aforementioned work focus on feasibility planning for high complexity problems involving object manipulation. However, far fewer works have applied CTMP concepts to planning for lower-complexity problems consisting of mobilerobots. Yet CTMP can provide improvements to the optimality of long mission plans, or adapt task plans in response to failures or perceived dynamic changes to the world. We highlight the UP2TA framework , which integrates task and pathplanning for applications to exploration mission planning. However, UP2TA does not consider general cost spaces in path optimisation, nor facilitates dynamic re-planning. Thus the contributions of this work is two-fold: (i) we compare a base planner, which integrates task and pathplanning to enable optimal taskplanning in continuous cost spaces by making use of a multi-tree T-RRT* algorithm , against UP2TA and a planning-in-isolation approach, and (ii) we extend the base planner with dynamic, anytime capabilities to enable high- level re-planning and low-level path corrections in dynamic environments. We collectively refer to the proposed planner as the Dynamic, AnytimeTask and Path Planner (DA-TPP).
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 mobilerobots. The two models of interest will be used throughout the whole thesis for examples and applications.
In recent years, RRT method has been combined with other pathplanning methods to help improve path quality. In , Gaussian process occupancy map was combined with RRT to plan secure path in cluttered environment. Tusi and Chung  combined RRT and artificial bee colony (ABC) methods. RRT algo- rithm was used in this method to generate nodes in the free configuration space and the best nodes were considered by applying ABC method to move the bees. The method was described to have performed bet- ter compared to particle swarm optimization (PSO) method. Any-angle search algorithm was combined with RRT to present theta*-RRT motion planning method for non-holonomic wheeled robots . Theta*-RRT method was described to have generated shorter paths in shorter time compared to other RRT methods. To obtain near-optimal path of nonlinear dynamicmobilerobots, Li et al.  combined neu- ral network and RRT to present NoD-RRT method. The authors considered the nonlinear kinodynamic constraints of the vehicles and revised RRT to per- form reconstruction to deal with the kinodynamic constraints problem. Results showed that NoD-RRT performed better compared to RRT and RRT*. An- other recent hybrid method involving RRT is RRT-A* motion planning method presented in . The method focused on optimizing RRT path generation for non-holonomic mobilerobots in known environ- ment. A* heuristic algorithm was used to decide the selection of the nearest node during the generation of the tree. The authors acknowledged the fact that the method suffered local minima problem. The authors indicated that the path length obtained using RRT-A* method was more optimal compared to goal-biased RRT method. This analysis, however, compared Man- hattan based RRT-A* to Euclidean based goal-bi- ased RRT which are of different metric functions as demonstrated in the paper. Comparing Euclide- an-based RRT-A* to Euclidean-based goal-biased RRT from their results, the proposed method utilized 849% of the time to achieve 6% path length improve- ment in sparse environment. In dense environment, 222% time was used to achieve 4.27% path length improvement over Euclidean-based goal biased RRT.
This work reports the problem of intelligent control and pathplanning of multiple mobilerobots. Soft computing methods, based on three main approaches i.e. 1) Bacterial Foraging Optimization Algorithm, 2) Radial Basis Function Network and 3) Bees Algorithm are presented. Initially, Bacterial foraging Optimization Algorithm (BFOA) with constant step size is analyzed for the navigation of mobilerobots. Then the step size has been made adaptive to develop an Adaptive Bacterial Foraging Optimization (ABFO) controller. Further, another controller using radial basis function neural network has been developed for the mobile robot navigation. Number of training patterns are intended to train the RBFN controller for different conditions arises during the navigation. Moreover, Bees Algorithm has been used for the pathplanning of the mobilerobots in unknown environments. A new fitness function has been used to perform the essential navigational tasks effectively and efficiently.
In the pathplanningtask for autonomous mobilerobots, robots should be able to plan their trajectory to leave the start position and reach the goal, safely. There are several pathplanning approaches for mobilerobots in the literature. Ant Colony Optimization algo- rithms have been investigated for this problem, giving promising results. In this paper, we propose the Max-Min Ant System for Dy- namic PathPlanning algorithm for the exploratory pathplanningtask for autonomous mobilerobots based on topological maps. A topological map is an environment representation whose focus is the main reference points of the environment and their connec- tions. Based on this representation, the path can be composed by a sequence of state/actions pairs, which facilitates the navigability of the path, with no need to have the information of the complete map. The proposed algorithm was evaluated in static and dynamic envi- ronments, showing promising results in both of them. Experiments in dynamic environments show the adaptability of our proposal.
