Optimal Robot Path Research Articles

Robot path optimization for laser cladding forming

Purpose The purpose of this paper is to provide an optimization method of robot cladding path, which is helpful to solve the problem of path under-optimization in laser cladding forming (LCF) based on robot. Design/methodology/approach First, the error influence parameters need setting before the cladding path generation, and the model of seeking appropriate error influence parameters is established based on the particle swarm optimization (PSO). Second, to solve the problem of collapse during the LCF process, the reason for collapse is analyzed and a robot cladding path error optimization method based on the layer path interpolation is proposed. Finally, the simulation and experiments are carried out. Findings Under the premise of giving the expected error of stereo lithography (STL) model, the optimal range of the chord height and the angle control can be quickly found by using PSO algorithm. Aiming at the collapse problem in the laser cladding process, a robot cladding path optimization method based on the layer path interpolation is proposed. A four-layer path interpolation simulation and the contrast experiments before and after the path optimization are completed; the results show that the robot cladding path optimization method can solve the problem of the collapse in laser cladding. Practical implications Robot cladding path optimization is one of the key technologies of LCF, and the quality of the robot cladding path is affected by STL model error and the path optimization method. This paper proposed a robot cladding path optimization method for LCF. This method can be used in other additive manufacturing techniques. Originality/value The quality of cladding path is important for LCF; this paper first proposed the optimization method of the robot cladding path for LCF to solve the collapse problem.

Double-robot obstacle avoidance path optimization for welding process.

For path planning of two welding robots, intelligent robot path optimization with obstacle avoidance is introduced first, where the optimization objective is the shortest time. In the optimization process, grid method is used for modeling. Then, ant colony algorithm is applied as search strategy to realize obstacle avoidance between welding gun and workpiece. For obstacle avoidance of robot joints, the robot is modeled using the sphere and the capsule. Besides, two-level collision detection and geometrical collision avoidance are used to obtain collision free robots' path. At last, an improved particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the proposed strategy could improve the effectiveness of the path planning. It can be used to shorten the teaching time and strengthen offline programming ability.

Optimal path planning for mobile robots using oppositional invasive weed optimization

AbstractA mobile robot is an autonomous agent, capable of planning the path from source to destination in both, a known, or an unknown environment. In this paper, we presented a novel approach to find the optimal trajectory. We have hybridized oppositional‐based learning (OBL) with the evolutionary invasive weed optimization (IWO) technique to generate the optimal path for different robot(s). The navigation algorithm considered in this work is intelligent enough to fulfill the objective of minimizing the path length and the time to reach its specified goal. Oppositional IWO (OIWO) algorithm mimics the colonizing behavior of weed and uses the concept of quasi‐opposite number, the concept of OBL is to find the shortest path between source and destination of mobile robot(s). An objective function has been formulated that takes care of the optimal target‐seeking behavior as well as the obstacle avoidance of the mobile robot. In this technique, OBL considers the current population generated by IWO algorithm and its opposite population simultaneously to get better and faster convergence. The simulation and experimental results prove and validate the effectiveness of developed OIWO path planning algorithm.

Optimal mobile robot path planning in the presence of moving obstacles

This paper presents an optimisation method to search for the optimal trajectory of an unmanned mobile robot while avoiding stationary and moving obstacles that may be in collision route. In order to meet the kinematic restrictions of the vehicle, the path is estimated using a finite-dimensional approximating space generated by B-splines basis functions. A penalised continuous functional is used to convert the constrained minimisation problem into an unconstrained one. The optimisation is performed through a genetic algorithm that searches the finite-dimensional space of the B-splines coefficients which determine the trajectory to be travelled. Experimental results with linear and nonlinear moving obstacle fields illustrate the estimated optimal trajectories.

Optimal mobile robot path planning in the presence of moving obstacles

Optimal mobile robot path planning in the presence of moving obstacles

Research on Intelligent Welding Robot Path Optimization Based on GA and PSO Algorithms

To make the welding robot more reasonable and furthermore improve the productivity and reduce costs, two intelligent algorithms for welding path optimization, genetic algorithm (GA) and discrete particle swarm optimization, are proposed to optimize the welding robot path. Through the improved selection of the operator, the GA achieves the fastest iterative efficiency. By introducing the “swap operator” and “swap sequence” in the particle swarm optimization algorithm, the PSO algorithm is improved for the solution of the discrete problem (welding robot path planning) which is superior to the continuous optimization problem. Besides, for the better iterative efficiency of PSO, the parameters of traditional inertia weight are determined by a linear inertia weigh, which can improve the convergence performance of the algorithm. The modeling and solutions of the two algorithms are discussed in detail to illustrate the applications in the welding robot path optimization. In order to compare the pros and cons of the two algorithms, the same welding tasks are presented, and Matlab simulation is carried out. The simulation results show that both genetic algorithm and particle swarm optimization algorithm can obtain the optimal or near-optimal welding path by iterative calculations.

