A Novel Approach to Optimizing the Initial Path of Mobile Robots in Static Environments

From the time being, there are even instances for application of mobile robots in our lifelike in home, schools, hospitals, etc. The goal of this paper is to plan a path and minimizing thepath lengths with obstacles avoidance for a mobile robot in static environment. In this work wedepict the issue of off-line wheeled mobile robot (WMR) path planning, which best route forwheeled mobile robot from a start point to a target at a plane environment represented by 2-Dwork space. A modified optimization technique to solve the problem of path planning problemusing particle swarm optimization (PSO) method is given. PSO is a swarm intelligence basedstochastic optimization technique which imitate the social behavior of fish schooling or birdflocking, was applied to locate the optimum route for mobile robot so as to reach a target.Simulation results, which executed using MATLAB 2014 programming language, confirmedthat the suggested algorithm outperforms the standard version of PSO algorithm with the sameenvironment conditions by providing the shortest path for mobile robot.

In view of biogeography-based optimization algorithm has the disadvantages of application limitations and slow convergence speed when in solving the problem of mobile robot path planning. This paper proposes an improved biogeography-based optimization algorithm, which is used to solve the global path planning of mobile robot in static environment. In the proposed algorithm, the navigation point model is selected as the working area model of mobile robot, and the nonlinear migration model and mutation mechanism with the elite retention mechanism are introduced to the biogeography-based optimization algorithm to improve its performance. Simulation proves the feasibility and the effectiveness of the proposed path planning algorithm.

The Rapidly Exploring Random Tree Star (RRT*) is a probabilistically complete algorithm. It is recognized as a better path planning algorithm, but its path quality and path planning speed still have room for improvement. This paper proposes an improved RRT* algorithm based on alternative paths and triangular area sampling (ATS-RRT*). The alternative paths strategy generates multiple initial paths based on whether the sample points can communicate with the target points and set the path with the smallest cost as the final initial path, which can speed up the initial path planning and improve the initial path finding rate. The triangular area sampling strategy combines every three adjacent nodes to generate some triangle areas and corresponding half-triangle areas. The path quality can be improved quickly by limiting the sampling in these triangle areas. In addition, the direct connection strategy with triangle nodes and the tabu table using in the Rewire process also speeds up the algorithm. Experiments show that the speed of finding the initial path and the success rate of finding the suboptimal path are improved by 2.3 and 1.45 times respectively compared with RRT*, Quick-RRT*, and Informed + Quick-RRT*.

This paper focuses on finding the shortest and correct path for the robots without any collision with the objects in its environment. Generally global (static) and local environments are used for path planning of mobile robots. In this work, static environment is considered and an optimized path is evaluated for efficient robot movement from the start position to the end position. Different classical and evolutionary based path search algorithms are used so as to find the optimized path, with reduced computation time and path length of the mobile robot. The results reveal that evolutionary-based algorithms provide a more effective solution to the problem under consideration.

Path planning is an important aspect of any mobile robot navigation to find a hazard-free path and an optimal path. Currently, the A* algorithm is considered to be one of the prominent algorithms for path planning in a known environment. However, with the rise of neural networks and machine learning, newer promising algorithms are emerging in this domain. Our work compares one such algorithm namely the Hopfield neural network-based path planning algorithm with A* in a static environment. Both the Hopfield network and the A* algorithm were implemented while minimizing the total run times of the programs. For this, both the algorithms were run in MATLAB environment and a set of mazes were then executed and their run times were compared. Based on the study, the A* algorithm fared better and the Hopfield network showed promising results with scope for further reduction in its run time.

Reaching is a critical task for humanoid robots, requiring the application of state-of-the-art algorithms for motion planning and inverse kinematics. Practical algorithms for solving these subproblems are currently not complete, offering resolution completeness or probabilistic completeness instead. While this lesser provision of completeness is acceptable in many cases, naively combining state-of-the-art approaches in motion planning and inverse kinematics can lead to a method that provides no measure of completeness. We present a probabilistically complete solution to the reaching problem for humanoid robots in static environments, and evaluate it against two other methods using a kinematically simulated humanoid in a virtual environment

It is a hot topic in the field of road landscape planning technology that a mobile robot can quickly and safely find an optimal path in a multi-obstacle environment. In path planning, in light of the problems of poor cooperation and slow convergence of ant colony algorithm in a known environment, the existing potential field method in the local path environment focuses on avoiding dynamic obstacles but cannot guarantee an optimal path. This study provides a new fusion algorithm for path planning optimization in both static and dynamic environments. Firstly, to prevent slipping into a local optimum, create a pheromone diffusion model and adaptively tweak the population information entropy factor to speed up the convergence speed of the Adaptive Ant Colony Optimization (AACO) algorithm. Secondly, on the basis of the globally planned path, by designing the local stability detection and escape functions, the Improved Artificial Potential Field (IAPF) method is utilized to solve the problem of unreachable destination. Finally, we conduct simulation experiments through MATLAB to compare the indicators for evaluating paths, it verifies that the fusion algorithm proposed in this research has obvious advantages in path planning in both static and dynamic environments.KeywordsPath planningAdaptive ant colony optimizationPopulation information entropyImproved artificial potential field