ABSTRACT: In this paper, we study how and to what extent multiple mobilerobots can be guided to in a desired direction to explore an unknown environment and map an area of interest. The scope of the work focuses mainly on the controllers’ design for multiple robots’ pathplanning and motion coordination. Fuzzy control methodology is developed to: 1. Ensure a best pathplanning and motion coordination of the mobiles, 2. Avoid the collision and store the position of the obstacles. Genetic algorithm is implemented to optimize the different parameters of the controllers and optimize the robots’ path in case of collision avoidance. To store the obstacle and map the area, the sensors localize the obstacles and store their coordinates in a binary matrix. The algorithms and their implementation are explained in addition to the demonstration of experimental results to illustrate the efficiency and the performances of the study.
solution for all the different environments. In , the authors investigated the capabilities of genetic algorithms (GA) for solving the global pathplanning problem in large-scale grid maps. They proposed a GA approach for efficiently finding an (or near) optimal path in the grid map. They considered three differents crossover operators, namely one-point crossover, two-point crossover and an improved crossover. A statistical evaluation of the proposed GA approach in terms of solution quality was conducated, and compared against the well-known A* algorithm as a reference. Simulation results showed that GA is able to find the optimal paths in large environments equally to A* in almost all the simulated cases. The execution time was not evaluated in this work. In , the authors proposed a new pathplanning algorithm for a mobile robot based on the genetic algorithms approach. The authors considered both static and dynamic obstacles in an unknown environment. The interesting point of this paper is that the novel algorithm was tested on a real-world application using Pioneer III mobile robot. The authors were positive toward the effectiveness of GA for solving pathplanning, however we believe that the experimental results was not extensive and still need further investigation as the paper didn’t provide a comparison between simulation and experimental studies. In , the authors proposed a genetic algorithm with only the crossover operator to improve execution time and computational cost. They used adaptive population size and fixed length chromosomes, each path is represented by two chromosomes, one for x-coordinate and one for y-coordinate. A comparative study between different metaheuristic approaches classified as trajectory-based and population-based approaches for solving the global pathplanning problem of mobilerobots was conducted in  . Three methods were evaluated namely tabu search, simulated annealing and GA. It was demonstrated through simulations that simulated annealing outperforms the other planners in terms of execution time, while tabu search was proved to provide the best solution in terms of path length.
The main contribution of this study is to provide pathplanning alternatives for mobilerobots using cellular learning automata. Existing pathplanning methods for mobilerobots implemented in ROS (Robot Operating System)  were designed using Dijkstra’s algorithm and the A* algorithm, where the pathplanning is computed step by step between two nodes of an irregular mesh. Our approach extends the existing method by exploiting the regularity of cellular automata in which the distance between two adjacent cells is equal. Furthermore, we use cellular learning automata to optimize the path generated in the first stage.
High performance controllers are currently developed for mobilerobots in order to cope with the three main navigation control problems , , , , i.e. pathplanning (or following), point stabilization and tracking control (or tracking a reference trajectory, further divided in local and global tracking problems . The control of Nonholonomic Wheeled MobileRobots (NWMRs) has received much research interest during the past two decades because of the effects of nonholo- nomic constraints on the feasible control signals of this class of nonsmooth or nonholonomic systems. Representative approaches to tracking control are backstepping , adaptive , , fuzzy , periodic  and neural networks  control. Multi-robot Pathplanning (PaPl) problems are gen- erally solved by centralized and decentralized algorithms. While decentralized algorithms generate independently (separately) collision-free paths for each robot avoiding possible inter-robot collisions, centralized algorithms consider each robot as a subsystem, thus global optimization is enabled.
Meza street 1/4-547, Riga, LV 1048, Latvia; ph.: +(371)67089584, e-mail: firstname.lastname@example.org Abstract. The paper deals with pathplanning programme for mobilerobots. The aim of the paper is to develop a pathplanning programme for mobilerobots that is based on simulation software development. The results of the careful analysis were considered for optimal pathplanning algorithms. The experimental evidence was proposed to demonstrate the effectiveness of the algorithm for steady covered space. The results described in this work can be extended in a number of directions, and applied to other algorithms.
Pathiran et. al  used radio waves and mo- bile robot for localization and measurement of the power of the received signal to estimate the distance of a mobile robot. Their simulation was made for four sensors and they did not present a clear path for movement of their mobile ro- bot. Corke et. al [3, 4] used a flying robot which is equipped with GPS for localization and the measurement of radio waves’ power to estimate the distance to a mobile robot. There are fixed robots in their method which localize a percent- age of network nodes. Sichitiu and Ramadurai [5, 6] used GPS and measurement of the re- ceived signal power of radio waves to determine the distance between the node and mobile robot. Su et. al [7, 8] presented a method independent of the distance. In this method, a mobile robot which is equipped with GPS moves in opera- tion environment and broadcasts its location. To design the path of mobile robot so that network nodes can localize the robot using the received signals of their own, three static paths of Scan, Doublescan and Hilbert are suggested in . Then, some methods were proposed to improve movement path of robot to increase the accura- cy of localization. In , two paths of Circles and S-curves were suggested. Also, in , two dynamic algorithms for the path designing were called BRF and BTG were proposed which have better performance with respect to the energy consumed by robot compared with static meth- ods of path designing.
When there are changes in the domain map due to changes in object arrangements, the robot automatically updates the map and recalculates the path to a destination . The pathplanning algorithm such as modified pulse-coupled neural network (MPCNN)  uses a simple neural network by first collecting the robot’s location, destination and obstacles that plan the shortest collision-free path so that the robot moves to the grid cell containing the highest reward . Energy consumed by the robot can be reduced when planning an optimal path . However, MPCNN do not include a learning function that lets the robot to learn.
