Path planning methods for autonomous vehicles at intersections: A review

Needle insertion is widely used in modern clinical practice, and flexible needles enable surgeons to adjust the needle insertion path to avoid obstacles and compensate for target drift. To increase the accuracy and safety of the needle insertion procedure, a path planning and tracking control method for flexible needle steering in dynamic environment is proposed in this paper. The path planning method is based on the artificial potential field algorithm and curve fitting algorithm, after several feasible paths with variant curvatures is obtained, they are furtherly optimized based on path length, obstacle clearance and control effort to select the best one. Simulations show that the proposed path planning method performs better than the current constant curvature method and the rapidly-exploring random tree (RRT) based method. By finely tuning the initial planned path, an intraoperative path replanning method is proposed, which are used to compensate for the drift of target and obstacles. Then to ensure the needle is inserted along the planned path, a tracking control method is adopted based on fuzzy logic algorithm. With the proposed intraoperative path planning and tracking control method, the robustness and accuracy of needle insertion in congested environment can be improved, and experiments also show that our proposed method achieves satisfactory insertion safety and targeting accuracy in dynamic environment. The obtained accuracy of 1.57 mm is sufficient for most clinical needle insertion applications.

This paper presents a survey of the most recent literature developments in the field of computational guidance and control for space applications. In recent years, the space sector has changed significantly, shifting towards solutions requiring increased performance and autonomy. This change has also been accelerated by the technical and economic needs of the new space approach. However, traditional guidance and control techniques cannot always meet the increasing requirements of autonomy and performance. For this reason, computational guidance and control has become of increasing interest to the aerospace sector. In contrast to traditional guidance and control, computational guidance and control generates commands in real time by using on-board numerical computation. It represents, therefore, a fundamental step on the way to system autonomy and autonomous operations, with the potential to increase system capabilities and reduce operational costs. On-board computation introduces, however, a series of challenges, including the need to develop algorithms which are computationally efficient, reliable and robust. The literature has classified computational guidance and control techniques into two major categories, namely model-based and data-based. The model-based category encompasses all techniques in which the guidance and control laws are explicitly related to the system dynamic and kinematic models. On the contrary, data-based techniques infer guidance and control laws from a set of data, derived from the models, but do not explicitly use such models in the algorithm itself. Model-based guidance and control includes optimal control, model predictive control and convex optimisation. For the specific case of space attitude re-orientation manoeuvres, additional techniques are path planning and artificial potential functions. The general concept behind the Model Predictive Control (MPC) framework is to solve an optimal control problem repeatedly, for example whenever new measurements are available. The MPC implements only the first element of the returned solution, and the process is then repeated. By doing so, the system is better able to cope autonomously with model uncertainties, failures, or errors. For spacecraft with limited on-board capability, having to repeatedly solve an optimisation problem on-board could represent a challenge. In this case, explicit implementations of the MPC have been proposed, in which the guidance and control laws are precomputed offline and implemented on-board using look-up tables or simple functions. If the optimal control problem of the MPC is convex, or if it can be convexified, convex optimisation can be used directly. If the problem cannot be convexified, the solution can be found by sequentially solving a convex approximation of the original non-convex problem. Convex optimisation can be used either repeatedly in the context of an MPC framework, or as a single function call to generate guidance and control laws, e.g. at the beginning of an attitude re-orientation manoeuvre. Convex optimisation has received increased attention in recent years, also due to its suitability to on-board implementation. In fact, if the optimal control problem is convex, it can be solved reliably and efficiently on-board. Convex optimisation has been successfully applied to the space sector with the notable examples of the SpaceX rocket booster landing and of the Mars pinpoint landing demonstration aboard the Masten Xombie sounding rocket. Model-based guidance techniques, not based on optimisation, have been increasingly explored in recent years, as guidance strategies employed in other engineering fields, such as robotics, are being applied to spacecraft. Among these strategies, of particular importance are path planning and artificial potential field methods, due to their applicability to spacecraft autonomous attitude manoeuvres. Path planning algorithms have been applied for decades to ground vehicles and robotic manipulators. These algorithms consist of techniques to find the best route between two points of a graph representing the search space of a problem. For spacecraft re-orientation applications, the literature has proposed different attitude representation conventions and various cost functions associated to the graph search algorithm. Each approach comes with advantages and disadvantages, which will be analysed in the paper. Along with path planning, Artificial Potential Function (APF) techniques have been developed in the field of robotics, and are now also considered for space applications, to track or maintain a specific reference under a set of constraints. The main idea behind these methods is to synthetise the controller using a potential function. This function is specifically built to grant asymptotic stability to the system, according to Lyapunov’s second stability theorem. This formulation grants flexibility to accommodate system constraints, in a computationally light-weight and efficient manner. Data-based guidance and control laws are mainly based on Artificial Intelligence (AI) technologies, and generate guidance commands after training an algorithm with a large set of data. The interest of the space sector towards AI has accelerated in the last years due to the greater maturity of the technology. Some of the most powerful examples lie in the area of machine learning, like reinforcement learning, supervised learning and deep learning. These algorithms are used to extract patterns from large amounts of data derived from complex nonlinear dynamical and kinematical models. Examples of such data can be numerical solutions of an optimal control problem. The patterns extracted from the data allow the system to identify, in a short computational time, the critical parameters needed to relate the state and control variables. Creating a sufficiently large dataset for training the algorithm requires a considerable computational effort, though, and for certain methods optimality cannot be guaranteed due to the lack of theoretical support derived from the optimality conditions. Advancements made in the last years span several research directions and open a wide variety of opportunities and challenges, which need to be examined to guide further exploration. In this paper, both model-based and data-based techniques and applications are presented. The considered techniques include model predictive control, convex optimisation and machine learning. In addition, for the implementation of autonomous attitude manoeuvres, path planning methods and artificial potential function methods are analysed. These techniques have been applied, in the literature, to a range of different applications. The applications discussed in this paper include, in addition to attitude manoeuvres, powered descent landing guidance, low-thrust orbital transfers, proximity operations and station keeping.

