Trajectory Planning Of Robot Research Articles

Trajectory Planning and Execution of Delta Robot

<div><p class="4">The main purpose of this study is to plan and execute effective trajectory on the Delta robot through graphical user interface (GUI) and appropriate electronic circuits, and optimize the integration with the mechanical prototype designed and implemented by the robot laboratory of Nueva Granada Military University, while considering and respecting its different characteristics, such as mechanical structure, workspace, kinematics, dynamics and motion singularity allow the correct development and implementation of algorithms that can effectively describe and control the planned trajectory.</p></div>

The Application of Geodesic in Deployable Antenna Mesh Dividing

Space deployable antennna has the characteristics of Lightweight, large diameter, high precision etc, which can meet the requirements of the launch vehicle carrying and payload’s Electromagnetic performance. Antenna’s mesh dividing have the direct bearing on payload’s Electromagnetic performance, geodesic mesh dividing has the advantage of shortest length of mseh line. but the equation solving of geodesic is complicated,which Impeding the geodesic’s application In academic and engineering fields. in this paper, a method has be proposed to solve the difficulty of geodesic equation solving. the solving result can be used in antenna’s mesh dividing, meanwhile the solving result can be used in Robot trajectory planning, Air route planning,Navigation route planning,Architectural structure design,etc. great care should be taken in constructing both.

Q-Free Walk Ant Hybrid Architecture for Mobile Robot Path Planning in Dynamic Environment

This work provides a novel hybrid system for the trajectory planning of an autonomous mobile robot named Q-Free Walk Ant Hybrid Architecture (Q-FWAHA). The proposed approach combines two layers: a deliberative layer (global planning) and a reactive one (local planning), it is adapted to partially unknown environments (dynamic). It includes both reactive planning methods based on the Perception & Action principle (Sense & Act) and deliberative methods based on the Perception-Planning & Action principle (Sense-Plan & Act). The deliberative layer consists of a global planning algorithm and a model (a map) based on current knowledge of the environment. It is based on a method inspired by nature called Ant Colony Optimization (ACO) for optimal trajectory planning in known environments. The next layer is the reactive layer, it allows the robot to avoid collisions with dynamic obstacles by directly using sensory data. The reactive planning method used is a reinforcement learning approach for planning in dynamic environments; The Q-learning method. The deliberative layer is compared alone with an existing system for path planning, results show that the final path produced by our system is more efficient in terms of safety and energy saving. Simulations and experimental results to validate the proposed hybrid system are presented.

Coordinated trajectory planning of a dual-arm space robot with multiple avoidance constraints

This paper presents a coordinated trajectory planning method for a free-floating dual-arm space robot system. The research purpose of this paper is to enable a space robot to complete the specified goals while avoiding collision with an obstacle by planning the motion of the joints. The coordinated trajectory planning problem for a space robot is transformed into an optimization problem to find the optimal trajectory of the base and manipulators by using particle swarm optimization (PSO). Since the optimization method is used to find an optimal trajectory, it is unnecessary to calculate the position of the base and the inverse kinematics of the joints. Therefore, the kinematic singular problem in seeking the inverse solution can be avoided. The joint trajectories are parametrized by a sine function with seventh-order polynomial parameters. Multiple constraints are added to the objective function as penalty items, and a new increasing strategy for a penalty factor is proposed to balance the penalty and search ability under multiple constraints. Finally, numerical simulations are carried out to verify the effectiveness of the proposed method.

Trajectory planning and base attitude restoration of dual-arm free-floating space robot by enhanced bidirectional approach

When free-floating space robots perform space tasks, the satellite base attitude is disturbed by the dynamic coupling. The disturbance of the base orientation may affect the communication between the space robot and the control center on earth. In this paper, the enhanced bidirectional approach is proposed to plan the manipulator trajectory and eliminate the final base attitude variation. A novel acceleration level state equation for the nonholonomic problem is proposed, and a new intermediate variable-based Lyapunov function is derived and solved for smooth joint trajectory and restorable base trajectories. In the method, the state equation is first proposed for dual-arm robots with and without end constraints, and the system stability is analyzed to obtain the system input. The input modification further increases the system stability and simplifies the calculation complexity. Simulations are carried out in the end, and the proposed method is validated in minimizing final base attitude change and trajectory smoothness. Moreover, the minute internal force during the coordinated operation and the considerable computing efficiency increases the feasibility of the method during space tasks.

Adaptive trajectory control of an under-actuated snake robot

This article proposes an adaptive trajectory control method for under-actuated snake robots by simplifying the control and actuation systems of hyper-redundant robots. The under-actuated joints are formed by controlling the spatial rotation angles of multiple rigid joints in the joint group in order for them to synchronize. Based on the under-actuated configuration, a trajectory control algorithm is proposed and includes the following parts: (i) A control principle based on variable rod length is adopted so that the snake robot can track into a small working space; (ii) a trajectory planning of the snake robot in a fixed state at one end is realized by an iterative tractrix principle minimizing the joint displacement; and (iii) a fast return tracking control of the snake robot in any posture is realized by adopting the principles of the variable rod-length tractrix and the forced return iteration. A snake robot with an arm 2300 mm in length is designed and fabricated. The trajectory control simulation and experiments validate the correctness of the trajectory control algorithm and the rationality of the structural design.

Trajectory optimisation with musculoskeletal integration features for fracture reduction orthopaedic robot.