Optimal path planning for motion robots based on bees pollen optimization algorithm

ABSTRACTDue to interference phenomena among unnatural dimensions of the motion robots’ operations space, optimal path planning of them has to satisfy not just one criterion, but rather multi-objects. In this paper, we propose a novel multi-object approach for optimal mobile robot path planning, based on bees pollen optimizer (BPO). We consider two objects of distance and smooth path of the special plan for motion robots for constructing a minimization one. In operation environment for action robots, the location of the target and the obstacles are set up for the solution of BPO. The selected sequence of the mobile robot is a set of the chosen global best settlement in each iteration, which updates its archived data throughout the movement for motion robots in order. A series of simulations are executed in some environments for the best pathway once the robot reaches its goal. The results indicate that the proposed approach offered the robot path to its target without touching the obstacles, and the proposed method may be an alternative approach to optimize the motion robot path planning.

Robot path planning optimization for welding complex joints

In robotic welding, the planning and generation of the motion of the welding torch are crucial in welding complex joints. This paper presents an approach for optimal robot path planning of the centroid pass in welding a Y-joint. Relevant data associated with the welding passes is defined and managed to facilitate robot path planning and generation. With properly selected welding torch inclination angles and welding pass segmentation techniques, a solution space containing a number of robot paths is generated to account for functional redundancy and different welding robot configurations. A solution with minimum joint movement is determined using a beam search algorithm as the optimal robot path for each welding pass segment. A case study of robot path planning of the centroid pass of a scaled-down Y-joint is simulated and discussed.

Environment Detection and Path Planning Using the E-puck Robot

Automatic path planning is one of the most challenging problems confronted by autonomous robots. Generating optimal paths for autonomous robots are some of the heavily studied subjects in mobile robotics applications. This paper documents the implementation of a path planning project using a mobile robot in a structured environment. The environment is detected through a camera and then a roadmap of the environment is built using some algorithms. Finally a graph search algorithm called A* is implemented that searches through the roadmap and finds an optimal path for robot to move from start position to goal position avoiding obstacles

Navigation of underwater robot based on dynamically adaptive harmony search algorithm

The current research work has employed an evolutionary based novel navigational strategy to trace the collision free near optimal path for underwater robot in a three-dimensional scenario. The population based harmony search algorithm has been dynamically adapted and used to search next global best pose for underwater robot while obstacle is identified near about robot’s current pose. Each pose is evaluated based on their respective value for objective function which incorporates features of path length minimization as well as obstacle avoidance. Dynamic adaptation of control parameters and new perturbation schemes for solution vectors of harmony search has been proposed to strengthen both exploitation and randomization ability of present search process in a balanced manner. Such adaptive tuning process has found to be more effective for avoiding early convergence during underwater motion in comparison with performances of other popular variants of Harmony Search. The proposed path planning method has also shown better navigational performance in comparison with improved version of ant colony optimization and heuristic potential field method for avoiding static obstacles of different shape and sizes during underwater motion. Simulation studies and corresponding experimental verification for three-dimensional navigation are performed to check the accuracy, robustness and efficiency of proposed dynamically adaptive harmony search algorithm.

Intelligent welding robot path optimization based on discrete elite PSO

Rational path optimization of weld joints sequence can reduce welding time, improve the welding quality and productivity in robot welding process, especially when the number of weld joints is large. In this article, definition of welding robot path optimization is studied first. Then, PSO is used to solve welding robot path optimization after algorithm discretization and global optimization capability improvement based on elite strategy. The shortest path length and welding deformation were considered as the optimization criteria. For double welding robot welding path optimization, the influence with each other should be avoided for two welding robots. Hence, an optimization strategy was proposed to solve double welding robots path optimization problem with constrains of avoiding mutual influence. Simulation results show that the proposed optimization algorithm and strategy can promise desired optimization effect.

MOBILE ROBOT PATH OPTIMIZATION ALGORITHM USING VECTOR CALCULUS AND MAPPING OF 2 DIMENSIONAL SPACE

This research explores path integration in mobile robot navigation and path optimization technique using vector calculus. A simulated robot in a simulated environment is used to test the algorithm that is to be developed. The simulated robot is equipped with a sonar sensor and several infrared sensors on its chassis. Mobile robot navigation in an unknown environment is very crucial as It not only has to be concerned about reaching its destination but also to avoid obstacles that may be in the way. This algorithm can effectively allow a mobile robot to navigate an unknown environment without collision into obstacles.

