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In this paper we present a new method for tool-path generation and adaptive interpolation for a three-axis CNC milling machine. This method satisfied the force and / or the error conditions by using a variable-direction variable-feedrate strategy based on Maximum Feedrate Map. The proposed adaptive interpolator modifies the cutting path and the moving direction according to the actual instantaneous position error in real time. It also adaptively slows down the speed of the cutting tool if the position error exceeds a permissible threshold so as to decrease the error. As the cutting points on the spline at each cycle are not calculated beforehand, memory requirement for the interpolator drastically reduces. The significance of the proposed method on the reduction of the cutting error in the presence of disturbance is demonstrated via computer simulations.

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The inertial forces and moments, due to the motion of robotic arms installed on a mobile base, lead to reaction forces on the moving base which may cause its unexpected motion. In this article, a method of designing a path of motion in the Cartesian space between the initial and final positions is presented which guarantees no reaction on the moving base. To this end, developing the system dynamics model, the moment equations are derived. Based on the conservation of momentum in the absence of any external force and moments, the angular motion due to the motion of robotic arms is solved. Then, based on the definition of reaction null-space map for dynamic coupling matrix, the joint speeds are projected to the reaction null-space, to obtain the joint speeds in this space. Next, using numerical integration of the obtained joint rates, the motion in the joint space with no reaction on the base is obtained. Therefore, motion of robotic arms according to these joint specifications, the total momentum of the system remains zero, and due to no reaction forces applied on the moving base, its position and attitude remains unchanged.

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In this paper, the problem of path planning for a special hyper-redundant manipulator with lockable joints is solved using particle swarm optimization. Two strategies have been used. In the first strategy, the path-planning problem is solved in two stages; first, the inverse kinematics of manipulator is solved with continuous PSO and then the sequence of switching is optimized by modified discrete PSO. The results of discrete PSO are compared to discrete GA to show the effectiveness of the discrete PSO. In order to implement multi-objective optimization in inverse kinematics calculation, linear fitness function and Vector Evaluated PSO (VEPSO) are used. In the second strategy, a novel approach in particle swarm optimization is proposed. In this approach, the integer and continuous value numbers are joined together in a single particle to form a hybrid particle. The results of two strategies were compared to show the convergence speed and performance of the second strategy which was proposed here.

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In this paper, the kinematic path planning of a special hyper-redundant manipulator with lockable joints is studied. In this manipulator the extra cables are replaced by a locking system to reduce weight of the structure and the number of actuators. In this research, the particle swarm optimization is used for path planning. In addition, the kinematic constraints such as joint limits are considered. In the first part of this paper, the minimum switch path planning is solved. This kind of path planning will decrease the vibration and energy consumption and increase the accuracy of manipulator. To validate the result, an innovative test is designed. According to the test results, the performance of the proposed method is shown. In the second part of the paper, the minimum time trajectory planning is studied based on the bang-bang theory. The inverse kinematic of manipulator is calculated such that the sum of legs length changes is decreased. Finally, the result of trajectory planning obtained from particle swarm optimization are compared to simulated annealing optimization results to confirm the performance and correctness of results.

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nonholonomic mobile robots are widely used in industrial environments due to their extended workspace. Also, to increase the productivity and efficiency of the mobile robot, their path planning is an important task, and is attracted attention of many of robotic scientists. In this paper, the optimal path planning of the wheeled mobile robots are performed considering their nonlinear dynamic equations and the nonholonomic constraints. Problem of the trajectory optimization is formulated, and conditions of the optimality are derived as a set of nonlinear differential equations by means of indirect method of optimal control method. Then, the optimality equations are solved numerically, and variant simulations are executed. To verify the simulation study, some experimental analysis are done for the Scout mobile robot and compared to the simulation results. The experimental analysis verifies the simulation results, and demonstrates the applicability of the proposed method for the optimal path planning of the mobile robots.

