Real-Time Obstacle-Avoiding Path Planning for Mobile Robots (original) (raw)
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In this paper, we present an obstacle avoiding smooth path planning method based on Voronoi diagram and composite Bezier curve algorithm which obtains the curvature bounded path with small length. In our algorithm, a Voronoi diagram is constructed according to the global environment. The piecewise linear rough path in the Voronoi diagram which keeps away from the obstacles is obtained by performing Dijkstra's shortest path algorithm. Dynamic programming is employed to subdivide the nodes on the piecewise linear path into control point subsequences to generate a collision free composite Bezier curve which satisfies the curvature constraint and approaches minimal path length.
Navigation of Mobile Robot with Polygon Obstacles Avoidance Based on Quadratic Bezier Curves
Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2019
Navigation and obstacle avoidance problems are introduced and solved in this paper. A new algorithm called quadratic Bezier curves is used to navigate a mobile robot in an unknown environment. The quadratic Bezier curve can be improved through a division and conquer technique whose basic operation is the generation of multiple midpoints on a specific curve. The platform of the mobile robot is constructed with three ultrasonic sensors placed around the front of the platform with 45° between them. These sensors are utilized to sense the locations of obstacles in any environment surrounding the mobile robot. Based on sensor(s) detection for obstacles in the environment of the mobile robot, five different scenarios are introduced and studied. To navigate the robot in the unknown environment, two main points are taken into consideration: Firstly, the smooth rotation of the mobile robot around the obstacles should be performed to avoid a collision; and secondly, the shortest path should be followed by the robot to reach a target with minimum time. Visual basic language is used to simulate the navigation of mobile robot in different environments. The quadratic Bezier curves algorithm is investigated real-time experiment, and the obtained results have proved the efficiency and evaluation of the proposed algorithm.
Path Planning Optimization based on Bézier Curves through Open-doors Way Point
Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics, 2013
Generalized Voronoï Diagrams has been demonstrated to be a relevant tool for planification in a mobile robotics context. Therefore, the generated trajectories may suffer of discontinuities and non-optimality. This paper introduces a reflexion on the use of Bézier curves to solve both of these drawbacks. The key idea of this paper is to be able to smoothen a trajectory in order to save traveling time and therefore reduce displacement and overall consumption (in our mobile robotics context: reduction of battery usage and localization errors). The presented work is firstly detailed and explained on a synthetic map, and experimental results with mobile robots are presented. Disadvantages and advantages are discussed at the end of the paper.
Path planning and navigation using Voronoi diagram and fast marching
Eighth International IFAC Symposium on Robot Control, 2006, 2006
To navigate in complex environments, a robot needs to reach a compromise between the need for having efficient and optimized trajectories and the need for reacting to unexpected events. This paper presents a new sensor based non-holonomic Path Planner which integrates the global motion planning and local obstacle avoidance capabilities. In the first step the safest areas in the environment are extracted by means of a tube skeleton similar to a Voronoi diagram but with tubular shape. In the second step Fast Marching Method is applied to the tube skeleton extracted areas in order to obtain the best path in terms of smoothness and safety. At the same time, during the motion, the algorithm modifies the calculated path when it encounters obstacles or other unforeseen dynamic objects that can not be included in the a priori map. In this way the trajectory obtained is the shortest between the safe possible ones. The method combines map-based and sensor-based planning operations to provide a reliable motion plan, while it operates at the sensor frequency. The main characteristics are speed and reliability, because the map dimensions are reduced to an unidimensional map and this map represents the safest areas in the environment for moving the robot.
Improving Path Accuracy of Mobile Robots in Uncertain Environments by Adapted Bézier Curves
Electronics
An algorithm that presents the best possible approximation for the theoretical Bézier curve and the real path on which a mobile robot moves in a dynamic environment with mobile obstacles and boundaries is introduced in this paper. The algorithm is tested on a set of scenarios that comprehensively cover critical situations of obstacle avoidance. The selection of scenarios is made by deploying robot navigation performances into constraints and further into descriptive characteristics of the scenarios. Computer-simulated environments are created with dedicated tools (i.e., Gazebo) and modeling and programming technologies (i.e., Robot Operating System (ROS) and Python). It is shown that the proposed algorithm improves the performance of the path for robot navigation in a highly dynamic environment, with dense mobile obstacles.
