used to generate the map model; to solve the problems of high rerouting rate, low coverage and large consumption of calculation in the global path planning, a regional division method was proposed to divide the whole area; to shorten the dock occupancy time, a path planning algorithm based on multi robots heuristic cooperation was proposed. Simulation results and experi-mental data show that the full coverage path planning algorithm proposed in this paper has satisfactory adaptability.

Whale optimization algorithm based on Levy flight and memory for static smooth path planning

In this paper, a whale optimization algorithm based on Levy flight and memory (WOALFM) is proposed to solve the curvature discontinuity problem in the global path planning of unmanned vehicles. Levy flight and chaotic mapping are introduced to disturb the solutions of each generation and enhance the diversity of solutions. A memory strategy based on fractional-order expansion is presented to remember the influence of the positions of individuals in previous generations on the positions of current generation. This strategy based on Levey flight and memory can enhance searching ability and avoid falling into local optimum. Furthermore, the ratio of global searching to local searching can be adjusted to achieve desired results in WOALFM algorithm. The proposed WOALFM algorithm is tested and compared with five algorithms including whale optimization algorithm (WOA), Moth-Flame Optimization (MFO), particle swarm optimization (PSO), Fractional-Order Velocity based Particle Swarm Optimization (FOPSO) and Grey Wolf Optimizer (GWO) on 23 standard benchmark functions. The experimental results show the effectiveness of WOALFM. The proposed algorithm is applied to smooth path planning problem of unmanned vehicles. Three factors, including the length, curvature and curvature derivative of a path are considered in order to obtain the shortest smooth path without collisions. The experimental results show that more collision-free paths can be obtained in lower computational cost by the presented method.

Path Planning Algorithm for Unmanned Surface Vehicle Based on Optimized Ant Colony Algorithm

In order to construct an efficient and reliable global path for unmanned surface vehicles (USVs), this paper proposes a USV global path planning algorithm based on an optimized ant colony algorithm (OACA). The algorithm constructs a USV global path planning model by comprehensively considering energy consumption costs and turning control costs. In order to ensure the adaptability of the model to the task environment, the USV's motion characteristics are analyzed with full consideration of external interference, and on this basis, the characterization of energy consumption costs and turning control costs is completed. In order to obtain the global optimal solution of the model and accelerate the convergence speed of the model, in the initialization process of the ant colony algorithm, the initial pheromone is distributed unevenly by introducing the distance relationship among the intermediate node, the starting point, and ending point to improve the initial search efficiency. In the iterative process of searching for the optimal solution, enhancing the guidance effect of the current optimal solution on the offspring ants by introducing a weight factor to improve the update rule of pheromone, which accelerates the convergence speed of the algorithm. In the last two steps of the deadlock path, the number of lost ants is reduced by introducing a penalty factor to punish the pheromone which can guarantee the diversity of ants, and the algorithm's premature convergence is overcome. The simulation results prove the effectiveness of this algorithm. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Dynamic adaptive security path planning based on A* algorithm

Aiming at the problems of large memory consumption, long operation time and low security of traditional A * algorithm, a dynamic adaptive improved algorithm based on A * algorithm is proposed. Firstly, according to the heuristic function of the proportion of obstacles in the map, the adaptive dynamic weighting is carried out to shorten the search time. Secondly, the jump point search (JPS) algorithm is integrated to remove a large number of redundant points and improve the running speed of the algorithm. Finally, according to the relative position of the starting point and the target point, the obstacles are expanded dynamically to keep a safe distance between the planned path and the obstacles. In order to verify the effect, the improved algorithm is tested in different sizes and different proportion of obstacles. The results show that the improved algorithm is efficient and safe in global path planning.

