Path Planning for Mobile Robot Navigation using Voronoi Diagram and Fast Marching

For navigation in complex environments, a robot need s to reach a compromise between the need for having efficient and optimized trajectories and t he need for reacting to unexpected events. This paper presents a new sensor-based Path Planner w hich results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Vorono i Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted a reas in order to obtain the path. The method combines map-based and sensor-based planning o perations to provide a reliable motion plan, while it operates at the sensor frequency. The m ain characteristics are speed and reliability, since the map dimensions are reduced to an almost uni dimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments wher e other methods of planning based on Voronoi do not work.This work has been supported by the CAM Project S2009/DPI-1559/ROBOCITY2030 I

3D robot formations path planning with fast marching square

This work presents a path planning algorithm for 3D robot formations based on the standard Fast Marching Square (FM2) path planning method. This method is enlarged in order to apply it to robot formations motion planning. The algorithm is based on a leader-followers scheme, which means that the reference pose for the follower robots is defined by geometric equations that place the goal pose of each follower as a function of the leader’s pose. Besides, the Frenet-Serret frame is used to control the orientation of the formation. The algorithm presented allows the formation to adapt its shape so that the obstacles are avoided. Additionally, an approach to model mobile obstacles in a 3D environment is described. This model modifies the information used by the FM2 algorithm in favour of the robots to be able to avoid obstacles. The shape deformation scheme allows to easily change the behaviour of the formation. Finally, simulations are performed in different scenarios and a quantitative analysis of the results has been carried out. The tests show that the proposed shape deformation method, in combination with the FM2 path planner, is robust enough to manage autonomous movements through an indoor 3D environment.Acknowledgments This work is funded by the project num ber DPI2010-17772, by the Spanish Ministry of Science and Innovation, and also by RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and co-funded by Structural Funds of the EU.Publicad

Motion planning and control of redundant manipulators for dynamical obstacle avoidance

none2noThis paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles detected before the robot starts to move; this is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; this is realized using a velocity control law based on the null space method for redundancy control. Furthermore, an additional term of the control law is introduced which takes into account the speed of the obstacles, as well as their position. In order to test the algorithms, a set of simulations are presented: The redundant collaborative robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. The simulated data show that the proposed method for the smoothing of the trajectory can give a reduction of the angular accelerations of the motors of the order of 90%, with an increase of less than 15% of the calculation time. Furthermore, the dependence of the on-line control law on the speed of the obstacle can lead to reductions in the maximum speed and acceleration of the joints of approximately 50% and 80%, respectively, without significantly increasing the computational effort that is compatible for transferability to a real system.openPalmieri G.; Scoccia C.Palmieri, G.; Scoccia, C

Graceful Navigation for Mobile Robots in Dynamic and Uncertain Environments.

The ability to navigate in everyday environments is a fundamental and necessary skill for any autonomous mobile agent that is intended to work with human users. The presence of pedestrians and other dynamic objects, however, makes the environment inherently dynamic and uncertain. To navigate in such environments, an agent must reason about the near future and make an optimal decision at each time step so that it can move safely toward the goal. Furthermore, for any application intended to carry passengers, it also must be able to move smoothly and comfortably, and the robot behavior needs to be customizable to match the preference of the individual users. Despite decades of progress in the field of motion planning and control, this remains a difficult challenge with existing methods. In this dissertation, we show that safe, comfortable, and customizable mobile robot navigation in dynamic and uncertain environments can be achieved via stochastic model predictive control. We view the problem of navigation in dynamic and uncertain environments as a continuous decision making process, where an agent with short-term predictive capability reasons about its situation and makes an informed decision at each time step. The problem of robot navigation in dynamic and uncertain environments is formulated as an on-line, finite-horizon policy and trajectory optimization problem under uncertainty. With our formulation, planning and control becomes fully integrated, which allows direct optimization of the performance measure. Furthermore, with our approach the problem becomes easy to solve, which allows our algorithm to run in real time on a single core of a typical laptop with off-the-shelf optimization packages. The work presented in this thesis extends the state-of-the-art in analytic control of mobile robots, sampling-based optimal path planning, and stochastic model predictive control. We believe that our work is a significant step toward safe and reliable autonomous navigation that is acceptable to human users.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120760/1/jongjinp_1.pd