Roadmap-Based Path Planning - Using the Voronoi Diagram for a Clearance-Based Shortest Path
IEEE Robotics & Automation Magazine, 2008
Using the Voronoi Diagram for a Clearance-Based Shortest Path T he path-planning problem was originally studied extensively in robotics, and, through this research, it has gained more relevance in areas such as computer graphics, simulations, geographic information systems (GIS), very-large-scale integration (VLSI) design, and games. Path planning still remains one of the core problems in modern robotic applications, such as the design of autonomous vehicles and perceptive systems [31], [39]. The basic pathplanning problem is concerned with finding a good-quality path from a source point to a destination point that does not result in collision with any obstacles. Depending on the amount of the information available about the environment, which can be completely or partially known or unknown, the approaches to path planning vary considerably. Also, the definition of a good-quality path usually depends on the type of a mobile device (a robot) and the environment (space), which has fostered the development of a rich variety of path-planning algorithms, each catering to a particular set of needs. Latombe [28] provides a comprehensive survey of different path-planning algorithms. Computational geometry plays a special role in path-planning developments. Extensive methodologies that rely on geometric representation of the space, reveal topological properties of the agents (robots and/ or obstacles), and allow the efficient dynamic position tracking and updates have been brought forward from computational geometry to solve a specific set of path-planning problems. Such problems usually have a well-defined and deterministic set of objectives, regular geometric space representation, and specific functions that discribe robotic movements. The problems also include planning a path and optimizing it (based on selected criteria such as the length, the smoothness, the cost, etc.) [7], solving problems involving evolutionary algorithms and swarm intelligence [2], and studying the behavior of a network of mobile robot agents [17]. The traditional computational geometry-based approaches to path planning can be classified into three basic categories: the cell decomposition method [35], the roadmap method [1], and the potential field method [40]. If robots are represented by polygonal objects, an approach based on the Minkowski sum is often used [24]. Both the cell decomposition and the roadmap methods along with the Minkowski sum method have their roots in computational geometry. The cell decomposition method uses nonoverlapping cells to represent the free-space (C f) connectivity. The decomposition can be exact or approximate. An exact decomposition divides C f exactly [4]. An approximation scheme Kambhampati discretizes C f with cells. It decomposes the free space recursively, stopping when a cell is entirely in C f or entirely inside an obstacle. Otherwise, the cell is further divided. Because of memory and time constraints, the recursive process stops when a certain degree of accuracy has been reached. The cell decomposition method, although simple to implement, seldom yields high-quality paths. The exact cell decomposition technique is faster than the
Robot motion planning using generalised voronoi diagrams
2008
In robot motion planning in a space with obstacles, the goal is to find a collision-free path of robot from the starting to the target position. There are many fundamentally different approaches, and their modifications, to the solution of this problem depending on types of obstacles, dimensionality of the space and restrictions for robot movements. Among the most frequently used are roadmap methods (visibility graphs, Voronoi diagrams, rapidly exploring random trees) and methods based on cell decomposition. A common feature of all these methods is the generating of trajectories composed from line segments. In this paper, we will show that generalised Voronoi diagrams can be used for fast generation of smooth paths sufficiently distant from obstacles.
This paper presents a new procedure for planning mobile robot trajectories by considering kinematic and dynamic constraints on the vehicle motion. This approach combines an original kinematic visibility graph planning method, an efficient path generation algorithm based on Beta-Spline curves and a cubic Spline speed profile definition technique. The maximum value of the curvature can be assured to be smaller than the value given by the constraints. Furthermore, speeds along the path are planned subject to the kinematic and dynamic constraints. The resulting trajectories provide ideal conditions for high precision path tracking and positioning. In the paper we present the application of the proposed methods to RAM-1, a mobile robot designed and built for indoor and outdoor industrial environment.
Mobile Robot Path Planning using Voronoi Diagram and Fast Marching
Robotics, Automation, and Control in Industrial and Service Settings
This chapter presents a new sensor-based path planner, which gives a fast local or global motion plan capable to incorporate new obstacles data. Within the first step, the safest areas in the environment are extracted by means of a Voronoi Diagram. Within the second step, the fast marching method is applied to the Voronoi extracted areas so as to get the trail. This strategy combines map-based and sensor-based designing operations to supply a reliable motion plan, whereas it operates at the frequency of the sensor. The most interesting characteristics are high speed and reliability, as the map dimensions are reduced to a virtually one-dimensional map and this map represents the safest areas within the environment.
O3: An optimal and opportunistic path planner (with obstacle avoidance) using voronoi polygons
2008 10th IEEE International Workshop on Advanced Motion Control, 2008
Traditional mobile robot research focuses on a robot navigating its environment to reach a single goal while avoiding obstacles. This paper proposes a new method called O 3 to solve the challenges presented at the Intelligent Ground Vehicle Competition (IGVC) where a navigation course includes multiple goals to be found in an optimal order. The O 3 technique includes improvements on traditional path planning and obstacle avoidance techniques while providing an explicit ability to change course as obstacles are discovered. This method uses modern trajectories such as minimum-weighted Hamiltonian circuits, A* algorithm for obstacle avoidance, and local points of opportunity to update the globally optimal path using Voronoi polygons. Environmental mapping is also used to speed up the search algorithms in static environments. Overall, the O 3 technique exploits local points of opportunity while avoiding obstacles and ultimately finding a globally optimal path through an unknown environment. This methodology will be implemented on an autonomous web-based tour guide robot to serve the Internet community reviewing Elizabethtown College. This methodology can be extended to other research areas where multiple locations need to be traversed independent of their order such as city map, trip planners, and distribution networks (power, internet, etc) due to its balance between weighted graphs and obstacle avoidance (objects, traffic, construction, etc).