SCVP: Learning One-Shot View Planning via Set Covering for Unknown Object Reconstruction

The view planning (VP) problem in robotic active vision enables a robot system to automatically perform object reconstruction tasks. Lacking prior knowledge, next-best-view (NBV) methods are typically used to plan a view sequence, with the goal of covering as many object surface areas as possible in an unknown environment. However, such methods have two problems: (1) they are unable to perform global path planning; and (2) the reconstruction process is inefficient because of time-consuming ray casting and high movement cost. We propose a neural network, SCVP, to pre-learn prior knowledge via set covering (SC) based training so as to achieve one-shot view planning. The SCVP network takes the volumetric occupancy grid as input and directly predicts a small (ideally minimum) number of views that cover all surface areas. Given object 3D models as a priori geometric knowledge, the training dataset is automatically labeled by the set covering optimization method. We propose a global path planning method to reconstruct objects without redundant movement. Comparative experiments on multiple datasets of 3D models show that, under the condition of similar or better surface coverage, the proposed method can outperform state-of-the-art NBV methods in terms of movement cost and inference time. Real-world experiments confirm that the proposed method can achieve faster object reconstruction than other methods.

TraEDITS: Diversity and Irregularity-Aware Traffic Trajectory Editing

We present TraEDITS, a novel traffic trajectory editing framework for autonomous vehicle testing, which can generate new traffic behaviors by controlling each vehicle interactively to increase the diversity or irregularity of traffic testing data. Given a traffic flow with its original trajectories, user’s edits, environmental constraints, and physical constraints as input, our framework is able to generate an edited traffic flow through a global path planning module and a data-driven microscopic traffic flow simulation module. With the way-points from the user, our global planning module generates lane-level navigation by heuristic-based path planning in discrete space. By taking internal properties of the vehicle, velocity continuity, reference path, and collision avoidance into consideration, our simulation module generates vehicles’ motions based on energy optimization driven by real traffic data. Given edits of the desired speed, the lateral deviation, or the reference path, our approach can generate a new trajectory of the selected vehicle, and adjust the surrounding vehicles’ trajectories accordingly. As a result, our framework is able to simulate irregular or even rare events existing in real traffic. Meanwhile, our framework can enhance the diversity and irregularity of traffic behaviors and interactions by creating challenging scenarios like swerve, nudge, and U-turn. We validate the usability and plausibility of our framework through extensive experiments and user studies.

Global path planning based on a bidirectional alternating search A* algorithm for mobile robots

While the A* algorithm has been widely investigated and applied in path planning problems, it has outstanding issues such as long calculation time, large turning angles, and the unsmoothed path in large task spaces. Aiming at overcoming these problems, an improved A* algorithm is proposed in this work. First, a bidirectional alternating search (BAS) strategy is introduced into the A* algorithm; the forward and backward path lists use the current path node in the opposite list as the target node to alternately search for paths until the paths meet, which improves the search efficiency of the algorithm. Then, the heuristic function is weighted via exponential attenuation, which overcomes the shortcomings of the BAS-A* algorithm. Finally, the filtering function of path nodes is introduced to reduce redundant nodes in the path, thereby effectively reducing the turning angle. Moreover, the use of Bézier curves fulfills the requirements of smooth path planning, which is critical for the motion control of mobile robots. Simulations of path planning on maps of different sizes were performed, and the proposed algorithm was compared with that of the A* algorithm, the genetic algorithm (GA) and the simulated annealing algorithm (SA) in terms of the computation time, path length, and turning angle. The results reveal that the proposed algorithm can solve the robot path planning problem more efficiently and smoothly than the other algorithms mentioned above. Additionally, the practicability of the proposed algorithm is validated on the TurtleBot3 Waffle Pi mobile robot.

Map Construction and Path Planning Method for a Mobile Robot Based on Multi-Sensor Information Fusion

In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized.

A Hybrid Mobile Robot Path Planning Scheme Based on Modified Gray Wolf Optimization and Situation Assessment

To better solve the problems associated with optimal pathfinding and dynamic obstacle avoidance in the path planning of mobile robots, a hybrid path planning scheme combining modified gray wolf optimization (MGWO) and situation assessment mechanism is proposed. Firstly, a MGWO algorithm is proposed to plan a global path. Secondly, different situational factors for robots in different regions are extracted from the fusion results of 2D laser measurements and image data, and a Bayesian network model of robot action selection is established. Then, the situational factors of the robot are used as evidence for reasoning. Based on the posterior probability value in the inference result, the grid to be moved is selected and the traveling direction of the robot is adjusted in order to take advantage of both global path planning and local dynamic obstacle avoidance. The simulation results show that the proposed MGWO has better optimization performance. When combined with a situation assessment mechanism, it realizes dynamic obstacle avoidance while keeping the path length as short as possible.