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In this study a safe and smooth path planning containing the slightest risk is considered for an Unmanned Underwater Vehicle (UUV). To do so, three smooth and continues functions resembling the three dimensional path are introduced and then their parameters are optimized using the particle swarm optimization method to find the safest possible path. For each point in space a numeric value is considered as vulnerability and the objective function is the integral of the vulnerability over the path produced. This path forms controlling signals which through a TSK fuzzy controller, the UUV is guided. The new arrangement of the propulsion vehicle subsurface was modeled. Since for the design of the controller, the parameters of the Under Water Vehicle dynamic system not used, so the control system is robust with respect to parameter Uncertainties. In the last section three environments with different complexities are considered to illustrate the creating process’s performance of the path and it is concluded that this method demonstrates desired performance in the development of a safe and smooth path through a harmful environment and the design of an adequate controller.

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The main purpose of this paper is to use the concept of manipulability index for finding the optimal link lengths of a surgical robot and then to plan an optimal path for it. This index depends on the configuration of the robot. In each configuration, the manipulability of the robot is calculated thorough the Forward and Inverse Kinematic and Jacobain. In the present paper, a nonlinear constrained optimization problem is formulated based on this index to find the optimum link lengths. The optimization problem is solved using Genetics Algorithm for entire work space of the robot. The manipulability index is calculated all over the workspace using the Mont-Carlo method. Afterward, using this set of optimal link lengths, the optimal path planning is presented in the Cartesian work space. This optimization is done by considering the manipulability criteria while the tracking error is minimized during performing an operation. The suggested procedure is implemented on a four degree of freedom robot manipulator. Several simulations scenarios are presented to demonstrate the capability of the procedure. The simulation results show the efficiency of the method.

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Regarding the progress in technology and increase in the capabilities of the robots, one of the main challenges in the field of robotics is the problem of real-time and collision-free path planning of robots. This paper focuses on the problem of path planning of a 3-DOF decoupled parallel robot called Tripteron in the presence of obstacles. The proposed algorithm is a synergy-based algorithm of convex optimization, disjunctive programming and model predictive control. This algorithm has many advantages compared to previous methods reported in the literature including not getting stuck in the local optimums and finding the global optimum and high computational speeds. Finally, the algorithm will be implemented on a model of the real robot. It should be mentioned that this algorithm has been implemented using Gurobi optimization package with C++ programming language in Qt Creator environment and the simulation of the parallel mechanism is performed by the CAD2MAT package for MATLAB. Obtained results reveal that the maximum computational time at each step is less that one second which, for this particular application, could be regarded as a real-time algorithm.

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In order to utilize robots for industrial tasks, designing a suitable path is necessary.Executing the path by the robot in the presence of obstacles, makes the path planning task a difficult one. In addition, path planning is a time consuming task and needs expertise to define certain path for each industrial job. In this paper, uses Jerk-minimum method, B-Spline curves, via-point, and obstacle avoidance algorithm to automatically generate a suitable and safe path for a simulated 7 degrees of freedom industrial manipulator.A user determines via-points for robot trajectory using a Kinect sensor,then a combination of Jerk-minimum method, B-Spline curves, a path is generated. This path is checked by an obstacle avoidance algorithm,and a final path is generated. The obstacle avoidance algorithm uses the inverse kinematic equation of the robot to modify the robot trajectory. One of the advantages of the proposed method is both to facilitate trajectory planning for the user and to create a smooth trajectory for the robotic arm.

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This paper deals with the collision-free path planning of planar parallel robot by avoiding mechanical interferences and obstacle within the workspace. For this purpose, an Artificial Potential Field approach is developed. As the main contribution of this paper, In order to circumvent the local minima problem of the potential fields, a novel approach is proposed which is a combination of Potential Field approach, Fuzzy Logic and also a novel algorithm consisting of Following Obstacle as well as Virtual Obstacle methods, as a hybrid method. Moreover, the inverse kinematic problem of the 3-RRR planar parallel robot is analyzed and then the aforementioned hybrid method is applied to this mechanism in singular-free case. It is worth mentioning that, in this paper, all the probable collisions, i.e., the collision between the mechanism and the obstacles and also among the links, are taken into accounts. Two general cases have been considered in collision-free path planning simulation; the first case considered a mobile robot in several workspaces and the second one was assigned to the 3-RRR planar parallel robot path planning. Results of the simulations, which are implemented in C programming language for the sake of real-time purposes. reveal that for the both cases, the newly proposed hybrid path planning method is efficient enough for the mobile robot, or the end-effector of the planar parallel robot to reach the goal without colliding with the obstacles.