Due to the different operating environments of logistics drones, there are differences in path planning models and methods. To solve the problem of path planning for cross-sea UAV logistics, an improved grid airspace environment construction method and a path planning model for cross-sea logistics UAV are proposed. Flight distance cost, population threat cost, obstacle risk cost, and height change cost are considered in the objective function, and the performance of the logistics drone is considered as the constraints of the path planning problem. A* algorithm with an improved heuristic function is used to solve this problem. The success rate of solving in a specific environment is close to 100%. Compared with the Dijkstra path planning method, the calculation time is shortened by 26.08% on average. The model considered various goals of UAV cross-sea logistics and the algorithm can solve the question faster, which has practical significance.

Maritime transportation is vital to the global economy. With the increased operating and labor costs of maritime transportation, autonomous shipping has attracted much attention in both industry and academia. Autonomous shipping can not only reduce the marine accidents caused by human factors but also save labor costs. Path planning is one of the key technologies to enable the autonomy of ships. However, mainstream ship path planning focuses on searching for the shortest path and controlling the vehicle in order to track it. Such path planning methods may lead to a dynamically infeasible trajectory that fails to avoid obstacles or reduces fuel efficiency. This paper presents a data-driven, efficient, and safe path planning (ESP) method that considers ship dynamics to provide a real-time optimal trajectory generation. The optimization objectives include fuel consumption and trajectory smoothness. Furthermore, ESP is capable of fast replanning when encountering obstacles. ESP consists of three components: (1) A path search method that finds an optimal search path with the minimum number of sharp turns from the geographic data collected by the geographic information system (GIS); (2) a minimum-snap trajectory optimization formulation with dynamic ship constraints to provide a smooth and collision-free trajectory with minimal fuel consumption; (3) a local trajectory replanner based on B-spline to avoid unexpected obstacles in real time. We evaluate the performance of ESP by data-driven simulations. The geographical data have been collected and updated from GIS. The results show that ESP can plan a global trajectory with safety, minimal turning points, and minimal fuel consumption based on the maritime information provided by nautical charts. With the long-range perception of onboard radars, the ship can avoid unexpected obstacles in real time on the planned global course.

