Path-planning modules for Autonomous Vehicles: Current status and challenges

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.

In the path planning of UAVs, autonomous decision-making and control are challenging tasks in the uncertain 3D environment consisting of static and dynamic obstacles. Hence, the selection of appropriate path-planning approaches is essential. In the proposed work, we have considered the meta-heuristic approaches only for an in-depth review. Metaheuristic approaches have been remarkably known for solving complex problems, optimal solutions, and lesser computational complexity compared to deterministic approaches that produce an inefficient solution. An in-depth review has been made by considering the approaches used for path planning, their advantages, disadvantages, applications, the type of time domain (offline or online), type of environment (simulation or real time), hybridization with other approaches, single or multiple UAV system, and obstacle handled (static or dynamic). It is observed that current meta-heuristic methods face constraints like inadequate convergence rates, entrapment in local optima, and complex operations, necessitating continuous development of novel approaches. Implementation of path-planning approaches are very much limited to simulation study over experimental analysis. Hybrid algorithms emerge as a potential solution for tackling these hurdles and optimizing UAV navigation, particularly in dynamic environments involving multiple UAVs. The paper highlights key research gaps, trends, along with prospects in the field of research.

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.

<div class="section abstract"><div class="htmlview paragraph">In this research, an optimal real-time trajectory planning method is proposed for autonomous ground vehicles in case of overtaking a moving obstacle. When an autonomous vehicle detects a moving vehicle ahead of it in a proper speed and distance and the braking is not efficient due to the lost of its kinematic energy, the autonomous vehicle decides to overtake the obstacle by performing a double lane-change maneuver. A two-phase nonlinear optimal problem is developed for generating the path for the overtaking maneuver. The cost function of the first phase is defined in such a way that the vehicle approaches the moving obstacle as close as possible. Besides, the cost function of the second phase is defined as the minimization of the sum of the vehicle lateral deviation from the reference path and the rate of steering angle during the overtaking maneuver while the lateral acceleration of the vehicle does not exceed a safe limit. For the real-time path planning of a vehicle with high speed, the result of solving the optimal path planning are stored in the look-up table form for offline use. A full nonlinear vehicle modeled in CarSim software is used for path tracking simulation. The simulation results show that the generated path for the autonomous vehicle satisfies all the vehicle dynamics constraints, therefore it is applicable for a real autonomous vehicle.</div></div>

Rapid path planner plays an important role in autonomous ground vehicle (AGV) operation. Depending on the non-holonomic kinematics constraints of AGV, its path planning problem is discussed. Since rapidly-exploring random tree (RRT) can directly take non-holonomic constraints into consideration, it is selected to solve this problem. By applying extra constraints on the movement, the generation of new configuration in RRT algorithm is simplified and accelerated. With section collision detection method applied, collision detection within the planer becomes more accurate and efficient. Then a new path planner is developed. This method complies with the non-holonomic constraints, avoids obstacles effectively and can be rapidly carried out while the vehicle is running. Simulation shows that this path planner can complete path planning in less than 0.5 s for a 170 m×170 m area with moderate obstacle complexity.

This paper proposes a new path planning approach based on statistical information, like the occupancy probability of partly or completely dynamic obstacles and/or the statistical flow of dynamic obstacles. With this new approach it is possible to integrate the expected behavior of moving obstacles into the preplanning of paths without knowing the individual movement of these dynamic obstacles. As the preplanned paths coincide with the average movement pattern in the environment, the runtime planning is simplified. The authors propose an appropriate world model to represent the necessary statistical data, which can be obtained automatically. In addition, a path planning approach, suited for point-shaped and arbitrary-shaped vehicles, is sketched. These paths integrate geometrical detours to fit the planned movements into the statistical movement pattern of the dynamic obstacles. >

In order to minimize the processing time for multiple robots to finish lots of tasks, an optimized multiple robots task allocation and path planning approach is proposed. In this approach, the optimized task sequence is first schemed on the assumption that all the tasks are processed by one robot. While scheming the task sequence. A star algorithm is employed to plan paths and calculate the path cost. Then, according to the number of the robots, the task sequence has been randomly divided into several task segments that will be assigned to robots. At last, the task number of each task segments are averaged according to the time each robot used, so that the optimized task allocation scheme is got. In this approach, not only multiple robot task allocation, but also the optimized path planning is realized to ensure that the robots can finish all the tasks in the shortest time. The experiments and simulation are provided to validate the effectiveness of the approach. The results show that the proposed approach can scheme optimized multiple robots task allocation and path planning scheme.

