Planar Mobile Robot Research Articles

Control of leader–follower formation and path planning of mobile robots using Asexual Reproduction Optimization (ARO)

This paper presents the optimal path of nonholonomic multi robots with coherent formation in a leader–follower structure in the presence of obstacles using Asexual Reproduction Optimization (ARO). The robots path planning based on potential field method are accomplished and a novel formation controller for mobile robots based on potential field method is proposed. The efficiency of the proposed method is verified through simulation and experimental studies by applying them to control the formation of four e-Pucks robots (low-cost mobile robot platform). Also the proposed method is compared with Simulated Annealing, Improved Harmony Search and Cuckoo Optimization Algorithm methods and the experimental results, higher performance and fast convergence time to the best solution of the ARO demonstrated that this optimization method is appropriate for real time control application.

Integrating Rough Set in Evolution Agorithm for Path Planning of Mobile Robots

In order to improve the robot path planning speed and accuracy, this paper combined cloud theory and rough sets in path planning. First initial path group can be gained by using rough set training, then a series of feasible path group can drawn by the minimum decision rule training, finally the population based on cloud model optimization can also gained and further the best route can be acquired. The simulation results verified the effectiveness of the proposed algorithm.

단일곡률궤적과 칼만필터를 이용한 이동로봇의 동적물체 추종

Path planning of mobile robots has a purpose to design an optimal path from an initial position to a target point. Minimum driving time, minimum driving distance and minimum driving error might be considered in choosing the optimal path and are correlated to each other. In this paper, an efficient driving trajectory is planned in a real situation where a mobile robot follows a moving object. Position and distance of the moving object are obtained using a web camera, and the rotation angular and linear velocities are estimated using Kalman filters to predict the trajectory of the moving object. Finally, the mobile robot follows the moving object using a single curvature trajectory by estimating the trajectory of the moving object. Using the estimation by Kalman filters and the single curvature in the trajectory planning, the total tracking distance and time saved amounts to about 7%. The effectiveness of the proposed algorithm has been verified through real tracking experiments.

Mobile Robot Motion Planning in Multi-Resolution Lattices with Hybrid Dimensionality

Safe and efficient path planning for mobile robots in large dynamic environments is still a challenging research topic. In order to plan collision-free trajectories, the time component of the path must be explicitly considered during the search. Furthermore, a precise planning near obstacles and in the vicinity of the robot is important. This results in a high computational burden of the trajectory planning algorithms. However, in large open areas and in the far future of the path, the planning can be performed more coarsely.In this paper, we present a novel algorithm that uses a hybrid-dimensional multi-resolution state × time lattice to efficiently compute trajectories with an adaptive fidelity according to the environmental requirements. We show how to construct this lattice in a consistent way and define the transitions between regions of different granularity. Finally, we provide some experimental results, which prove the real-time capability of our approach and show its advantages over single-dimensional single-resolution approaches.

Weighted virtual tangential vector algorithm for local path planning of mobile robots

A real-time path generation method for a mobile robot is newly proposed based on the virtual tangential vector (VTV). The VTV is for imposing a feature to drive a robot along with the tangential direction to a circular obstacle virtually generated in real-time. Subjected to multiple obstacles, a weighted resultant vector of all the VTVs, so-called weighted VTV (WVTV), is defined. Together with the conventional objectives such as the goal attractive vector and the obstacle repulsive vector, the WVTV is included in a multi-objective optimisation framework. Significant performance enhancements are demonstrated by simulations for complicated unknown environments.

Complete coverage path planning of mobile robots for humanitarian demining

PurposeThe purpose of this paper is to present a path planning algorithm for a non‐circular shaped mobile robot to autonomously navigate in an unknown area for humanitarian demining. For that purpose the path planning problem comes down to planning a path from some starting location to a final location in an area so that the robot covers all the reachable positions in the area while following the planned path.Design/methodology/approachThe proposed algorithm uses occupancy grid map representation of the area. Every free cell in the grid map represents a node in the graph being searched to find the complete coverage path. The complete coverage path is followed by the dynamic window algorithm, which includes robot's kinematic and dynamic constraints.FindingsThe proposed algorithm finds the complete coverage path in the graph accounting for the dimensions of the mobile robot, where non‐circular shaped robots can be easily included. The algorithms are implemented under the ROS (robot operating system) and tested in the stage 3D simulator for mobile robots with a randomly generated simulation map of an unknown area.Research limitations/implicationsSome parts of the area close to obstacles are hard to cover due to complex non‐circular shaped robot and non‐perfect path following. The future work should include better path following algorithm.Practical implicationsThe proposed algorithm has shown itself as effective and could meet the working demands of humanitarian demining.Originality/valueThe algorithm proposed in the paper enables complete coverage path planning of non‐circular shaped robots in unknown areas.