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In this paper, the effects of the addition of an active toe joint on a 2D humanoid robot with heel-off and toe-off motions are studied. To this end, the trajectories of joints and links are designed firstly. After gait planning, the dynamic model of the humanoid robot in different phases of motion is derived using Kane and Lagrange methods. Then, the veracity of the derived dynamic model is demonstrated by two different methods. The under-study model, is in accordance with the features of SURENA III, which is a humanoid robot designed and fabricated at the Center of Advanced Systems and Technologies (CAST) located in University of Tehran. Afterward, the optimization procedure is done by selection of two different goal functions; one of them minimizes the energy consumption and the other maximizes the stability of the robot. At last, the obtained results are presented. According to the results, there is an optimum value for heel-off and toe-off angles in each velocity which minimizes the consumption of energy. The results also show that, the heel-off angle does not have any significant effects on the stability of the robot while increasing the toe-off angle improves the stability of motion. Finally, the effects of mass and length of the toe joint is inspected. These inspections suggest that heavier toe joints cause an increase in both energy consumption and stability of the robot while increasing the length of the toe joint does not have any effects on both goal functions.

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In this paper, design of an adaptive controller, as a combination of feedback linearization technique and Lyapunov stability theory, is presented for a parallel robot. Considering a three degree-of-freedom parallel mechanism of the robot, which serves pure translational motion for its end-effector, kinematic and constraint equations are derived. Then the dynamic model of the constrained system is extracted via Lagrange’s method to be used in the robot control. Two optimized trajectories are designed for the end-effector in the presence of some obstacles using harmony search algorithm to be tracked by the robot. An objective function is defined based on achieving to the shortest path and also avoiding collisions to the obstacles keeping a marginal distance from each. The first trajectory is a 2D path with four circular obstacles and the second is a 3D path with three spherical obstacles. Performance of the designed controller is simulated and studied in conditions including external disturbances and varying system parameters. The results show that the proposed adaptive controller has a suitable performance in control of the end-effector to track the designed trajectories in spite of external disturbances and also uncertainty and variation of the model parameters.

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In this paper, dynamic modeling, optimal path planning and control scheme on a redundant parallel cable robot is presented. Path planning in parallel robots necessitates the consideration of robot’s kinematics to discern the singularities in the workspace. Also, dynamics analysis is required to consider actuation constraints. To this end, kinematics and dynamics of cable driven redundant parallel robot is derived. In this modeling, cables are assumed to be rigid with negligible mass and hence, tension and sagging along the cable are neglected. Next, a sampling-based algorithm upon rapidly-exploring random tree is developed to increase the convergence rate. In this scheme, distance, epochs and safety are considered as optimization constraints. To evaluate the performance of the proposed algorithm in collision avoidance, a number of obstacles have been considered too. Tracking of the planned path has been handled using a feed-forward controller in the presence of obstacles. Regarding the redundancy feature of robot, a redundancy resolution scheme is considered for optimal force distribution. Path planning and control algorithms are implemented on the RoboCab (ARAS Lab.) and experimental results reveal the efficiency of the proposed schemes.

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Cooperation and autonomy are among the most important aspects of unmanned systems through which greater use of these system is possible. Most applications in civil market is related to government organizations requiring surveillance and inspection, such as coast guards, border patrol, emergency services and police. A cooperation algorithm is developed and simulated in this research for autonomous UAVs to track a dynamic target in an adversarial environment. First, a mathematical formulation is developed to represent the area of operation that contains various types of threats in a single framework. Then a search point guidance algorithm is developed by using a rule-based approach to guide every UAV to the way points created by the cooperation algorithm, with the requirements of completing mission, avoiding restricted areas, minimizing threat exposure level, considering the dynamic constraints of the UAVs and avoiding collision. The cooperation algorithm is designed based on a variable formation which depends on a cost function. The efficiency of the team is improved in the terms of increasing the area of coverage of the sensors, flexibility of the UAVs to search for better trajectories in terms of restricted area avoidance and threat exposure minimization, and improving the estimation. Finally, the performance of the algorithm is evaluated in a MATLAB environment, which includes the dynamics of vehicles, the models of sensor measurement and data communication and the discrete execution of the algorithms. The simulation results demonstrate that the proposed algorithms successfully generated the trajectories that satisfy the given mission objectives.

