Path planning and integrated collision avoidance for autonomous vehicles

Path planning and formation control are two of the most significant concepts which can be considered in multi-vehicle systems and particularly in autonomous underwater vehicles (AUVs). The cooperative implementation of complicated commands would lead to desirable results and increase the probability of success in the missions. Due to the nonlinear dynamics and environmental conditions, the cooperative control of AUVs is a challenging topic. The developments in AUV applications demonstrate the significance of research and development in path planning and formation control. Unlike ground or aerial autonomous vehicles, this field of study has not attracted considerable attention and further exploration is required as a result. The present paper reviews the different structures of formation control in AUVs and discusses their advantages and disadvantages. Besides formation control, the cooperative path planning of AUVs along with the limitations specific to the cooperative structure is taken into consideration in the present study. Moreover, avoiding any obstacle collision and preventing any encounter between group members are considered as critical issues in the formation control and cooperative path planning. Some areas are still open to investigation as implied by the technological suggestions, which will facilitate future research. At the end of the article, a simulated sample is given of the triangular formation path planning for AUVs.

The path planning of autonomous land vehicle has become a research hotspot in recent years. In this article, we present a novel path planning algorithm for an autonomous land vehicle. According to the characteristics of autonomous movement towards the autonomous land vehicle, an improved A-Star path planning algorithm is designed. The disadvantages of using the A-Star algorithm for path planning are that the path planned by the A-Star algorithm contains many unnecessary turning points and is not smooth enough. Autonomous land vehicle needs to adjust its posture at each turning point, which will greatly waste time and also will not be conducive to the motion control of autonomous land vehicle. In view of these shortcomings, this article proposes a new heuristic function combined with the artificial potential field method, which contains both distance information and obstacle information. Our proposed algorithm shows excellent performance in improving the execution efficiency and reducing the number of turning points. The simulation results show that the proposed algorithm, compared with the traditional A-Star algorithm, makes the path smoother and makes the autonomous land vehicle easier to control.

Abstract: Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.

In recent times Unmanned Aerial Vehicle developed promptly and broadly used in many areas like reconstruction rescue and monitoring. In all the areas one important key challenge is path planning which tells UAVs that where to fly and how to find a path. The Multiple consideration is to be considered for an ideal path planning which includes path completeness, optimality. The execution of path planning becomes a challenging task in terms of trajectory generation and also require careful consideration. Unmanned Aerial Vehicle (UAV) path planning techniques have been extensively studied and involve two key stages. First, 3-D objects and obstacles (static and dynamic) are identified. Second, these objects and obstacles are mapped to plan an appropriate path. In the context of UAVs, we can explain path planning techniques in terms of roadmaps/Voronoi diagrams (a representation technique), or we can adopt an optimization approach using either cooperative or non-cooperative techniques. These two approaches, and the techniques used in each, are summarized in terms of the key factors that have been discussed in the context of UAVs. This paper, enables future studies to easily compare with existing and previous studies.

Recent advancements have been made in unmanned freight systems at ports, effectively improving port freight efficiency and being widely promoted and popularized in the field of cargo transportation in major ports around the world. The path planning and motion control of port transfer vehicles are the key technology for automatic transportation of vehicles. How to integrate cutting-edge unmanned driving control technology into port unmanned freight transportation and improve the level of port automation is currently an important issue. This article introduces the three-layer control operation architecture of unmanned freight systems in ports, as well as the challenges of applying path planning and motion control technology for unmanned freight vehicles in port environments. It focuses on the mainstream algorithms of path planning and motion control technology, introduces their principles, provides a summary of their current development situation, and elaborates on the improvement and integration achievements of current researchers on algorithms. The algorithms are reviewed and contrasted, highlighting their respective strengths and weaknesses. Finally, this article looks ahead to the development trend of unmanned cargo transportation in ports and provides reference for the automation and intelligent upgrading of unmanned cargo transportation in ports in the future.

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.

Path planning plays a vital role in ensuring the efficient and safe operation of off-road autonomous ground vehicles (AGVs). Current methods mostly focus on minimizing the travel time or path length of AGVs and often overlook the fact that the AGVs could fail in many ways during the operation due to stochastic and rough terrain conditions. The objective of this article is to not only generate a proper path for the vehicle but also ensure that the planned path is overall reliable and safe for the vehicle with complex terrain conditions. To achieve this goal, this article develops a reliability-based mission planning method for off-road AGVs subject to two failure modes in terms of mobility (the maximum attainable speed and vehicle vertical acceleration) induced by the uncertain ground properties of the terrain. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility for any given terrain conditions of a path. Mobility reliability of an AGV is then analyzed using surrogate modeling methods considering uncertainty sources in the off-road terrain conditions. After that, the reliability constraints for the two failure modes are integrated with the Rapidly-exploring Random Tree Star (RRT*) algorithm to identify an optimal path, which is the shortest path while satisfying the reliability requirements of the two considered failure modes. Results of a case study demonstrated the effectiveness of the proposed methods for path planning with the consideration of uncertainty in the deformable terrain.

