Abstract : Motion planning is a fundamental problem in robotics and also arises in other applications including navigation, simultaneous localization and mapping (SLAM), animation etc., in the motion planning normally using many techniques and algorithms. In this paper for pathplanning using a novel pathplanning algorithm called as Dijkstra’s iteration (D*) inspired by the witkowski’s algorithm based on a two dimensional grid map of the Static environment designed in mat lab using bioinformatics tools.
(C. Shi et al, 2007) also proposed a Harmonic Potential Field Method (HPF) for Autonomous Ship Navigation. The main idea was adopted from (C.I Connolly et al, 1990). The APF had a major drawback “local minima” which relies on the configuration of obstacles and goal. To ensure the route of pathplanning was proper and collision free, the harmonic function was chosen to generate a free path collision solution. Harmonic functions were the solution to Laplace’s equation. This method had a credit over simple potential field as they were good at solving the local minima problems. However, HPF had a drawback which its computation time increased fast with the grid size and this method also decayed rapidly. This affected the pathplanning performance when the potential field regions grown.
Abstract. Assisting people with daily living tasks in their own homes with a robot requires a navigation through a cluttered and varying en- vironment. Sometimes the only possible path would be blocked by an obstacle which needs to be moved away but not into other obstructing regions like the space required for opening a door. This paper presents semantic assisted pathplanning in which a gridded semantic map is used to improve navigation among movable obstacles (NAMO) and partially plan simple household tasks like cleaning a carpet or moving objects to another location. Semantic planning allows the execution of tasks ex- pressed in human-like form instead of mathematical concepts like coor- dinates. In our numerical experiments, spatial planning was completed well within a typical human-human dialogue response time, allowing for an immediate response by the robot.
Traditionally, the robot scan path is either taught or programmed manually. Automation of NDT scan path generation, as presented in this paper, o ﬀ ers further signi ﬁ cant time reduction, and increase in ﬂ ex- ibility of inspection planning compared to manual robot teaching and programming. Moreover, such a solution helps to maintain a controlled probe orientation with respect to the scanned surface, and thus dra- matically reducing lift-o ﬀ noise. Scan pathplanning can be performed either using general purpose CAM software or proprietary tools devel- oped for particular NDT application . O ﬄ ine robot programming o ﬀ ers the robot programmer a number of key bene ﬁ ts, most notably a reduction in the time it takes to design and program an automated robotic system thus reducing production downtimes [15,16]. However, signi ﬁ cant errors can occur in an o ﬀ -line generated toolpath due to deviations of individual robot characteristics (machining tolerances in the robot linkages, compliance and elasticity in the robot arm, encoder resolution, and the lack of repeatability during calibration) from the general kinematic model .
In conclusion, a navigation system was developed in ROS for Milpet. The path plan- ning and obstacle avoidance systems have been created to work in sync with one another. Each system is modular, making it easy to modify and test new algorithms. Both systems, along with the localization system, have been written in Python, mak- ing it easier to debug and maintain in the future. The overall behavior of the robot can be controlled by changing the modes in the obstacle avoidance code. The path plan- ning code was adapted to encompass a few shortcomings of the localization system. Localization is based on image data, and the predicted location can jump between locations on the map. To make the pathplanning impervious to this, a count variable was added to filter out erroneous predictions.
Abstract. Users navigating in virtual environment normally suffers from getting lost and lost of interest. Furthermore in a large and complicated virtual environment, users are tending to plan their path and navigate in an automatic manner. This paper describes the result of a research project aimed to integrate a path-planning optimization-algorithm and produce an efficient tour in a virtual environment. The visitors can simply identify the locations they wish to visit and the system will automatically generate a tour plan and guide the user to walk through the desired locations. The results are presented using a 3D model of several different rooms and booths in a virtual exhibition area. This paper presents to the development of efficient virtual tour that later will contribute to an efficient design of the building or environment and also optimal solution of the path finding. We expect the result of this research project to be used as a new navigation assistance for visitors to plan their tours in a large exhibition area.
each set. In the next set of experiments, we compare the hybrid algorithms MMAS- IGA, IGA-MMAS, and GA-ACO, which is a hybrid of the traditional approaches. From the above set of experimental results, we summarize MMAS-IGA robot pathplanning has performed with improved path length and better time factor when com- pared with IGA-MMAS. Even though in most of the cases the near optimal path length obtained is similar for MMAS-IGA and IGA-MMAS in some cases the MMAS- IGA had near optimal path lengths lesser than that of IGA-MMAS and also the time taken for MMAS-IGA is lesser than that of IGA-MMAS. In the set-4 experiments there is a vast difference in the time taken than that of set-1 to set-3 experiments. This is because the IGA method has a OOA method which takes time in deducing the obstacle free paths for each scenario of the population followed by three cross over operations this is adding more time with the increases size of the maps. An impro- visation could be added to reduce this time as a part of future work in complicated maps.