Motion planning and obstacle avoidance for mobile robots in highly cluttered dynamic environments

After a quarter century of mobile robot research, applications of this fascinating technology appear in real-world settings. Some require operation in environments that are densely cluttered with moving obstacles. Public mass exhibitions or conventions are examples of such challenging environments. This dissertation addresses the navigational challenges that arise in settings where mobile robots move among people and possibly need to directly interact with humans who are not used to dealing with technical details. Two important aspects are solved: Reliable reactive obstacle avoidance to guarantee safe operation, and smooth path planning that allows to dynamically adapt environment information to the motion of surrounding persons and objects. Given the existing body of research results in the field of obstacle avoidance and path planning, which is reviewed in this context, particular attention is paid to integration aspects for leveraging advantages while compensating drawbacks of various methods. In particular, grid-based wavefront propagation (NF1 and fast marching level set methods), dynamic path representation (bubble band concept), and high-fidelity execution (dynamic window approach) are combined in novel ways. Experiments demonstrate the robustness of the obstacle avoidance and path planning systems

Multi-resolution mapping and planning for UAV navigation in attitude constrained environments

In this thesis we aim to bridge the gap between high quality map reconstruction and Unmanned Aerial Vehicles (UAVs) SE(3) motion planning in challenging environments with narrow openings, such as disaster areas, which requires attitude to be considered. We propose an efficient system that leverages the concept of adaptive-resolution volumetric mapping, which naturally integrates with the hierarchical decomposition of space in an octree data structure. Instead of a Truncated Signed Distance Function (TSDF), we adopt mapping of occupancy probabilities in log-odds representation, which allows representation of both surfaces, as well as the entire free, i.e.\ observed space, as opposed to unobserved space. We introduce a method for choosing resolution -on the fly- in real-time by means of a multi-scale max-min pooling of the input depth image. The notion of explicit free space mapping paired with the spatial hierarchy in the data structure, as well as map resolution, allows for collision queries, as needed for robot motion planning, at unprecedented speed. Our mapping strategy supports pinhole cameras as well as spherical sensor models. Additionally, we introduce a first-of-a-kind global minimum cost path search method based on A* that considers attitude along the path. State-of-the-art methods incorporate attitude only in the refinement stage. To make the problem tractable, our method exploits an adaptive and coarse-to-fine approach using global and local A* runs, plus an efficient method to introduce the UAV attitude in the process. We integrate our method with an SE(3) trajectory optimisation method based on a safe-flight-corridor, yielding a complete path planning pipeline. We quantitatively evaluate our mapping strategy in terms of mapping accuracy, memory, runtime performance, and planning performance showing improvements over the state-of-the-art, particularly in cases requiring high resolution maps. Furthermore, extensive evaluation is undertaken using the AirSim flight simulator under closed loop control in a set of randomised maps, allowing us to quantitatively assess our path initialisation method. We show that it achieves significantly higher success rates than the baselines, at a reduced computational burden.Open Acces

Safe and Efficient Navigation of a Mobile Robot: Path Planning Based on Hierarchical Topology Map and Motion Planning with Pedestrian Behavior Model