Abstract. Urban traffic congestion, obstacle avoidance, and driving efficiency are the challenges faced by autonomous-vehicle path-planning technology in cities. The traditional artificial potential field (APF) algorithm is insufficient to meet the requirements of efficiency and safety in path planning, as it easily gets trapped in local optima when dealing with complex environments. Therefore, this paper proposes a novel AS-IAPF path-planning algorithm to more efficiently enhance the target reachability of autonomous vehicles in complex traffic environments. Firstly, this paper analyzes and elucidates the macroscopic traffic model, achieving effective modeling of dynamic traffic flow stability based on Lyapunov stability theorem and a classical 1D flow model. Thus, the threshold discriminant formula for traffic element stability is obtained. Secondly, based on the aforementioned threshold discriminant formula, a new AS-IAPF algorithm is proposed. The algorithm mainly includes two aspects: firstly, by pre-generating initial paths and introducing a Gaussian oscillation coefficient of force fields, it avoids the algorithm falling into local optima; secondly, by using the aforementioned driving stability threshold discriminant formula as a dimensional adjustment for adaptively improving and adjusting the strength coefficient of the AS-APF repulsive field, the algorithm further improves the efficiency of path planning. Finally, the algorithm is subjected to joint simulations of 2D and 3D scenarios of different types. The research results show that the AS-IAPF algorithm outperforms other algorithms of the same type with respect to comprehensive performance based on multiple 2D scenario simulation experiments. In the 3D simulation experiments of three different typical traffic scenarios, the proposed algorithm can drive autonomous vehicles to effectively perform corresponding obstacle avoidance actions based on the actual traffic scenarios ahead, ultimately achieving safe obstacle avoidance. The path-planning method proposed in this paper can enhance driving efficiency while considering the safety and stability of vehicles, providing a promising approach and reference for the path planning of autonomous vehicles.

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.

A detailed survey of the available literature on path planning of Autonomous Ground Vehicle (AGV) is conducted, including the overview of single-robot control architectures, different path-planning approaches, analyses of current sensor systems and different velocity estimation techniques. In order to achieve the full autonomous operation of a mobile robot, path or motion planning, i.e., the planning of a collision-free path from a start to goal position through a collection of obstacles, is the fundamental task in the field of autonomous control systems. As AGVs are used in a wide variety of applications to perform autonomous tasks, organising their intelligence plays a key role in successfully programming a robot for a particular application and applying the right control architecture makes the autonomous control problem easier to solve. For autonomous vehicles, sensors play an important role in acquiring different attributes of the working environment and, by extracting meaningful information from these data, the autonomous system can acquire knowledge about its environment. Moreover, different velocity estimation techniques are reviewed in the context of dealing with the dynamic obstacles. A brief review of the available literature on path-planning approaches and techniques is provided.

Path planning is one of the key technologies for autonomous vehicles. Ant Colony Algorithm can effectively achieve the goal of path planning for autonomous vehicles, but the algorithm has the problems of low search efficiency and local optimal solution in path planning. Therefore, this paper improves the classical ant colony algorithm, using adaptive initial pheromone distribution range build initial pheromone distribution, at the same time improve stimulating factor enhanced heuristic search efficiency, and introduces the rollback strategy self-locking and deadlock problem and adopt preferential set limit to update pheromone strategy, help reduce blind ant search path, and reduce the redundancy of map information. The simulation results show that the improved ant colony algorithm can greatly improve the global search ability and convergence speed, and can help the autonomous vehicle to find the optimal path quickly.

Path planning on satellite images for unmanned surface vehicles

Obstacle avoidance problem for the autonomous vehicle has received more and more attention these years, especially for the autonomous heavy vehicle, which has the high gravity center, large carrying capacity and promising commercial prospect. However, the vehicle dynamic properties have been rarely considered for path planning, which is a crucial factor for driving safety. An integrated collision avoidance framework that consists of a new path planner and a tracking controller considering multiple dynamic constraints is proposed for an autonomous multi-axis distributed vehicle. In the upper level, a new optimal path generator based on the B-splines is designed for safety and stability purposes, and the collision avoidance path is optimized considering multiple dynamic constraints, including the tire nonlinear dynamic, lateral stability limits and the anti-slip constraints. Meanwhile, a safety assessment evaluator according to the time to collision calculation is designed for risk level recognition and path planning weight determination. In the lower level, a path tracking controller based on the nonlinear model predictive control (NMPC) is designed to improve the control accuracy, and a high-fidelity nonlinear dynamic predictive vehicle model with 16 degrees of freedom is established to depict the vehicle dynamic properties comprehensively. To solve the nonlinear optimization problem and get the optimal control steering angle output, an adaptive weight adjustment strategy and a varying predictive duration method are formulated. The optimization constraints related to steering actuator ability, plane stability limits, rollover prevention thresholds and tire adhesion requirements are constructed and converted as the bounds of the vehicle dynamic states. To verify the proposed framework, the simulation and HIL test platform are built and different driving scenarios are validated. The test results show the satisfactory performance of path planning and path tracking considering the dynamic constraints for safety and stability

Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities.

The path planning and tracking control scheme for unmanned vehicle using multi-dimensional Taylor network (MTN) is proposed. First, the reference path is designed by the A-star algorithm, which is a common method used as path planning. Second, design the MTN controller based on PID controller to control the unmanned vehicle tracking the reference path; and the improved BP algorithm is used as the learning of MTN controller. Finally, the simulation experiment of unmanned vehicle is given to verify the effectiveness of the proposed scheme. The simulation results show that the proposed scheme has optimal path planning and good tracking control performance.