Abstract—Discrete search and rescue pathplanning is known to be hard, and problem-solving techniques proposed so far mainly fail to properly assess optimality gap for practical size problems. A new mixed-integer linear programming (MIP) formulation is proposed to optimally solve the single agent discrete search and rescue (SAR) pathplanning problem. The approach lies on a compact open-loop SAR with anticipated feedback problem model to efficiently maximize cumulative probability of success in detecting a target. Anticipated feedback information resulting from possible observations outcomes along the path is exploited to update target occupancy beliefs. A network representation is utilized to simplify modeling, facilitate constraint specification and speed-up problem-solving. The proposed MIP approach rapidly yields optimal solutions for realistic problems using parallel processing CPLEX technology, while providing for the first time a robust upper bound on solution quality through Lagrangean integrality constraint relaxation. Fast computation naturally allows extending open-loop modeling to a closed-loop environment to progressively integrate real-time action outcomes as they occur on a rolling time horizon. Comparative performance results clearly show the value of the approach.
Pathplanning includes both point-to-point and region filling operations. Point-to-point pathplanning looks for the best route to move an entity from point A to point B while avoiding known obstacles in its path, not leaving the map boundaries, and not violating the entity's mobility constraints. This type of pathplanning is used in a large number of robotics and gaming applications such as finding routes for autonomous robots, planning the manipulator's movement of a stationary robot, or for moving robots to different locations in a map to accomplish certain goals in a gaming application. While point-to-point operations are appropriate for some applications, they do not always produce the desired route for tasks such as vacuuming a room, plowing a field, or mowing a lawn. These types of tasks require region filling pathplanning operations that are defined as follows:
In this paper we have discussed the pathplanning part of a total water-borne rescue system by comparing, simulating four pathplanning algorithms on grip based map for both one starting - one destination cell and one starting - multi destination cells. From the tabular and graphical results of the experiments and the inferences from the algorithms, we found some important information for pathplanning for maps with same cost cells, different cost cells and with one starting - one destination and one starting - multi destination cells. For maps with same cost cells, with one starting-one destination cell and multi destination cells, using Breadth-first algorithm is the best if the computational time is the primary desire criteria. But if the size of memory is the major criteria then using A* can be a better alternative. For maps with different cost cells and with one starting - one destination cell A* is best in both computational time and size of memory. But the heuristic cost function D for A* must be chosen in order to find the shortest and lowest cost path. Again for maps with different cost cells and with one starting-multi destination cells A* is best in computational time with no certainty for the shortest path. But it must be understood that Dijkstra, using visited cells advantage especially in enormous multi-destination cells and shortest path guarantee, can be a good choice for these maps.
Graph theoretic approaches plays important role in the pathplanning problems. The usefulness of Graph Theoretic approaches mainly the voronoi diagram is provided for the pathplanning in static and dynamic environment in this paper. Voronoi Diagram plays important role in computing the optimal path between the source & destination in static as well as dynamic environment. So, through the literature survey and a detailed description about the methods, we provide the significance of this graph theoretic approach of voronoi diagram in the robot motion planning.
Abstract - Pathplanning is a fundamental but an essential task in the field of robotics .The completion and correctness of a problem in planning a path depends upon the accuracy of the algorithms used. One category of algorithms used in Pathplanning has a great influence from nature and are known as bio-inspired algorithms. These algorithms imitate the nature for discovering the path in an artificial environment. Bio inspired algorithms in pathplanning has evoked large attention from research community in past two decades which can we seen by the way large number of algorithms are being developed today to improve and increase the efficiency of finding the path.
as wind gusts. Moreover one of the tasks in the project includes the aerial and hand deployment operation, where the UAV has to be deployed by a human operator or from an helicopter in overturned initial condition. For this task, the need arises for a glob- ally stabilizing controller able to execute acrobatic maneuvers as attitude recovery and to stabilize the system from any initial configuration, avoiding dangerous crash. On the pathplanning side, the requirements for the project are: real-time and low computation algorithm for real on-board application, a robust algorithm able to take into account disturbances and to plan safe paths accordingly and suitable to replanning because of the unknown environment. Moreover a general framework for multiple heterogeneous robots had to be develop and shared between different agents. Me and my group were responsible for the multi-propeller UAV and the ground rover, hence the focus of this thesis is on control and pathplanning with applications on those platform, but suitable for other mobile robots. The two models of interest will be used throughout the whole thesis for examples and applications.