Fast Path Planning for Long-Range Planetary Roving Based on a Hierarchical Framework and Deep Reinforcement Learning

The global path planning of planetary surface rovers is crucial for optimizing exploration benefits and system safety. For the cases of long-range roving or obstacle constraints that are time-varied, there is an urgent need to improve the computational efficiency of path planning. This paper proposes a learning-based global path planning method that outperforms conventional searching and sampling-based methods in terms of planning speed. First, a distinguishable feature map is constructed through a traversability analysis of the extraterrestrial digital elevation model. Then, considering planning efficiency and adaptability, a hierarchical framework consisting of step iteration and block iteration is designed. For the planning of each step, an end-to-end step planner named SP-ResNet is proposed that is based on deep reinforcement learning. This step planner employs a double-branch residual network for action value estimation, and is trained over a simulated DEM map collection. Comparative analyses with baselines demonstrate the prominent advantage of our method in terms of planning speed. Finally, the method is verified to be effective on real lunar terrains using CE2TMap2015.

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.

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.

Research on Effective Path Planning Algorithm Based on Improved A* Algorithm

In recent years, with the rapid development of technology, mobile robots are being applied in all aspects of production and life. The A* algorithm can be used for global path planning of mobile robots. Aimed at the problem of the traditional A* algorithm planning path having many turning points and do not satisfying the global optimality, an A* algorithm based on the adaptive neighborhood search and steering cost has been proposed. Based on the information of the surrounding obstacles, the algorithm adaptively selects the appropriate neighborhood to search for the optimal child node. By establishing the steering cost model of the mobile robot, the steering cost is joined into the evaluation function of the A* algorithm. Based on the adaptive neighborhood search, the A* algorithm with steering cost breaks through the constraint of eight neighborhood search nodes, shortens the path length, and uses the steering cost to search for the global optimal path with fewer turning points. The simulation results show that compared to the traditional A* algorithm, the total path cost of the A* algorithm based on adaptive neighborhood search and steering cost is reduced by 19.3%, and the number of turning points is reduced by 44.4%.

An IoT-Based COVID-19 Prevention and Control System for Enclosed Spaces

To date, the protracted pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had widespread ramifications for the economy, politics, public health, etc. Based on the current situation, definitively stopping the spread of the virus is infeasible in many countries. This does not mean that populations should ignore the pandemic; instead, normal life needs to be balanced with disease prevention and control. This paper highlights the use of Internet of Things (IoT) for the prevention and control of coronavirus disease (COVID-19) in enclosed spaces. The proposed booking algorithm is able to control the gathering of crowds in specific regions. K-nearest neighbors (KNN) is utilized for the implementation of a navigation system with a congestion control strategy and global path planning capabilities. Furthermore, a risk assessment model is designed based on a “Sliding Window-Timer” algorithm, providing an infection risk assessment for individuals in potential contact with patients.

An Open-Source Low-Cost Mobile Robot System With an RGB-D Camera and Efficient Real-Time Navigation Algorithm

Currently, mobile robots are developing rapidly and are finding numerous applications in the industry. However, several problems remain related to their practical use, such as the need for expensive hardware and high power consumption levels. In this study, we build a low-cost indoor mobile robot platform that does not include a LiDAR or a GPU. Then, we design an autonomous navigation architecture that guarantees real-time performance on our platform with an RGB-D camera and a low-end off-the-shelf single board computer. The overall system includes SLAM, global path planning, ground segmentation, and motion planning. The proposed ground segmentation approach extracts a traversability map from raw depth images for the safe driving of low-body mobile robots. We apply both rule-based and learning-based navigation policies using the traversability map. Running sensor data processing and other autonomous driving components simultaneously, our navigation policies perform rapidly at a refresh rate of 18 Hz for control command, whereas other systems have slower refresh rates. Our methods show better performances than current state-of-the-art navigation approaches within limited computation resources as shown in 3D simulation tests. In addition, we demonstrate the applicability of our mobile robot system through successful autonomous driving in an indoor environment.

Hybrid Path Planning Model for Multiple Robots Considering Obstacle Avoidance

To solve the problem that the single robot task execution capability is not enough to meet the whole handling task demand under complex conditions, the hybrid path planning models such as multi-robot path planning and formation cooperative control considering obstacle avoidance are studied. Firstly, for the robot global path finding problem, on the basis of the construction for a robot working environment model based on the geometric map model building method, an improved particle swarm algorithm-based global path planning model is proposed to solve the problems of low robot path planning solution efficiency and easy to fall into local optimal solutions. Secondly, for the multi-robot cooperative formation control and obstacle avoidance and inter-robot collision avoidance problems, a multi-robot formation local path planning model based on the improved artificial potential field method is constructed, a simulated annealing algorithm is introduced to optimize the traditional artificial potential field method, and a multi-robot formation control strategy, robot obstacle avoidance, and inter-robot collision avoidance methods are designed in combination with the pilot-following method to improve the robot formation path exploration The proposed method can improve the path exploration capability and handling efficiency of robot formation. Finally, the global path planning model of the robot based on the improved particle swarm algorithm is simulated and analyzed using Matlab 7.0 to verify the outstanding performance of the model in pathfinding capability. Then the local path planning model of multi-robot formation based on the improved artificial potential field is simulated and analyzed to verify the improved algorithm has good path planning as well as obstacle avoidance performance. The hybrid path planning model is applied to a real case and simulated, and the results show that the improved algorithm improves the exploration capability of the robot formation, effectively avoids obstacles, and verifies its reliability and superiority in the hybrid path planning process.

Particle Swarm Optimization for Dynamic Risk-Aware Path Following for Autonomous Ships

This work presents the use of particle swarm optimization (PSO) for dynamic risk-aware path following, or waypoint re-planning during autonomous surface navigation in a maritime environment with polygonal grounding obstacles. Although recent research on control and local or global path planning for maritime autonomous surface ships (MASS) is considerable, few deal with the concept of risk during dynamic path following along a preplanned path. The proposed method introduces risk-based terms in the PSO fitness function for dynamic (online) adjustment or re-planning of intermediate waypoints during path following of a pre-planned route, where the control of the vessel in this work is left to a standard line-of-sight PID controller. Moreover, the results are compared to the performance of an analogous implementation of risk-aware model predictive control (MPC) using a gradient-based solver. The suggested method yields adequate performance similar to that of the MPC algorithm. Ultimately, the PSO approach may in future works allow for more complex and dynamic risk-aware behavior related to e.g. weather conditions implemented through lookup tables, or discrete machinery modes that are non-smooth.

An improved ant colony algorithm for integrating global path planning and local obstacle avoidance for mobile robot in dynamic environment.

To improve the path optimization effect and search efficiency of ant colony optimization (ACO), an improved ant colony algorithm is proposed. A collar path is generated based on the known environmental information to avoid the blindness search at early planning. The effect of the ending point and the turning point is introduced to improve the heuristic information for high search efficiency. The adaptive adjustment of the pheromone intensity value is introduced to optimize the pheromone updating strategy. A variety of control strategies for updating the parameters are given to balance the convergence and global search ability. Then, the improved obstacle avoidance strategies are proposed for dynamic obstacles of different shapes and motion states, which overcome the shortcomings of existing obstacle avoidance strategies. Compared with other improved algorithms in different simulation environments, the results show that the algorithm in this paper is more effective and robust in complicated and large environments. On the other hand, the comparison with other obstacle avoidance strategies in a dynamic environment shows that the strategies designed in this paper have higher path quality after local obstacle avoidance, lower requirements for sensor performance, and higher safety.