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

Path planning arid coordination of multiple unmanned air vehicles, so as to jointly reach a target area while minimizing the flight's exposure to radar is addressed. A hierarchical decomposition approach is pursued. The optimal path planning for a single vehicle is performed to minimize exposure while a timing constraint is imposed. Each vehicle plans it's own path. The path planning consists of a two step procedure. First, a polygonal path to the optimal path is obtained. Second, the initial path is refined to a flyable path by acknowledging maneuverability constraints. The coordination of the timing of the vehicles' arrival at the target area is performed by the higher level coordination agent. The coordination agent calculates a team estimated time of arrival from a sensitivity function that is calculated and communicated by each of the UAVs. This approach is illustrated in a simulation of a simplified scenario where three UAVs are coordinated to arrive at the target area simultaneously. Popup threats are introduced that result in dynamic inflight replanning. Simulation results show that coarse grid planning and decentralized coordinated control can rapidly respond to new threats while satisfying rigid rendezvous timing constaints.

During an earthquake, the better performance of segmental tunnel lining, compared to the continuous in-cast concrete lining, is generally related to the joints between segments. In order to better understand the influence of the segment joints, their effect on the internal forces induced in tunnel lining simultaneously with the effects of the other influential parameters should be considered. In this work, the segmental joints were simulated by the representative stiffnesses and effects of these characteristics in relation to the other parameters such as the soil-liner interface behavior, number of segments in each ring and thickness of segments on the internal forces induced in structure were investigated. For this purpose, 2D numerical analyses were performed and the results obtained were discussed. Results showed that under the seismic condition, the components that had the most significant role on the internal axial forces induced in the segmental lining were rotational stiffness and axial stiffness of joints. Also the bending moments were more affected by the rotational stiffness. Generally, the radial joint stiffness had a less effect on the induced internal forces. With increase in the number of segments and their thickness, the effect of joint stiffness on the internal forces increases and the design of joints should be given more attention; however, the effects of joint stiffness and frictional behavior at the soil-liner interface on the maximum induced forces are almost independent from each other. Also in a specified joint behavior, by variation in each one of the other parameters including the soil-liner interface condition, number of segments and their thickness, the absolute magnitude of the maximum induced internal forces sometimes change significantly.

Mobile robots path planning: Electrostatic potential field approach

PurposeThe purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD).Design/methodology/approachBased on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground.FindingsThe assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis.Originality/valueThe work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.

The rapid expansion in the industrial revolution has been aroused to utilize of the robot in various high accuracy and productivity applications. Optimal path planning is an important concern in navigation of mobile robot interesting with finding an optimal path. The firefly algorithm is considered as an increasingly promising tool of Swarm Intelligence, which is used in various optimization areas. The objective of the proposed method is to find the free-collision points in mobile robot environment and generate the optimal path based on firefly algorithm and D* algorithm. D* algorithm has been applied to find the shortest path. The essential function of applying the firefly algorithm and Quadratic parametric equations is to iterative globally search and determine the accurate positions of a set of intermediate via points within the free space and generating the corresponding smooth trajectory at a specified time respectively. The collision with obstacles can be efficiently avoided by constructing the free Cartesian space, which guarantee a free collision path planning. The simulation results illustrate the efficiency of proposed methods performance in finding and determining the optimal path and trajectory even in various degrees of environmental complexity.

Mobile robot navigation is to find an optimal path to guide the movement of the robot, so path planning is guaranteed to find a feasible optimal path. However, the path planning problem must be solve two problems, i.e., the path must be kept away from obstacles or avoid the collision with obstacles and the length of path should be minimized. In this paper, a path planning algorithm based on random coding particle swarm optimization (RCPSO) algorithm is proposed to get the optimal collision-free path. Dijstra algorithm is applied to search a sub-optimal collision-free path in our algorithm; then the RCPSO algorithm is developed to tackle this optimal path planning problem in order to generate the global optimal path. The crossover operator of genetic algorithm and random coding are introduced into the particle swarm optimization to optimize the location of the sub-optimal path. The experiment results show that the proposed method is effective and feasible compared with different algorithms.

Robot navigation is challenging for mobile robots technology in environments with maps. Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments. The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm. Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances.