Meza street 1/4-547, Riga, LV 1048, Latvia; ph.: +(371)67089584, e-mail: email@example.com 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.
The Grey Wolf Optimizer (GWO) algorithm  mimics grey wolf behaviour towards finding the prey, which actually models the solution to the optimization problem (OP). The advantage of all versions of GWO algorithms is the reduced number of search parameters, which is reflected in various applications including GWO-based optimal tuning of Proportional-Integral (PI)-fuzzy controllers , , ,  and Proportional-Integral-Derivative (PID)-fuzzy controllers , , . GWO algo- rithms are also applied to mobile robot PaPl, and recent results concern integration of safe boundary algorithm , inclusion of chaotic features in GWO , and combination of artificial intelligence techniques and probability theory , . In addition, recent GWO-based PaPl approaches for underwater autonomous vehicles are reported in , , .
Recent results derived from the synthesis of the shortest paths for a car- like robot (see above and Sou` eres–Boissonnat’s Chapter) provide an analyti- cal way to compute the shortest path distance to a polygonal obstacle for a point car-like robot [ 91 ]. This means that all the distance computations in the Reeds&Shepp metric can be done on-line. This property has been exploited to include dynamic obstacle avoidance when the robot executes its trajectory. Figure 13 from [ 38 ] shows an example of on-line updating of an admissible path when an unexpected obstacle (the black box) occurs during the execution of the motion. The various balls covering the path in the figure are the projection onto R 2 of the maximal collision-free Reeds&Shepp balls covering the path in the configuration space. Up to now, the distance function are known for a point robot; its extension to a polygonal robot has to be done.
In , the authors presented a modified ACO algorithm to solve the global pathplanning problem in an indoor environment for a UAV. The workspace of the UAV is modeled as 3-dimenstional grid map. To overcome the shortcomings of the conventional ACO, the authors proposed a modified pheromone update rule: they proposed to update the quantity of pheromone only on the best path generated after an iteration of the algorithm. They also limited the quantity of pheromone to solve the problem of premature convergence of ACO. To evaluate the robot path, they added a climbing parameter in addition to the path cost, they claimed that this parameter will help the robot to choose the best direction and bypass obstacles. It was demonstrated throw simulation that the algorithm could produce good quality of solution in 10*10 grid map. In , Ganganath et al. proposed an off-line path planner for a mobile robot based on Ant Colony Optimization algorithm with Gaussian functions (ACO-2-Gauss). The workspace of the mobile robot is a 3D hilly landscape. Unlike ordinary ACO algorithms, the proposed path planner provides the ants an extra ï ˇn ´Cexibility in making routing decisions: an ant is allowed to select any point on the circumference of a circle with a radius of R as its next position. Each ant will select a set of circles to move toward the goal position. The pheromone is distributed on the center of the different circles as a 2D Gaussian function. In their simulation study, the authors compared the proposed algorithm against a preceding version: ACO-Gauss, it was demonstrated that the quality of solution of ACO-2-Gauss was improved as compared to ACO-Gauss path planner.
environments is a widely researched area of robotics. The potential for autonomous mobilerobots use, in industrial and military applications, is boundless. Pathplanning entails computing a collision free path from a robot’s current position to a desired target. The problem of pathplanning for these robots remains underdeveloped. Computational complexity, path optimization and robustness are some of the issues that arise. Current algorithms do not generate general solutions for different situations and require user experience and optimization. Classical algorithms are computationally extensive. This reduces the possibility of their use in real time applications. Additionally, classical algorithms do not allow for any control over attributes of the generated path. A new roadmap pathplanning algorithm is proposed in this paper. This method generates waypoints, through which the robot can avoid obstacles and reach its goal. At the heart of this algorithm is a method to control the distance of the waypoints from obstacles, without increasing its computational complexity. Several simulations were run to illustrate the robustness and adaptability of this approach, compared to the most commonly used pathplanning methods.
question that we addressed in the iroboapp project about which method is more appropriate for solving the pathplanning problem. It seems from the results that heuristics methods including evolutionary com- putation techniques such as GA, local search tech- niques, namely, the TS and NNs cannot beat the A algorithm. A also is not appropriate to solve robot pathplanning problems in large grid maps as it is time consuming, which is not convenient for robotic applications in which real-time aspect is needed. RA is the best algorithm in this study, and it out- performs A in terms of execution time at the cost of losing optimal solution. Thus, we designed new hybrid approaches that take the benefits of both RA and heuristic approaches in order to ameliorate the path cost without inducing extra execution time. Lesson 4: We designed a new hybrid algorithm that combines both RA and GA. The first phase of the RA þ GA uses RA to generate the initial popu- lation of GA instead of the greedy approach in order to reduce the execution time, and then GA is used to improve the path quality found in the previous phase. We demonstrated through simulation that the hybridization between RA and GA brings a lot of benefits as it gathers the best features of both approaches, which contributes in improving the solution quality as compared to RA and reducing the search time for large-scale graph environments as compared to A .
Kinematic modeling and analysis of skid-steered mobile robot have been done by Yi et al.  Skid-steer mobilerobots are widely used because of their simple mechanism and high reliability. They have used the Kalman filter to take care of the kinematic constraints to enhance the accuracy during navigation. Kane et al.  have discussed about the control of mobile robot on arbitrary surfaces. They have done the kinematic analysis of the mobile robot movement on a two dimensional flat surface. They have tested their results in a medical surgical robot. Li et al.  have done the kinematic analysis of a transformable wheel-track robot using self-adaptive mobile mechanism. They have done the kinematic analysis of the movement of Amoeba-III mobile robot. They have given experimental results for validity of their mobile platform. On and Ahmet  have considered the kinematic constraints of a mobile robot during pathplanning. Using the kinematic constraints, the robot can negotiate with the sharp corners with the help of a smooth trajectory. They have conducted experiments to show the effectiveness of their proposed approach.
Rapid advances in sensing, computing and communication technologies have led to con- siderably increased research activities in multi-robot systems over the last decade. Top- ics include multi-robot motion planning, cooperative manipulation, aerial applications involving cooperative exploration of the unknown environment, automated highway sys- tems, software architectures for multi-robot systems, and formation control. Multi-robot systems have been proven to offer additional advantages in terms of flexibility in operat- ing a group of robots and failure tolerance due to redundancy in available mobilerobots. However, the benefits of using multi-robot teams do not come without cost. Coordinating teams of autonomous robots is much more challenging than maneuvering a single robot. This dissertation addresses formation control problems, which are among the most ac- tive research topics in multi-robot systems. Over the last two decades, there have been a large number of publications on this field, and it is still growing. Recently, this re- search has been extended to some related research areas, e.g., consensus problems and distributed control systems, imposing new challenges on formation control problems. In general, formation control subproblems addressed in the literature can be classified as formation shape generation, formation reconfiguration/selection, formation tracking, and role assignment in formation. The main purpose of this dissertation is to address two important and correlated subproblems in formation control: formation tracking and role assignment in formation. The goal of the former is that a team of mobilerobots is required to maintain a geometric formation while tracking a reference or a set of refer- ences. The latter arises when a mobile robot in the team must decide what role to take on in a desired formation configuration.