Post-earthquake robots can be used extensively to inspect and evaluate building damage for safety assessment. However, the surrounding environment and path for such robots are complex and unstable with unexpected obstacles. Thus, path planning for such robot is crucial to guarantee satisfactory inspection and evaluation while approaching the ideal position. To achieve this goal, we proposed a distributed small-step path planning method using modified reinforcement learning (MRL). Limited distance and 12 directions were gridly refined for the robot to move around. The small moving step ensures the path planning to be optimal in a neighboring safe region. The MRL updates the direction and adjusts the path to avoid unknown disturbances. After finding the best inspection angle, the camera on the robot can capture the picture clearly, thereby improving the detection capability. Furthermore, the corner point detection method of buildings was improved using the Harris algorithm to enhance the detection accuracy. An experimental simulation platform was established to verify the designed robot, path planning method, and overall detection performance. Based on the proposed evaluation index, the post-earthquake building damage was inspected with high accuracy of up to 98%, i.e., 20% higher than traditional unplanned detection. The proposed robot can be used to explore unknown environments, especially in hazardous conditions unsuitable for humans.

Path planning and tracking control play a crucial role in the safe driving of an autonomous vehicle. To enhance the safety, comfort, and energy efficiency of the vehicle during obstacle avoidance, a path planning and tracking method considering road space constraints and safety constraints is proposed. Firstly, a path planning algorithm for obstacle avoidance was designed, which takes the safety distance between vehicles and the constraints of road width into account, ensuring safe obstacle avoidance during the process, while reducing energy consumption and enhancing comfort; Second, a three-degree-of-freedom simplified vehicle dynamics model considering real-time slip rate is constructed, improving the dynamic accuracy of the vehicle simulation model under different road conditions; Finally, a nonlinear time-varying MPC is designed based on the dynamics model. The results show that in various scenarios, compared with the path planning methods based on obstacle avoidance function and artificial potential field, the safety has been improved by an average of 10.06%, the comfort has been enhanced by an average of 24.45%, and the average energy consumption has been reduced by 14.73%.

As the demand for autonomous mobile robots has risen sharply, the collision-free path planning/navigation problem has become and still is a focus of attention for many researchers. This paper covers a rangeof path planning approaches for various types of mobile robots, such as ground mobile robots, unmanned aerial vehicles, and autonomous vehicles. The literature is classified into two categories: global path planning and reactive path planning. To help readers comprehend the flow within each category, we analyze and compare each category from the perspectives of environmental modeling, optimization criteria, and different methods for path planning. In comparison to earlier survey articles, we place a greater emphasis on Artificial Intelligence (AI) and self-learning navigation methods. In particular, different robot kinematic models are also discussed in thisarticle. Finally, we indicated some future research directions.

Path planning and tracking of autonomous vehicles are generally two independent tasks accomplished through traffic environment modeling, reference trajectory generation and vehicle motion control. In this paper, we proposed a unified path planning and tracking method utilizing the optimization algorithm of the model predictive control to generate optimal reference trajectory and vehicle motion control concurrently. The vehicle’s surroundings including obstacle vehicles and road marks are firstly reconstructed based on the artificial potential field approach which generates a reference trajectory, then the total potential of the traffic environment is incorporated into the cost function of the model predictive controller. Therefore, the path planning and tracking of the vehicle can be unified for collision avoidance with moving or fixed obstacles in its surrounding traffic environment. The simulation shows that this unified path planning and tracking method is capable of accomplishing the obstacle avoidance for the vehicle during severe traffic scenarios.