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This paper focused on the vehicle path planning in the highways and complex urban environments. At first, obstacles and road lines have been detected by sensors of the intelligent vehicle, thereupon the vehicle will be find the safe areas using the time distance method developed in this paper. Then, an appropriate path close to the intelligent decisions about human being would be chosen through the developed algorithm. There is the possibility of collision to surrounding vehicles in the areas where changing the lane is needed. Therefore, to prevent collision, a five orders polynomial curve is offered for each lane change maneuver. The reached maneuver is optimized based on the vehicle dynamic and allowed lateral acceleration. Finally, a suitable path to pass quite safely and without any collision through the obstacles is suggested. At the end, two main and different simulation scenarios included the lack of collision is verified by MATLAB software and the obtained path is controlled by the sliding mode controller. These simulations indicated effectiveness of this method. The lateral acceleration is obtained in allowed range for comfort of occupants in these scenarios.

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With the increasing number of road accidents and driver assistance systems development, the automated vehicles importance has increased more than ever. As the issue of automated vehicles comes up, attending to their safety, and the impact of the other vehicles in traffic flow on their performance dramatically increased. One of the most important problems for automated vehicles is designing and controlling the trajectory regarding the surrounding vehicles in transient dynamic traffic conditions during complicated maneuvers. Although various studies have been performed in the field of lane change in dynamic traffic conditions and even in critical high speed, considering the transient dynamic conditions has been limited to the beginning of the maneuver and no solution has been provided for the surrounding vehicles’ immediate changes during the maneuver. The algorithm presented in this paper is able to design new safe optimized trajectories according to the sudden decisions of the surrounding vehicles during the lane change maneuver, also ensures collision avoidance in the whole maneuver via vehicle’s simultaneous longitudinal and lateral control. After evaluating the decision-making unit’s performance by real driving tests, the presented algorithm is simulated with different scenarios in complicated transient dynamic traffic conditions by using MATLAB software and its desired performance has been proven in the dynamic environment of IPG CarMaker, in the presence of surrounding vehicles.

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Multi-robot path planning problem involves some challenges. One of them is the exponential increase in the size of the search space as a result of increasing the number of robots in the operating environment. Therefore, there is a need for algorithms with high computational performance to plan optimal and collision-free paths in a limited time. In this article, a centralized path planning algorithm is presented. The algorithm is a heuristic incremental search, in which the D* Lite algorithm has been adapted for the multi-robot case. The concept of occupancy time has been embedded into the environment model to avoid path interference. A centralized function has been designed to update the environment model and robot data. To evaluate the method, two groups of simulations of static and dynamic types were carried out. The static simulations focused on studying the effect of algorithm parameters, and it was shown that the algorithm can plan paths for up to 40 robots in an environment having 55 percent free space. The dynamic simulations were carried out in Gazebo, a real-time and dynamic physical simulator. The results were compared to a baseline method based on potential fields. The number of robots was increased to 14, and it was demonstrated that for 9 robots and more, the potential field approach either fails or has a rapid increase in computation time, while the proposed method can find feasible solutions in a limited time.

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The Artificial Potential Fields approach is amongst the widely used path planning methods in continuous environments. However, the implementation of it in multi-robot path planning encounters challenges such as the local-minima and an increase in traffic probability with the rise in the number of robots. The purpose of the proposed method is to improve multi-robot path planning in complex environments. A new adaptive potential function is introduced that reduces the probability of the robots entering an area at the same time and thus reducing the probability of traffic. Also, new potential functions have been proposed that lead to smoother paths with less traverse time when the robot encounters obstacles. In these functions, in addition to the position of robots and obstacles, heading of the robot and the position of the target are also considered. In order to evaluate this method, a distributed software architecture has been designed and implemented in the framework of the robot operating system. In this architecture, as robots move, new robots can join the operation or new tasks can be assigned to robots. Two series of real-time simulations are carried out in the Gazebo environment. The results show that the use of the proposed potential functions leads to a decrease in the convergence of the robots. In the simulation done for 2 robots, proposed method has resulted in a 35% reduction in the traversal time. While in case of 15 robots in the same map, a 50% reduction in the traversal time has been achieved.