Mobile robot path planning based on adaptive bacterial foraging algorithm

The utilization of biomimicry of bacterial foraging strategy was considered to develop an adaptive control strategy for mobile robot, and a bacterial foraging approach was proposed for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation, two test scenarios of static environment with different number obstacles were adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.

NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

A novel approach for collision-free path planning of a multiple degree-of-freedom （DOF） articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point （VNP）, a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

A Rough Set GA-based Hybrid Method for Robot Path Planning

In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.

Robot path planning in dynamic environment based on reinforcement learning

Proposes an adaptive learning method based on reinforcement learning for robot path planning problem, which enables the robot to adaptively learn and perform effective path planning, to avoid the moving obstacles and reach the target. Thereby achieving automatic construction of path planning strategy and making the system adaptive to multi robots system dynamic environments, and concludes from computer simulation experiment that the method is powerful to solve the problem of multi robot path planning, and it is a meaningful try to apply reinforcement learning techniques in multi robot systems to develop the system’s intelligence degree.

Ant Colony Optimization with Potential Field Based on Grid Map for Mobile Robot Path Planning

For the mobile robot path planning under the complex environment,ant colony optimization with artificial potential field based on grid map is proposed to avoid traditional ant colony algorithm's poor convergence and local optimum.Firstly,the pheromone updating mechanism of ant colony is designed by a hybrid strategy of global map updating and local grids updating.Then,some angles between the vectors of artificial potential field and the orientations of current grid are introduced to calculate the visibility of eight-neighbor cells of cellular automata,which are adopted as ant colony's inspiring factor to calculate the transition probability based on the pseudo-random transition rule cellular automata.Finally,mobile robot dynamic path planning and the simulation experiments are completed by this algorithm,and the experimental results show that the method is feasible and effective.

Robot path planning using genetic algorithms

Presents a strategy for soccer robot path planning using genetic algorithms for which, real number coding method is used, to overcome the defects of binary coding method, and the double crossover operation adopted, to avoid the common defect of early convergence and converge faster than the standard genetic algorithms concludes from simulation results that the method is effective for robot path planning.

A novel method for robot path planning

Path planning is one of the most important problems in the design of a mobile robot.A novel approach called generalized Voronoi diagrams(GVD)may deal with this matter.First,a method was introduced to normalize the obstacles and present efficient techniques for generating GVDs.Then a best path searching algorithm was presented.Examples implemented were given to indicate the availability of the mentioned algorithms.The approaches in this paper can also be used in applications including visualization,spatial data manipulation,etc.

Path Planning for Robotic Arms Based on an Improved RRT Algorithm

The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.

Robot path planning in globally unknown environments based on rolling windows

In this paper, robot path planning in globally unknown environments is studied. Using the rolling optimization concept in predictive control for reference, a new strategy of path planning for a mobile robot, based on rolling windows, is proposed. The method makes full use of the real-time local environmental information detected by the robot and the on-line path planning is carried on in a rolling style. Optimization and feedback are combined in a reasonable way. The convergence of the planning algorithm is also discussed.

Intelligent learning technique based-on fuzzy logic for multi-robot path planning

Soccer robot system is a tremendously challenging intelligent system developed to mimic human soccer competition based on the multi discipline research: robotics, intelligent control, computer vision, etc. robot path planning strategy is a very important subject concerning to the performance and intelligence degree of the multi robot system. Therefore, this paper studies the path planning strategy of soccer system by using fuzzy logic. After setting up two fuzziers and two sorts of fuzzy rules for soccer system, fuzzy logic is applied to workspace partition and path revision. The experiment results show that this technique can well enhance the performance and intelligence degree of the system.

A review:On path planning strategies for navigation of mobile robot

This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap.The classical approaches such as cell decomposition(CD),roadmap approach(RA),artificial potential field(APF);reactive approaches such as genetic algorithm(GA),fuzzy logic(FL),neural network(NN),firefly algorithm(FA),particle swarm optimization(PSO),ant colony optimization(ACO),bacterial foraging optimization(BFO),artificial bee colony(ABC),cuckoo search(CS),shuffled frog leaping algorithm(SFLA)and other miscellaneous algorithms(OMA)are considered for study.The navigation over static and dynamic condition is analyzed(for single and multiple robot systems)and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches.It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm.Hence,reactive approaches are more popular and widely used for path planning of mobile robot.The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.

Tracking Control for a Cushion Robot Based on Fuzzy Path Planning With Safe Angular Velocity

This study proposes a new nonlinear tracking control method with safe angular velocity constraints for a cushion robot. A fuzzy path planning algorithm is investigated and a realtime desired motion path of obstacle avoidance is obtained. The angular velocity is constrained by the controller, so the planned path guarantees the safety of users. According to Lyapunov theory, the controller is designed to maintain stability in terms of solutions of linear matrix inequalities and the controller's performance with safe angular velocity constraints is derived.The simulation and experiment results confirm the effectiveness of the proposed method and verify that the angular velocity of the cushion robot provided safe motion with obstacle avoidance.