to evaluate robot’s position and pose use landmarks. For example, GPS is widely used for abovementioned purposes, which is used mostly for outdoor navigation. However if receivers are not synchronized with the satellites and cannot be precise enough to be used for localization purposes of mobilerobots. Sunhong Park and Shuji Hashimoto in their research carried out a number of experiments in this field using exclusive radio-frequency identification. In their work, the robot is able to estimate its position with help of IC tags and trigonometric functions and information of its location recorded dynamically. The usage of IC tags is determined by the low cost and small size, i.e., the tags could be easily installed inside the apartment. The robot in this experiment is based on an electric wheel- chair for elderly and disabled people, with mounted distance and touch sensors. The in- creased amount of IC tags are improving system’s accuracy, however the cost is increased accordingly, therefore in this research it is compromised with use of polar Cartesian co- ordinates of the IC tags, thus these tags are arranged accordingly. In contrast with tradi- tional methods offered algorithm reverberates robot’s posture each time it senses a new IC tag. The method works as follows:
criterion , . However, the environment may be imprecise, vast, dynamical and partially non-structured . In such environment, pathplanning depends on the sensory information of the environment, which might be associated with imprecision and uncertainty. Thus, to have a suitable planning scheme in a cluttered environment, the controller of such kind of robots must have to be adaptive in nature. Trajectory planning, on the other hand, considers time and velocity of the robots, while planning its motion to a goal. It is important to note that pathplanning may ignore the information about time/velocity, and mainly considers path length as the optimality criterion. Several approaches have been proposed to address the problem of motion planning of a mobile robot. If the environment is a known static terrain and it generates a path in advance, it is said to be off-line algorithm. It is called on-line, if it is capable of producing a new path in response to environmental changes.
There are many ways to study pathplanning. The traditional pathplanning methods include: Visibility Graph, C-Space Method, Grid Algorithm, Artificial Potential Field Theory, Topological Method, etc. . With the development of modern computing technology, intelligent algorithms have been widely used in the pathplanning of mobilerobots. Genetic Algorithm , Fuzzy Logic Algorithm , Neural Network Algorithm , Ant Colony algorithm , and their combination have been applied in pathplanning effectively. At present, these methods are mainly studied that finding a optimal path from the starting point direct to the ending point. However, in practical applications, mobilerobots may be required to go to a number of different places to complete the task before reaching the designated area. The pathplanning studied in this paper differs from other paths in that the mobile robot must pass through other specified target points before it reaches the designated location.
The actual working environment of a mobile robot is a realistic physical space, and the space handled by the pathplanning algorithm is an abstract space of the environment. Environment modeling is a very important link of the robot pathplanning. Through a large number of literature research , this paper uses the grid method to establish the model. The working environment is divided with grids of the same size and the obstacle model is constructed according to the working environment of the security robot.
A* algorithm  starts to execute by looking at the starting node first and then expanding to the surrounding nodes. This operation proceeds until the destination hub is found. So as to know the hubs, which will be utilized as a part of hunt, A* calculation needs an approach to monitor the hubs. So the hubs to be inspected are held in a rundown, called Open List. The open line is utilized to store the hubs which have been recognized, yet their contiguousness has not put away. Since the operations of insertion and cancellation will happen much of the time, the open line takes a connected rundown usage At the starting it put the beginning hub to the Open List, and subsequent to looking at all of its encompassing hubs it will move it from the Open List and place in another rundown called the Closed List. Shut List holds the hubs that are gone by and there is no compelling reason to return to its individuals.
Memory usage in CTRs mainly depends on the number of sample points along the robot centre line as well as the robot architecture (i.e. type of tubes and number of tubes). Fixing the number of sample points allows the preallocation of the maximally needed memory, and thus no allocation is necessary during the forward kinematic calculations. Further, the memory is constrained by memory- alignment requirements to enable vector instructions (e.g. 64-byte for the Xeon Phi™architecture). Respecting these requirements allows memory allocation as a single chunk, which simplifies and expedites memory copying and network transmission of robot objects. Further, optimal memory al- location and alignment helps to fully utilize cache lines and hence optimises overall processor cache utilization, which reduces cache misses and increases its coherency, reducing potential bottlenecks caused by memory access times.