In this thesis, the focus is to analyze different pathplanning algorithms which were developed over years and evaluate their strengths and weaknesses. The feedback control approaches to solve the pathplanning problem has usually failed. The main reason is that feedback control has traditionally approached this problem where the work space and environment is free of obstacles, or the obstacles are static. The attempts to provide a feedback control law in presence of obstacles are usually extremely limited in practice. This work will present such attempts and will discuss why they are so limited and then propose a set of feasible solutions that are relatively easy to implement as base path planner and then expand upon with a more sophisticated algorithm. The proposed algorithms do not depend on strong mathematical background and are fast enough to find collision free paths even in dynamic workspaces.
Pathplanning is a typical problem studied in many robotic domains, such as autonomous mobile robots, underwater vehicles, and unmanned aerial vehicles. In this paper, we seek to address the AUVs that are frequently used for ocean monitoring, information collection, marine resource exploration . The challenge issue for an AUV is that its endurance of task execution in ocean environments must be seriously taken into consideration . Due to the limited fuel or battery capacity, an AUV should efficiently use its energy while moving towards its target location. However, the ocean currents can disturb its path execution. Thus, when planning the feasible path, an approach must consider the existence of the ocean currents. Moreover, the ocean currents can amplify the actuation errors, which means that the trajectory points should be far away the dangerous zones.
a great deal of work has been done for GPUs are a few examples to name a few. These works analyze sources of bottlenecks and discuss ways to mitigate them. Summing up these works, we devise that most challenges remain in the fine- grain inner loops of pathplanning algorithms. We believe that analyzing and scaling pathplanning on single-chip setup can minimize the fine-grain bottlenecks. Since shared memory is efficient at the hardware level, we proceed with parallelization of the pathplanning workload for single-chip multi-cores. The single-chip parallel implementations can be scaled up at multiple nodes or clusters granularity, which we discuss.
In order to make pathplanning for AGV, it is primary to build the parking lot model that represents the roads, parking lots and other information. The parking map is showed in Figure 1 Generally, there are three methods to model, the visibility graph, the grid method and the topological method. And the last one is used commonly , because it is easy to implement and has a visual effect. However, it can’t reflect the relationship between two nodes in parking lots, and it saves less information about the environment, but the visibility graph algorithm can conquer these defects. So this paper uses the visibility graph algorithm to model. The model of the intelligent parking is mainly composed of the parking station and path information, and these information is modeled by the Class object of C++, so the Class objects of the parking station and the Class objects of the path are constructed.
performance of pathplanning. Literature  put forward to a method of smoothing the path, and effectively improved the quality of the path. At present, many algorithms have the disadvantages of easily trapping into the local optima and having a slow convergence speed in pathplanning for mobile robot. Because of Genetic algorithm has numerous of evolution algebra, it takes up a lot of storage space, resulting in slower speed. Although artificial fish algorithm has faster convergence speed, it is difficult to get accurate solutions, that is to say, the quality of path is non-ideal. Hybrid leapfrog algorithm easily fall into local optimum and have a slow convergence speed, thus to limiting the application of the algorithm which is used in pathplanning for mobile robot. Neural network algorithm has a stronger ability to learn, but it is difficult to use mathematical formula to describe it when the environment is complex. Artificial potential field method is widely used in pathplanning because of its simple mathematical calculations. But it has the disadvantage of a local minimum point and target unreachable. Ant colony algorithm is a kind of bionic algorithm. it has the advantages of self-organization, distributed computing and a strong optimization capability. Therefore, it is very suitable for application to pathplanning for mobile robot.
algorithms are given a graph containing many vertices, with some neighboring vertices to ensure connectivity, and are tasked with finding the shortest path from a given source vertex to a destination vertex. Parallel implementations assign a set of vertices or neighboring vertices to threads, depending on the parallelization strategy. These strategies naturally introduce input dependence. Uncertainty in selecting the subsequent vertex to jump to, results in low locality for data accesses. Moreover, threads converging onto the same neighboring vertex sequentialize procedures due to synchronization and communication. Partitioned data structures and shared variables ping-pong within on- chip caches, causing coherence bottlenecks. All these mentioned issues make parallel pathplanning a challenge. Prior works have explored parallel pathplanning problems from various architectural angles. Pathplanning algorithms have been implemented in graph frameworks. These distributed settings mostly involve large clusters, and in some cases smaller clusters of CPUs. However, these works mostly optimize workloads across multiple sockets and nodes, and mostly constitute either complete shared memory or message passing (MPI) implementations. In the