Dynamic path planning of mobile robots with improved genetic algorithm

In this study, a new mutation operator is proposed for the genetic algorithm (GA) and applied to the path planning problem of mobile robots in dynamic environments. Path planning for a mobile robot finds a feasible path from a starting node to a target node in an environment with obstacles. GA has been widely used to generate an optimal path by taking advantage of its strong optimization ability. While conventional random mutation operator in simple GA or some other improved mutation operators can cause infeasible paths, the proposed mutation operator does not and avoids premature convergence. In order to demonstrate the success of the proposed method, it is applied to two different dynamic environments and compared with previous improved GA studies in the literature. A GA with the proposed mutation operator finds the optimal path far too many times and converges more rapidly than the other methods do.

Bacterial memetic algorithm for simultaneous optimization of path planning and flow shop scheduling problems

The paper deals with simultaneous optimization of path planning of mobile robots and flow shop scheduling problem. The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. The objective is to minimize the path length without colliding with an obstacle. On the other hand, shop scheduling problems deal with processing a given set of jobs on a given number of machines. Each operation has an associated machine on which it has to be processed for a given length of time. The problem is to minimize the overall time demand of the whole process. In this paper, we deal with two robots carrying items between the machines. Bacterial memetic algorithm is proposed for solving this combined problem. The algorithm is verified by experimental simulations and compared to classical techniques.

Path planning with variable-fidelity terrain assessment

Terrain assessment and path planning for mobile robots are intrinsically linked. There exists a variety of terrain assessment algorithms and these methods follow the trend of low-fidelity at low-cost and high-fidelity at high-cost. We present a modular path-planning algorithm that uses a hierarchy of terrain-assessment methods, from low-fidelity to high-fidelity. Using the available sensor data, the visible terrain is first assessed with the low-fidelity, low-cost method. The decision to assess a piece of terrain with the high-fidelity, high-cost method is made considering potential path benefits and the cost of assessment. This can be thought of as providing a means to triage large amounts of terrain data. The result is a lower combined cost of the path and terrain assessment that exploits the capabilities of the robot chassis where prudent. We demonstrate a system using one implementation of the technique on a large number of simulated path planning problems in fractal terrain. Additionally, we provide results and system details from an experimental field test carried out on Devon Island, Canada.

Trajectory planning of mobile robots using indirect solution of optimal control method in generalized point-to-point task

This paper presents an optimal control strategy for optimal trajectory planning of mobile robots by considering nonlinear dynamic model and nonholonomic constraints of the system. The nonholonomic constraints of the system are introduced by a nonintegrable set of differential equations which represent kinematic restriction on the motion. The Lagrange’s principle is employed to derive the nonlinear equations of the system. Then, the optimal path planning of the mobile robot is formulated as an optimal control problem. To set up the problem, the nonlinear equations of the system are assumed as constraints, and a minimum energy objective function is defined. To solve the problem, an indirect solution of the optimal control method is employed, and conditions of the optimality derived as a set of coupled nonlinear differential equations. The optimality equations are solved numerically, and various simulations are performed for a nonholonomic mobile robot to illustrate effectiveness of the proposed method.

Optimal path planning of mobile robots: A review

Mobile robots are increasingly used in automated industrial environments. There are also other applications like planet exploration, surveillance, landmine detection, etc. In all these applications, in order that the mobile robots perform their tasks, collision-free path planning is a prerequisite. This article provides an overview of the research progress in path planning of a mobile robot for off-line as well as on-line environments. Commonly used classic and evolutionary approaches of path planning of mobile robots have been addressed. Review shows that evolutionary optimization algorithms are computationally efficient and hence are increasingly being used in tandem with classic approaches while handling Non-deterministic Polynomial time hard (NP-hard) problems. Also, challenges involved in developing a computationally efficient path planning algorithm are addressed. Key words: Path planning, mobile robot, off-line environment, on-line environment, classic, evolutionary algorithms.

Bacterial memetic algorithm for offline path planning of mobile robots

The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. This problem belongs to the group of combinatorial optimization problems which are approached by modern optimization techniques such as evolutionary algorithms. In this paper the bacterial memetic algorithm is proposed for path planning of a mobile robot. The objective is to minimize the path length and the number of turns without colliding with an obstacle. The representation used in the paper fits well to the algorithm. Memetic algorithms combine evolutionary algorithms with local search heuristics in order to speed up the evolutionary process. The bacterial memetic algorithm applies the bacterial operators instead of the genetic algorithm’s crossover and mutation operator. One advantage of these operators is that they easily can handle individuals with different length. The method is able to generate a collision-free path for the robot even in complicated search spaces. The proposed algorithm is tested in real environment.

ON-LINE PATH PLANNING FOR MOBILE ROBOTS IN DYNAMIC ENVIRONMENTS

Motion planning of mobile robots is a complex problem. The com- plexity further increases when it comes to path planning in dynamic environments. This paper presents an algorithm for on-line path planning of mobile robots in un- known environments with moving obstacles. A mathematical model is established which considers all the current on-line information of robot as well as nearing ob- stacles. Particle Swarm Optimization technique is used to optimize the velocity parameters of the robot, to arrive at the shortest collision-free trajectory, satisfy- ing dynamic constraints. Simulation results show that the proposed algorithm is computationally efficient and effective.