. A generates an optimal path but its computational time is high and the clearance space from the obstacle is low. The APF algorithm suffers from local minima problem. In case of FCE, the path length and turning value are comparatively larger than all other methods. The authors considered that in case of planning in unknown environments, a good path is relatively short, keeps some clearance distance from the obstacles and is smooth. They concluded that APF and the proposed FCE techniques are better with respect to this attributes. Al-Arif et al. 20 evaluated the performance of A*, Dijkstra and breadth-first search to find out the most suitable pathplanning algorithm for rescue operation. The three methods are compared for two cases: for one starting one-goal cells and for one starting multi-goal cells in 256 256 grid in terms of path length, number of explored nodes and CPU time. A* was found to be the best choice in case of maps containing obstacles. However, for free maps, breadth- first search is the best algorithm for both cases (one starting one-goal cell and one starting multi-goal cells) if the execu- tion time is the selection criteria. A* can be a better alterna- tive if the memory consumption is the selection criteria.
Abstract— This work addresses pathplanning for nonholo- nomic robots moving in two-dimensional space. The problem consists in computing a sequence of line segments that leads from the current configuration of the robot to a target location, while avoiding a given set of obstacles. We describe a planning algorithm that has the advantage of being very efficient, requiring less of one millisecond of CPU time for the case studies that we have considered, and produces short paths. Our method relies on a search in a Voronoi graph that characterizes the possible ways of moving around obstacles, followed by a string-pulling procedure aimed at improving the resulting path.
The idea of generating a set of waypoints and tracking the waypoints is one of the ear- liest problems in video game development . Like all other problems in robotics, game developers have already come up with a smart, efficient and easy to implement algorithm for this problem. The pure-pursuit algorithm are among the most common algorithms of path tracking for mobilerobots or even UAVs . The algorithm and its variations are so efficient and powerful that they were used by multiple different teams in the 2007 DARPA grand challenge, MIT  , Stanford  and Carnegie . The Stanford team even won the competitions with this algorithm with a slight change in both name 1 and logic. The algorithm for mobile robot and car like vehicles was first introduced by Craig Coulter  at Carnegie Mellon University. If the speed of the vehicle is restricted, such as an indoor lab environment, the problem of controlling the steering of a robot can be approached as a pure kinematic problem. The performance of the algorithm has been compared to other modern
Mobile robotics is an active research area that is continuously evolving to enable mobilerobots display precise movements through simple user-generated velocity commands. Nowadays, unmanned mobile robot application domains has extended in industrial as well as in service sectors . It has also attracted lot of interests from scientific, governmental, and residential areas [2-3]. Another great result from this advancement is that places unreachable to mankind before are now accessible through meticulous operations of mobile robot systems such as exploration of outer space bodies , recognition of targets in conflict and disaster zones , navigation in hazardous environments, and dangerous military operations . In cases where the operating environment is known, navigation of mobilerobots is a systematic operation that is divided into three main phases; pathplanning , trajectory generation , and tracking control .
Robot pathplanning in the unknown dynamic environment has been a challenge for the researchers from the last decades. The solution of this kind of problem plays a crucial role in the field of robotics. The main task of pathplanning of robot is to move the robot from its source to a goal position while avoiding obstacles and follow the optimum path. It’s an optimization problem because it involves the generation of optimal collision free trajectories by the mobile robot. There are two types of pathplanning problem such as global pathplanning and local pathplanning. In global pathplanning problem the environment will be familiar with the robot while in local pathplanning the robot is ignorant of the surrounding. For solving the optimization problem a socially inspired popular meta-heuristic optimization algorithm is used; which is known as Particle Swarm Optimization (PSO) technique. PSO was inspired by the social behaviour of birds and fish.
Perhaps the most exciting issues come from practical applications. The mo- tion of the robot should be performed in the physical world. The gap between the world modeling and the real world is critical. Usually, pathplanning as- sumes a two-steps approach consisting in planning a path and then executing it via feedback control. This assumption holds under the condition that the geometric model of the environment is accurate and that the robot’s Cartesian coordinates are directly and exactly measured. Designing a control law that executes a planed path defined in a robot centered frame may be sufficient in manufacturing applications; it is not when dealing with applications such as mobile robot outdoor navigation for instance. In practice, the geometric model of the world and the localisation of the robot should be often performed through the use of embarked extereoceptive sensors (ultrasonic proximeters, infrared or laser range finder, laser or video cameras . . . ).
The probabilistic roadmap method is also a popular pathplanning scheme for mobilerobots. This method uses a sampling technique to discover a sparse representation of obstacles in a configuration space. Reference  makes use of a probabilistic roadmap to avoid occlusions of the target and any obstacles. The probabilistic roadmap method is easily implemented and can be applied to complex environments. However, this method may fail when the environment does not have a sufficient number of free points with which to construct a probabilistic map. There are also many heuristic methods that can be used for pathplanning, such as rapidly exploring random trees , neural networks , genetic algorithms , simulated annealing , ant colony optimization , particle swarm optimizer and fuzzy logic [15, 16]. These methods can yield feasible schemes; however, their optimality cannot be assured with any of the above-mentioned methods.