Safety in mobile robot navigation is an essential aspect, but it is often accompanied by a trade-off of efficiency in different navigation steps. For global planning, safety is frequently handled by inflated obstacles. Larger margins to the obstacles increase safety while lessening the path efficiency, such as longer path length. For local motion planning tasks, safety becomes even more critical because of dynamic objects in the robot's proximity but also accompanies substantial trade-offs, such as highly low-speed operations. In such aspects of safety and efficiency, we propose safe and efficient global path planning and local motion planning methods. For global path planning, we proposed the allowable speed of navigation for safety, which limits the maximum speed based on the clearances in the environment. The corresponding cost formulates the traveling time, leading to planning a time-efficient global path. A new map representation is proposed, named Hierarchical Topology Map (HTM) and Hierarchical Topology Map with Explicit Corridor (HTM-EC), and incorporated with the proposed safety-aware navigation speed. HTM is a double-layered data structure of a topology graph and the corresponding metric skeleton points of a map, which returns a feasible, on-skeleton path in an extremely short time. HTM-EC is an extended map expression with the Explicit Corridor that incorporates the nearest obstacle points along with skeleton points into HTM, which returns an optimal, off-skeleton (i.e., metric) path with corridor optimization of the given on-skeleton path. When they are used together, safety-aware time-efficient paths can be planned with light computation. The efficacy of the safety-aware allowable speed and lighter computations have been verified with simulations and experiments. For local motion planning, we incorporate a pedestrian navigation model to address efficiency. The pedestrian model seeks the desired direction of motion that minimizes the remaining distance to the destination. The cognitive collision locations are computed, which enables far-sighted path planning by incorporating future configurations of the surroundings. Safety guarantee is analyzed for the output of the pedestrian-model-based motion planner. Finally, to address the behavioral uncertainties, the degree of cooperation (DoC) is proposed that helps prediction of the other objects' velocities with online estimation. The simulation results with the different number of agents show that the pedestrian model can generate a smooth path between the agents. As the local planner uses fixed-length visual measurement such as LiDAR, the proposed planner is scalable regardless of the number of moving obstacles as well as the types of obstacles (i.e., static or dynamic)

Fast Marching Methods in path and motion planning: improvements and high-level applications

Mención Internacional en el título de doctorPath planning is defined as the process to establish the sequence of states a system must go through in order to reach a desired state. Additionally, motion planning (or trajectory planning) aims to compute the sequence of motions (or actions) to take the system from one state to another. In robotics path planning can refer for instance to the waypoints a robot should follow through a maze or the sequence of points a robotic arm has to follow in order to grasp an object. Motion planning is considered a more general problem, since it includes kinodynamic constraints. As motion planning is a more complex problem, it is often solved in a two-level approach: path planning in the first level and then a control layer tries to drive the system along the specified path. However, it is hard to guarantee that the final trajectory will keep the initial characteristics. The objective of this work is to solve different path and motion planning problems under a common framework in order to facilitate the integration of the different algorithms that can be required during the nominal operation of a mobile robot. Also, other related areas such as motion learning are explored using this framework. In order to achieve this, a simple but powerful algorithm called Fast Marching will be used. Originally, it was proposed to solve optimal control problems. However, it has became very useful to other related problems such as path and motion planning. Since Fast Marching was initially proposed, many different alternative approaches have been proposed. Therefore, the first step is to formulate all these methods within a common framework and carry out an exhaustive comparison in order to give a final answer to: which algorithm is the best under which situations? This Thesis shows that the different versions of Fast Marching Methods become useful when applied to motion and path planning problems. Usually, high-level problems as motion learning or robot formation planning are solved with completely different algorithms, as the problem formulation are mixed. Under a common framework, task integration becomes much easier bringing robots closer to everyday applications. The Fast Marching Method has also inspired modern probabilistic methodologies, where computational cost is enormously improved at the cost of bounded, stochastic variations on the resulting paths and trajectories. This Thesis also explores these novel algorithms and their performance.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Antonio Giménez Fernández.- Vocal: Isabel Lobato de Faria Ribeir

Coverage Path Planning with Real‐time Replanning and Surface Reconstruction for Inspection of Three‐dimensional Underwater Structures using Autonomous Underwater Vehicles

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113717/1/rob21554.pd