Target attraction-based ant colony algorithm for mobile robots in rescue missions

After an earthquake, the road conditions are usually unknown and hazardous, which poses a great challenge for mobile robots to plan paths and reach the goal position safely for rescue operations. This paper presents a target attraction-based ant colony (TAAC) algorithm for the dynamic path planning of mobile robots operated in rescue missions. The global information of the road map is deployed to establish a target attraction function so that the probability of selecting an optimal path to the goal node is improved and the probability of converging to a local minimum path is reduced. Simulation results show that the proposed TAAC algorithm has a better dynamic performance and a faster convergence speed, compared with the existing max-min ant system algorithm.

2-Dimensional SVFC Application in Path Planning of Mobile Robots

A 2-dimensional single variable fuzzy controller (SVFC) is introduced to achieve the path planning for mobile robots. With fusing some fuzzy and uncertain information obtained from distance and angle sensors, and invoking the corresponding fuzzy logic inference, this SVFC can output the action instruction, which then determines the behavior control, realizing the optimal non-collision path planning for the mobile robots. The essential jobs in this whole design process consist of the defining the fuzzy sets, discrete grades and membership functions for the input and output parameters, establishing the rules for fuzzy controller, and evaluating the fuzzy control response. Due to the complex computation of the high dimension matrixes achieved from the SVFC, an off-line inquiring table scheme is proposed to gain the inference response, which strictly follows the Mamdani inference rules, viz. Max-Min integrated theory, a practical fuzzy control response table is presented in sequence. Moreover, after constituting the working environmental model, the numerical simulation is implemented to estimate the performance of this fuzzy control system. The simulation result shows that the SVFC leads to the intelligent, simple, and accurate path guidance for mobile robots, indicating the behavior control and realizing the navigation tasks.

Autonomous Variable-Resolution Map Building for Mobile Robots in Unknown Environments

SUMMARY This paper presents a method that allows simultaneous map building and path planning for mobile robots in unknown environments. A graphical representation of a workspace in variable resolutions is constructed using measurement data obtained by omnidirectional distance sensors. At the same time, a search for a feasible path to the target destination is executed using the constructed graph map. The proposed method is evaluated by performing simulations and experiments using an omnidirectional mobile robot equipped with laser range finders. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(4): 59–69, 2014; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22493

Efficient Interpolated Path Planning of Mobile Robots based on Occupancy Grid Maps

Abstract This paper focuses on interpolation-based path planning algorithms for mobile robot that use the occupancy grid map of the environment. Such an algorithm produces the path close to optimal solution in continuous search space, but has computational burden. We discuss on using heuristics to improve efficiency of the E* algorithm, as the interpolation-based path planning algorithm, with a trade-off of generating less optimal paths. We compare obtained paths with the almost optimal solution in continuous search space and also with the path obtained by the D* algorithm, which is a representative of the classical graph search algorithms applied on the occupancy grid map. All used algorithms are verified both by simulation and experimentally on a Pioneer 3DX mobile robot equipped with a laser range finder.

Path Planning for Mobile Robots in 3D Dynamic Environments

Aiming at the robustness of the path planning of mobile robots in the 3D dynamic environment, an improved ARF (Artificial Potential Field) based path planning algorithm is proposed in this paper. Supposing that all the obstacles move regularly and the robot is on uniform motion in a grid 3D environment. Firstly, the algorithm computes the future statuses of the environment, such as the coordinate of all the obstacles and the goal, until a time step T in which there is at least one route between the start and goal. T is obtained by BFS (Breadth First Search) and environment configuration parameters. Secondly, because in every time step the environment can be consider as being static, ARF is used to determine the potential value of every space position in each time step. Finally, a route along the lowest potential values is found for the robot from the start to goal. Simulation results show that the algorithm makes the robot avoid obstacles effectively and reach the goal safely.

A Novel Algorithm for Path Planning of Mobile Robots in Dynamic Environment

Aimed to accuracy and convergence of Evolutionary Algorithms for path planning of robots in dynamic environment, this paper integrated cloud model into evolutionary algorithm to improved evolutionary operators. One-dimensional cloud operator was used evolution and mutation, at the same time the previous search results were used. Simulation results show that the method is effective in performance.

Closing the learning-planning loop with predictive state representations

A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate environment model, and then plan to maximize reward. Unfortunately, learning algorithms often recover a model that is too inaccurate to support planning or too large and complex for planning to succeed; or they require excessive prior domain knowledge or fail to provide guarantees such as statistical consistency. To address this gap, we propose a novel algorithm which provably learns a compact, accurate model directly from sequences of action-observation pairs. We then evaluate the learner by closing the loop from observations to actions. In more detail, we present a spectral algorithm for learning a predictive state representation (PSR), and evaluate it in a simulated, vision-based mobile robot planning task, showing that the learned PSR captures the essential features of the environment and enables successful and efficient planning. Our algorithm has several benefits which have not appeared together in any previous PSR learner: it is computationally efficient and statistically consistent; it handles high-dimensional observations and long time horizons; and, our close-the-loop experiments provide an end-to-end practical test.