Path planning and obstacle avoidance for AUV: A review

From naval operations to ocean science missions, the importance of autonomous vehicles is increasing with the advances in underwater robotics technology. Due to the dynamic and intermittent underwater environment and physical limitations of underwater unmanned vehicle (UUV), feasible and optimal path planning is crucial for autonomous underwater operations. According to different mission, the path planning method of UUV is divided into two categories: the point to point path planning and the complete coverage path planning. The objective of this thesis is to develop and demonstrate an efficient underwater path planning method that is adapted to complicated ocean environment. In this thesis, existing path planning method for the fields of ocean science and robotics are first reviewed, and then local dynamic obstacle avoidance method is proposed to avoid dynamic obstacles. Based on this again, the path planning of UUV in local dynamic environment can be efficiently implemented by adopting rolling window path planning method and local dynamic obstacle avoidance method. This method with the guide point strategy combines global path planning with local dynamic path planning, so that not only the requirements of real-time on-line path planning for UUV are met, the global optimality is also considered. A navigation route for UUV is planned in advance by using priori environmental information based on ant colony algorithm, so it provides the reference information for the selection of guide point. In order to solved the problem of area coverage search, a complete coverage path planning method is proposed by combining ant colony algorithm with biologically inspires neural network. In order to demonstrate underwater path planning method, all of the above ideas and methods developed were tested in simulation experiments.

A novel seam extraction and path planning method for robotic welding of medium-thickness plate structural parts based on 3D vision

<p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Following the development of the unmanned aerial vehicle (UAV) industry, the number of UAVs has increased significantly, and higher expectations regarding the effect of spatial path planning have been expressed. Traditional coordinate-based conflict detection and path planning methods of UAVs are highly complex and have low computational efficiency. While the conflict detection method based on the grid system can improve efficiency, the rigid grid structure of the existing local grid system or discrete global grid system is insufficient. Since the rigid structure only encodes grid cells, multiple or different size grids are required to identify UAVs in motion, further complicating path planning. The main contributions of this article are as follows. First, we proposed a nonrigid hierarchical discrete grid structure and coding method for spatial three-dimensional grids and grid data model. This method can accomplish the aggregation of any adjacent eight grids to form multiple nonrigid structures, making the original fixed relationship between parent and child grids more flexible. Second, the nonrigid grid structure optimizes the identification ability of grid vertices, grid edges, and grid faces. It has eight times the identification ability of the rigid grid structure. We exploited this structure for UAV identification with the same size grid at any position of the grid structure. Third, based on the nonrigid grid structure, an improved UAV conflict detection method and path planning method are designed. Experimental results indicate that, on an average, the efficiency of the improved conflict detection is an order of magnitude higher than that of the traditional computational detection method. The improved path planning method reduces the path length by an average of 11% compared to the single-grid planning method, and is 3.85 times more efficient than the multi-grid buffer planning method.

Based on the characteristic of autonomous underwater vehicle path planning, the method of path planning was analyzed by genetic algorithm. Firstly, by means of grid, plan space was modeled into two markers. Best path was searched by genetic algorithm. Method of giving birth to initial groups was improved. Sufficiency function of path planning was given. Chamfer operator in genetic algorithm was imported to speed up the convergence. It guarantees the request of planning time. Through simulation experiment, the important function of chamfer operator was proved in path planning. The method can produce an almost best path for Autonomous underwater vehicle in short time.

In this paper, a novel global time-varying path planning (GTVP) method is proposed. In the method, real-time paths can be generated based on tunable Bezier curves, which can realize obstacle avoidance of manipulators. First, finite feature points are extracted to represent the obstacle information according to the shape information and position information of the obstacle. Then, the feature points of the obstacle are converted into the feature points of the curve, according to the scale coefficient and the center point of amplification. Furthermore, a Bezier curve representing the motion path at this moment is generated to realize real-time adjustment of the path. In addition, the 5-degree Bezier curve planning method consider the start direction and the end direction is used in the path planning to avoid the situation of abrupt change with oscillation of the trajectory. Finally, the GTVP method is applied to multi-obstacle environment to realize global time-varying dynamic path planning. Through theoretical derivation and simulation, it can be proved that the path planned by the GTVP method can meet the performance requirements of global regulation, real-time change and multi-obstacle avoidance simultaneously.

Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.