(Ghosh,Panigrahi & Parhi,2017)employs an autonomous mobile robot to achieve optimal path by proposing two efficient intelligent optimal controllers consisting of bat algorithm (BA) and flower pollination algorithm (FPA) in an unfamiliar condition . FPA is designed by taking into consideration the process of pollinating flowering plants where various pollinators transport pollen.BA solves different kinds of optimisation problems in engineering through echolocation and frequency tuning. A fitness function is considered autonomously for achieving the path-planning task of mobile robot , by taking into consideration the distance existing either between the robot and the obstacle or between the robot and the goal satisfying the criteria of obstacle avoidance where the robot shows the goal=achiever's behaviour. The mobile robot considers the values provided by the objective function to avoid obstacles in an unfamiliar environment and arrives at its goal.
This article presents a novelimplementation of Particle Swarm Optimisation(PSO)forfinding the most optimal solution to pathplanning problem for a swarm of robots. The swarm canvasses through the configuration space having static obstaclesby applying PSO on potential fields generated by the target. The best possible path by the momentary leaders of the group is retraced toget the solution. The designed algorithm was simulated on a specially developed simulator adhering to real time constraints and conditions faced by the mobilerobots. The solutions for various configuration spaces are presented to verify the effectiveness of the algorithm.
An experimental comparison of three different trajectory generation methods for actual navigation of a mobile robot is presented. Experiments have shown that established researches have drawbacks in tracking a path with high curvature. The first method that considers the acceleration limits of the mobile robot produces a trajectory with non-uniform sampling time and has issues when the initial or terminal velocity is zero-value. The convolution-based method that considers the heading angles of a curved path was able to consider wider scope of physical limits of the mobile robot, the geometrical constraints were not satisfied which results to inability to track the planned path.
Soltani also presented a framework based on transportation costs, safety and reliability for construction pathplanning. He studied a fuzzy-based mul- ti-objective optimization method to make optimal strategic decisions on the movement path of construction site, and make detailed decisions on the distance of the workplace . Sivakumar investigated the potential of applying genetic algorithms to automate the pathplanning of cooperative construction manipu- lators. The basic premise of this work is that automating the different planning steps will contribute to more reliable plans and thus promote the usage of coop- erative manipulator. Two methodologies have been proposed using the concept of Configuration Space (C-Space) technique in conjunction with the genetic search .
The parameter d is strictly dependent on the primitives of the supervisor, and on how those primitives are mapped in time-trajectory. The dependence of this parameter is basically related to the discretization of the map, to the time-trajectory chosen (for ex- ample a straight line primitive could be mapped in uniformly accelerated trajectory, constant speed trajectory, trajectory with initial and final speed equal to zero, and so on) and to the speed at which the trajectory is executed. This last point is very important; the same geometric path executed with a different speed will lead to different bounds of the derivative, so as at bigger speed, the time needed to complete the trajectory is shorter but the parameter d will result bigger. Reminding that q should be greater than d one can easily understand that for a given primitive, a different execution speed will lead to a shorter execution time but to a bigger tracking error and a bigger constrain on τ . This could inspire a particular policy for choosing the trajectory. In particular one can choose a shorter execution time when the precision doesn’t matter (moving through areas without obstacles) leading to a faster but less precise movement, while one can choose a larger execution time when the precision matters (moving through areas full of obstacles, moving through holes, precision operations) leading to a slower but more precise movement.
obstacle. Thus c describes the ‘closeness’ of q to an obstacle. We compare the performance of the DA-TPP base planner with a simple planner consisting of task and pathplanning in isolation (isolated TP) and UP2TA. The isolated TP consists of a task planner that solves for an optimal action sequence by using the Euclidean distances between landmarks as movement action costs. For consistency in comparison, a single instance of a bi-directional T-RRT* algorithm is called for each movement action to obtain the final action-motion sequence. Our implementation of the UP2TA framework employs the greedy search algorithm  to obtain approximate cost metrics for each possible movement action. For consistency, LPG-td is then used to solve the task planning problem. An additional pathplanning layer based on Theta*  is required to obtain true paths for each movement action like in isolated TP.