Successive dynamic programming and subsequent spline optimization for smooth time optimal robot path tracking

Abstract. The time optimal path tracking for industrial robots regards the problem of generating trajectories that follow predefined end-effector (EE) paths in shortest time possible taking into account kinematic and dynamic constraints. The complicated tasks used in industrial applications lead to very long EE paths. At the same time smooth trajectories are mandatory in order to increase the service life. The consideration of jerk and torque rate restrictions, necessary to achieve smooth trajectories, causes enormous numerical effort, and increases computation times. This is in particular due to the high number of optimization variables required for long geometric paths. In this paper we propose an approach where the path is split into segments. For each individual segment a smooth time optimal trajectory is determined and represented by a spline. The overall trajectory is then found by assembling these splines to the solution for the whole path. Further we will show that by using splines, the jerks are automatically bounded so that the jerk constraints do not have to be imposed in the optimization, which reduces the computational complexity. We present experimental results for a six-axis industrial robot. The proposed approach provides smooth time optimal trajectories for arbitrary long geometric paths in an efficient way.

Robot Path Optimization Based on a Reference Model and Sigmoid Functions

The purpose of this study is to minimize the arc length for the path described by the model of a robot platform when the path is constrained to have smooth transitions given by sigmoid functions. The optimization required a proof of stability for the resulting control law, the selection of the best sigmoid function among ten functions, and the definition of two gains necessary to parameterize the control law. The optimization was carried out by simulating the system under several kinematic and dynamical conditions, and the best sigmoid function was a hyperbolic tangent. Thus, the motion control first implied the simulation of a reference model to define an optimal path, and later the control of an actual robot platform, which followed the optimal path. The use of the optimized path reduced the complexity of the controller while allowing natural and intuitive paths for the robot platform.

A Kind of Rolling Robot Path Plan Algorithm

This paper proposes a new rolling algorithm for path plan of mobile robot based on automatically shunt the ant algorithm.The goal node is mapped to the node nearby the boundary in the eyeshot of the robot, and make out the best partial path, which the robot goes ahead for a step. The algorithm will iterate one time when the robot arrives at the goal node. The robot will reach the destination along the optimal path. Simulation experiments illustrate that the algorithm can be used to plan the optimal path for mobile robot even in the complex and unknown static environment.

Rolling Path Plan of Mobile Robot Based on Automatic Diffluent Ant Algorithm

This paper proposes a new rolling algorithm for path plan of mobile robot based on automatically shunt the ant algorithm.The goal node is mapped to the node nearby the boundary in the eyeshot of the robot, and make out the best partial path, which the robot goes ahead for a step. The algorithm will iterate one time when the robot arrives at the goal node. The robot will reach the destination along the optimal path. Simulation experiments illustrate that the algorithm can be used to plan the optimal path for mobile robot even in the complex and unknown static environment.

Optimal Robot Path Planning for Multiple Goals Visiting Based on Tailored Genetic Algorithm

In real applications, mobile robot may be commanded to go to multiple goals to execute special commissions. This study analyzes the particular properties of this multiple goals visiting task and proposes a novel tailored genetic algorithm for optimal path planning for this task. In proposed algorithm, objectives for evaluating the path are energy consumption and idle time that are proposed in our previous work. Under the constraint of energy consumption, it will generate an optimal path that comprises as more goals as possible and as less idle time as possible. In this algorithm, customized chromosome representing a path and genetic operators including Repair, Cut and Deletion are developed and implemented. Afterwards, simulations are carried out to verify the effectiveness and applicability. Finally, analysis of simulation results is conducted and future work is addressed.

Cellular neural network-based thermal modelling for real-time robotic path planning

This paper presents a heat conduction method for optimal robot path planning by drawing an analogy between heat conduction and path planning of mobile robots. This method treats the target as a heat source to propagate the target activity through heat conduction. Obstacles in the configuration space are treated with Dirichlet boundary conditions such that the resultant heat flux automatically causes the robot to avoid them. A transient-state heat conduction model is established to generate the temperature field in the state space, guaranteeing that the target and obstacles remain at the peak and the bottom of the heat distribution, respectively. A cellular neural network is also established for the real-time modelling of the heat conduction process. An iterative heat flux method is established to generate an optimal path from the robot to the target. The proposed method can not only generate real-time, optimal and collision-free paths without any prior knowledge of the dynamic environment and without explicitly searching over the global free work space or searching collision paths, but it can also easily respond to the real-time changes in dynamic environments.

Optimal Motion Planning of Spot Welding Robot Applications

This paper discusses the problem of effective motion planning for industrial robots. The first part dealt with current method for off-line motion planning. In the second part the presented work is done by one of the simulation systems with automatic trajectory generation and off-line programming capability. A spot welding process is involved. The practical application of this step strongly depends on the method for robot path optimization with high accuracy, thus transform the path into a time and energy optimal robot program for the real world, which is discussed in the third step.

Path Planning with Motion Optimization for Car Body-in-White Industrial Robot Applications

This paper discusses the problem of effective motion planning for industrial robots. The first part dealt with current method for off-line motion planning. In the second part the presented work is done by one of the simulation systems with automatic trajectory generation and off-line programming capability. A spot welding process is involved. The practical application of this step strongly depends on the method for robot path optimization with high accuracy, thus transform the path into a time and energy optimal robot program for the real world, which is discussed in the third step.