Path Planning of Continuum Robot Based on a New Improved Particle Swarm Optimization Algorithm

Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its body.So its path planning becomes more difficult even compared with hyper-redundant robots.In this paper a circular arc spline interpolating method is proposed for the robot shape description,and a new two-stage position-selectable-updating particle swarm optimization(TPPSO)algorithm is put forward to solve this path planning problem.The algorithm decomposes the standard PSO velocity’s single-step updating formula into twostage multi-point updating,specifically adopting three points as candidates and selecting the best one as the updated position in the first half stage,and similarly taking seven points as candidates and selecting the best one as the final position in the last half stage.This scheme refines and widens each particle’s searching trajectory,increases the updating speed of the individual best,and improves the converging speed and precision.Aiming at the optimization objective to minimize the sum of all the motion displacements of every segmental points and all the axial stretching or contracting displacements of every segment,the TPPSO algorithm is used to solve the path planning problem.The detailed solution procedure is presented.Numerical examples of five path planning cases show that the proposed algorithm is simple,robust,and efficient.

Path planning based on sliding window and variant A* algorithm for quadruped robot

In order to improve the adaptability of the quadruped robot in complex environments,a path planning method based on sliding window and variant A* algorithm for quadruped robot is presented. To improve the path planning efficiency and robot security,an incremental A* search algorithm( IA*) and the A* algorithm having obstacle grids extending( EA*) are proposed respectively. The IA* algorithm firstly searches an optimal path based on A* algorithm,then a new route from the current path to the new goal projection is added to generate a suboptimum route incrementally. In comparison with traditional method solving path planning problem from scratch,the IA* enables the robot to plan path more efficiently. EA* extends the obstacle by means of increasing grid g-value,which makes the route far away from the obstacle and avoids blocking the narrow passage. To navigate the robot running smoothly,a quadratic B-spline interpolation is applied to smooth the path.Simulation results illustrate that the IA* algorithm can increase the re-planning efficiency more than 5 times and demonstrate the effectiveness of the EA* algorithm.

Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots

为活动机器人计划的即时全球性最佳的路径的一个新奇方法基于蚂蚁殖民地系统(交流) 被建议算法。这个方法包括三步：第一步正在利用 MAKLINK 图理论建立活动机器人的空间模型，第二步正在利用 Dijkstra 算法发现一条非最优的没有碰撞的路径，并且第三步正在利用 ACS 算法优化非最优的路径的地点以便产生全球性最佳的路径。建议方法是有效的并且能在即时路径被使用活动机器人计划的计算机模拟实验表演的结果。建议方法比与优秀人材模型一起基于基因算法计划方法的路径处于集中速度，答案变化，动态集中行为，和计算效率有更好的性能，这被验证了。

Planning of circlelocus for multi-path/multi-layer welding robot with automatical error-correction

In this paper, a planning algorithm for multi path/multi layer circular locus is poposed. The algorithm is applied to weld the nipples on the header of boiler. Multi path/multi layer circular locus is planned according to three teaching points, which is lapped head on end to satisfy the requirement of technology. For the nipples wherever they are arranged radially or axially, even if there are errors caused by positioning and thermal deformations, providing that nipple's position and orientation relative to the teaching one can be measured, the multi path/multi layer circular locus can be planned without teaching any more. The algorithm has been applied in welding robot for manufacturing power station' boiler.

IMMUNE GENETIC ALGORITHM FOR THE PATH PLANNING OF TIGHTLY COORDINATED TWO-ROBOT MANIPULATORS

A novel algorithm, the immune genetic algorithm based on multi-agent, isproposed for the path planning of tightly coordinated two-robot manipulators, which constructsmainly immune operators accomplished by three steps: defining strategies and methods of multi-agent,calculating virtual forces acting on an agent, and constructing immune operators and performingimmunization during the evolutionary process. It is illustrated to be able to restrain thedegenerate phenomenon effectively and improve the searching ability with high converging speed.

Path planning for MIG surfacing of robot-based remanufacturing system

Robot-based remanufacturing system can scan the worn parts and develop the corresponding models, compare them with the standard model, calculate the weld deposit, implement welding path planning, and repair the worn parts with MIG surfacing automatically. This paper investigates the welding path planning after calibrating, scanning and model rebuilding. The following aspects are contained： introducing the planning principle, selecting the suitable welding process based on welding parameters （current and speed）, calculating welding overlap quantity by the superposition method. Also, it has been verified that good weld profile can be obtained with the optimized parameters.