Recent Advances in Mobile Robotics 128 gradient field Unfortunately, path planning approach based on the function family of potential field cannot obtain the optimal objective function which, in turn, cannot guarantee the desired optimal trajectories. Additionally, some of them, for example, potential panel method and harmonic potential function, can plan a path for an autonomous vehicle, but the computing burdens is huge and real time performance hardly satisfies the practical requirement. Inspired by biological intelligence, many approaches, such as ant colony optimization (ACO) They mostly rely on the stochastic searching, known as non-deterministic algorithm. These methods will eventually find an optimal solution, but no estimate on the time of convergence can be given. Thus, it may take long even infinite time to find the best solution. Furthermore, all of them have a great number of parameters to tune and that is never an easy job particularly when users are short of prior knowledge. Some comparisons Another kind of method is mathematic programming. Based on the relative velocity coordinates (RVCs), linear programming (LP) and mixed integer linear programming (MILP) can be employed for path planning problem. For example, Wang et al. converted the 2D path planning of an unmanned underwater vehicle to constrained optimization or semiinfinite constrained optimization problem Zu et al. discussed the path planning in 2D and an LP method was proposed for the problem of dynamic target pursuit and obstacle avoidance This method tried to plan the variables of the linear acceleration and the angular acceleration of a ground vehicle. Schouwenaars et al. proposed a MILP formulation with receding horizon strategy where a minimum velocity and a limited turn rate of aircraft are constrained However, these results still focused on the two dimensions. In this chapter, we consider the same problem of TPOA but in three dimensions. To our problem, the uncertain environment has one target or some of them, denoted by G, and many obstacles O that are all velocity-changeable with their moving actions. The aim of the vehicle A is to find an optimal path for pursuing the target while, at the same time, avoiding collision threaten from obstacles. The position and velocity of movers, including the target and obstacles, are assumed known or estimated at current time. To be more specific, it is assumed that the noise contained in the data can be eliminated with certain filters. However, this paper has no intend to discuss the filtering algorithm for achieving the feasible data of on-board sensors. The trajectory from a start to a destination location typically needs to be computed gradually over time in the fashion of receding horizon The target and obstacles, probably static or dynamic, are modeled by spheres having a certain radius for their impact area, and the vehicle is modeled by a mass point. In order to optimize the vehicle's acceleration online, we construct a linear programming model in Cartesian orthogonal coordinates based on relative velocity space This chapter is organized as follows. First, the relative velocity coordinates are introduced in both two dimensions and three dimensions and then the TPOA principles are introduced, including obstacle-avoidance and target-pursuit principles. The formulation of the standard linear programming is mathematically introduced in Section 3.1, and the path planning model is described in detail in Section 3.2. Simulations of path planning in 2D and 3D are www.intechopen.com Path Planning of Mobile Robot in Relative Velocity Coordinates 129 A, G, O Vehicle, target, obstacle Planning period

Autonomous ground vehicles (AGVs) are of major importance in exploration missions since they perform difficult tasks in changing or harmful environments. Mapping and exploration is crucial in hazardous areas, or areas inaccessible to humans, demanding autonomous navigation. This paper proposes a lightweight, low-cost AGV platform, which will be used in resource-constrained situations and aimed at scenarios like exploration missions (e.g., cave interiors, biohazard environments, or fire-stricken buildings) where there are serious security threats to humans. The proposed system relies on simple ultrasonic sensors when navigating and applied traversal algorithms (e.g., BFS, DFS, or A*) during path planning. Since on-board microcomputers have limited memory, the traversal data and direction decisions are stored in a file located on an SD card, which supports long-term, energy-saving navigation and risk-free backtracking. A fish-eye camera set on a servo motor captures three photos ordered from left to right and stores them on the SD card for further off-line processing, integrating each frame into a low-frame-rate video. Moreover, when the battery level falls below 50%, the exploration path does not extend further and the AGV returns to the base station, thus combining a secure backtracking procedure with energy-efficient decisions. The resultant platform is low-cost, modular, and efficient at augmenting; thus it is suitable for exploring missions with applications in search and rescue, educational robotics, and real-time applications in low-infrastructure environments.

Three-dimensional path planning for autonomous underwater vehicles based on a whale optimization algorithm

Integrating self-health awareness in autonomous systems

Shinde, Sonali Mane, Sunil B.An autonomous vehicle can operate itself without any human interaction through the capacity to sense its surroundings. It requires a navigation plan to travel avoiding collision and following all the traffic rules at the same time. Navigation issue is a computational issue to discover the succession of legitimate designs that move the object from source to destination. In the robotics field, this term is known as motion planning or path planning. There exist well-recognized methodologies for this problem; however, by applying some helpful heuristics, a better version of driving API can be designed. This study develops a complete software architecture needed for autonomous vehicles. It gives brief insights about available techniques for each module involved in motion planning and possible optimizations to achieve better results. The global planner uses a road network stored in open drive format to find the most eligible global path for ego vehicle. Whereas reactive local planner uses surrounding information to plan better paths avoiding static and dynamic obstacles. At last, the results are introduced in examination with existing methods to show improved measurements accomplished by this framework.

This paper discusses some of the current state-of-the-art and remaining challenges in research on path planning and vehicle control of autonomous vehicles. Reliable path planning is fundamental for the proper operation of an autonomous vehicle. Typically, the path planner relies on an incomplete model of the surroundings to generate a reference trajectory, used as input to a vehicle controller that tracks this reference trajectory. Depending on how much complexity is put into the path-planning block, the path planning and vehicle-control blocks can be viewed as independent of each other, connected to each other, or merged into one block. There are several types of path-planning techniques developed over the last decades, each with its own set of benefits and drawbacks. We review different techniques for path planning and trajectory tracking, and give examples of its use in relation to autonomous vehicles. We report on our own recent findings and give an outlook on potential research directions.

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.

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.