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Autonomous Robots: Modeling, Path Planning, And...

Abstract:Providing mobile robots with autonomous capabilities is advantageous. It allows one to dispense with the intervention of human operators, which may prove beneficial in economic and safety terms. Autonomy requires, in most cases, the use of path planners that enable the robot to deliberate about how to move from its location at one moment to another. Looking for the most appropriate path planning algorithm according to the requirements imposed by users can be challenging, given the overwhelming number of approaches that exist in the literature. Moreover, the past review works analyzed here cover only some of these approaches, missing important ones. For this reason, our paper aims to serve as a starting point for a clear and comprehensive overview of the research to date. It introduces a global classification of path planning algorithms, with a focus on those approaches used along with autonomous ground vehicles, but is also extendable to other robots moving on surfaces, such as autonomous boats. Moreover, the models used to represent the environment, together with the robot mobility and dynamics, are also addressed from the perspective of path planning. Each of the path planning categories presented in the classification is disclosed and analyzed, and a discussion about their applicability is added at the end.Keywords: guidance; autonomy; vehicle; survey; trajectory; route; graph search; sampling; wheeled

Autonomous Robots: Modeling, Path Planning, and...


Abstract:A digital twin describes the virtual representation of a real process. This twin is constantly updated with real data and can thus control and adapt the real model. Designing suitable digital twins for path planning of autonomous robots or drones is often challenging due to the large number of different dynamic environments and multi-task and agent systems. However, common path algorithms are often limited to two tasks and to finding shortest paths. In real applications, not only a short path but also the width of the passage with a path as centered as possible are crucial, since robotic systems are not ideal and require recalibration frequently. In this work, so-called homotopic shrinking is used to generate the digital twin, which can be used to extract all possible path proposals including their passage widths for 2D and 3D environments and multiple tasks and robots. The erosion of the environment is controlled by constraints such that the task stations, the robot or drone positions, and the topology of the environment are considered. Such a deterministic path algorithm can flexibly respond to changing environmental conditions and consider multiple tasks simultaneously for path generation. A distinctive feature of these paths is the central orientation to the non-passable areas, which can have significant benefits for worker and patient safety. The method is tested on 2D and 3D maps with different tasks, obstacles, and multiple robots. For example, the robust generation of the digital twin for a maze and also the dynamic adaptation in case of sudden changes in the environment is covered. This variety of use cases and the comparison with alternative methods result in significant advantages, such as high robustness, consideration of multiple targets, and high safety distances to obstacles and areas that cannot be traversed. Finally, it was shown that the environment for the digital twin can be reduced to reasonable paths by constrained shrinking, both for real 2D maps and for complex virtual 2D and 3D maps.Keywords: path planning; digital twin; skeletonization; thinning; shrinking; autonomous mobile robots; drones

Changing industrial environments like flexible manufacturing facilities and automated warehouses where robots are intended to work side by side with humans are benefiting directly from advancements in complex path planning and autonomous decision making based on AI-powered algorithms. On the consumer side, applications like cleaning robots and delivery robots are also becoming part of our daily lives. The implementation of AI-powered path planning and control algorithms drastically improves the efficiency and practicality of these robots, as the environments in which these robots must operate is highly dynamic and needs constant adaptation.

Path planning lets an autonomous vehicle or a robot find the shortest and most obstacle-free path from a start to goal state. The path can be a set of states (position and/or orientation) or waypoints. Path planning requires a map of the environment along with start and goal states as input. The map can be represented in different ways such as grid maps, state spaces, and topological roadmaps. Maps can be multilayered for adding bias to the path.

Path planning, along with perception (or vision) and control systems, comprise the three main building blocks of autonomous navigation for any robot or vehicle. Path planning adds autonomy in systems such as self-driving cars, robot manipulators, unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs).

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment.

When searching path based on graph, the ability of the search algorithms that are widely used has been shown in mobile robot path planning. RRT is to perform forward search in continuous coordinates [7]. This algorithm can search rapidly, but when the environment is complex, this algorithm cannot be widely used in the narrow passages. A algorithm is effective to find a shortest obstacle-free path on basis of a certain decision criteria [8], but the generated path is always composed of straight lines that are hard to execute. D algorithm has been proposed with the aim of navigating autonomous vehicles in the environment of 2D, and the main advantage of D method is that it can find an optimal path when navigating in dynamic environment [9]. This method is often constrained with vehicle kinematic.

In simulations or simple environments, the path planning methods developed by scholars are easy to handle the navigation problem. Despite the fact that doable paths can be found by using these traditional methods, it is difficult to meet the requirement of the involved vehicle dynamic and fluxional constraints. In complex dynamic environment, when carrying out the path planning method, the following must be considered: (1) The path is often planned once. The path is not suitable when change occurs in dynamic environment, and the changed environment cannot influence the performance of vehicle. (2) To ensure safety, a secure clearance barrier must be generated and verified. (3) There is a real-time performance of planning method. (4) The maximum curvature of generated smooth path must be less than the steering curvature of vehicle. (5) The execution error of control leads to the failure of planning, and the path must be trackable [19].

The rest of this paper is organized as follows: Section 2 briefly introduces the system architecture, especially the two-layered planning model framework for autonomous vehicle. Section 3 proposes the improved Bi-RRT planning method in high-level model. Section 4 describes the planning method in low-level model; in this section, a VFH guided polynomial planning method based on Frenet coordinates is detailed. The simulation of two-layered path planning model is shown in Section 5. The description of experiments, results, and future work are provided in Section 6. Finally, the conclusions are given in Section 7.

The nonholonomic constraint of autonomous vehicle must be considered in complex environment, owing to the fact that there are always some tortuous lines in final path. Once the curvature of path exceeds the maximum curvature of vehicle, the control system is hard to deal with the path tracking problem. Observing Figure 6, it is a simplified model of vehicle. The kinematics equation can be described as follows and Table 1 shows the parameters of vehicle.

Based on the above analysis of vehicle path planning problem, this paper proposes a set of path planning algorithms suitable for autonomous vehicles based on the RRT framework. The steps of the algorithm are shown as the improved Bi-RRT algorithm. There are two main improvements in the algorithm, as Figure 7 shows. First, the sampling space is limited by the vehicle steering constraint, which can avoid searching in the entire sampling space, thus speeding up the convergence speed of the algorithm. The second point is to restrict the generation of nodes according to the steering constraints, so that the generated paths are more in line with the dynamic constraints of the vehicle. 041b061a72


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