Human body is an integrated system of bones and muscles. To date, there are insufficient studies on the effect of muscles on the trajectory planning of orthopaedic robot. To this end, based on a Stewart-Gough platform (6-UPU) fracture reduction orthopaedic robot, a musculoskeletal trajectory optimisation method was constructed for the interference of soft tissue during the reduction process. Firstly, pose description of the fracture reduction orthopaedic robot was introduced, and its working space was analysed. Secondly, an improved Hill muscle theory was used to construct the musculoskeletal system, and finite element analysis (FEA) was carried out. Thirdly, particle swarm optimisation (PSO) with variable weights was imported, and fracture reduction trajectory planning was obtained by quintic polynomial in the workspace. Then mathematical model for trajectory optimisation was presented, and targets of musculoskeletal optimisation were extracted, which include muscle energy consumption, robot-assisted repositioning time and trajectory length. In this sense, musculoskeletal integration trajectory optimisation method was put forward. Finally, comparative optimisation simulation with skeleton only and bone and muscle together were tested, and safety experiment with musculoskeletal integration was conducted. A cone shape workspace was got, whose range can be defined as 635.14mm on the X axis, 720mm on the Y axis, and 240mm on the Z axis, respectively. Besides, different FEA displacements revealed the effect of bone and muscle on the movement of the robot. Moreover, the optimal results with and without muscle had showed different movement time: the former consumed about 27s, but the latter spent about 25s. Additionally, the speed and acceleration of the drive rods can be obtained from zero to zero during the reset. The results show that the optimised trajectory obtained with this method is safe and reliable. Furthermore, the presence of muscle has great influence on the trajectory of robot, which could prolong the reduction time so as to prevent the occurrence of uneven soft and hard traction rate problem. This paper could be helpful for the future trajectory planning study of fracture reduction orthopaedic robot.

Design of 3DOF Delta Parallel Capture Robot with High Speed and Light Weight

For sucking medicine bottles, according to the particularity of camber surface of the bottle and the sucker, in order to improve the speed, accuracy and stability of capture, this paper presents a 3 degree of freedom Delta parallel robot with high speed and light weight. First, coordinate system of the robot is established, kinematical equationes are obtained based on kinematics analysis and geometric parameter is determined according to specified operation space; Next, trajectory planning is proceeded applying double rounded corner door shape loop which could solve the problem of the wobbling of robot arms; Then, robot structure is designed based on lightweight theory using hollow rod and I-shaped arm; In the end, the prototype is processed with adjustable. Design results show that: capture range is 400 mm x 200 mm, maximum speed is 200 time/min, maximum acceleration is 50 m/s2. Setting operator parameter to the maximum value, repeated positioning experiment is completed. With repeated positioning accuracy 0.385 mm, it is strong evidence that the Delta parallel robot presented by this paper could capture targets accurately, even for smaller ones. The results from this study provide theoretical references for lightweight design and trajectory planning of Delta parallel robot, and because this Delta parallel robot uses only mechanical parts to make this accuracy with no electronic sensor and machine vision module, it lays the foundation for further improvement in accuracy of the Delta parallel robot.

Semiclosed-loop motion control with robust weld bead tracking for a spiral seam weld beads grinding robot

• A novel two-DOF platform for the seam weld beads grinding is designed. • A semiclosed-loop motion control method is proposed for accurate grinding. • Robust weld bead tracking improved the detection and grinding accuracy of welds. Grinding robots have been widely used in industrial manufacturing, but not in the production of large steel pipes. In this paper, a grinding robot is developed for the removal of the external and internal seam weld beads at the end of a spiral welded pipe. A two-degree-of-freedom linear motion platform is added to the end of a six-degree-of-freedom industrial robot to achieve high-precision grinding and to decouple the motions. A semiclosed-loop motion control method is proposed to control the position of the grinding mechanism under the spark spatter during grinding. Based on the identification method of weld bead feature points and the closed-loop control of position, the tracking method of seam weld beads is proposed to realize robust detection and grind planning. Two sets of experiments including tracking and grinding were carried out on both external and internal seam weld beads. The results show that the developed structure and proposed methods are effective and perform well. The grinding robot with the two-degree-of-freedom linear motion component makes the robot trajectory planning and decoupling control greatly convenient.

Comparison Study of the PSO and SBPSO on Universal Robot Trajectory Planning

Industrial robots were modified over the years. The benefit of robots is making production systems more efficient. Most methods of controlling robots have some limitations, such as stopping the robots. The robot stops by various reasons, such as collisions. The goal of this study is to study the comparison of improving the Artificial Potential Field (APF) by the traditional Particle Swarm Optimization (PSO) algorithm and the Serendipity-Based PSO (SBPSO) algorithm to control the path of a universal robot UR5 with collision avoidance. Already, the metaheuristic algorithm kinds deal with a premature convergence. This paper presents a new approach, which depends on the concept of serendipity and premature convergence applied to the path of the universal manipulator UR5 and also compares it with traditional the PSO. The features of the SBPSO algorithm prototype are formalized in this paper using the concept of serendipity in two dimensions: intelligence and chance. The results showed that the SBPSO is more efficient and has better convergence behavior than the traditional PSO for controlling the trajectory planning of the UR5 manipulator with obstacle avoidance.

Dynamic Simulation Modeling of Industrial Robot Kinematics in Industry 4.0

This paper studies the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment and guides the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment in the context of the research. To address the problem that each parameter error has different degrees of influence on the end position error, a method is proposed to calculate the influence weight of each parameter error on the end position error based on the MD-H error model. The error model is established based on the MD-H method and the principle of differential transformation, and then the function of uniform variation of six joint angles with time t is constructed to ensure that each linkage geometric parameter is involved in the motion causing error accumulation. Through the analysis of the robot marking process, the inverse solution is optimized for multiple solutions, and a unique engineering solution is obtained. Linear interpolation, parabolic interpolation, polynomial interpolation, and spline curve interpolation are performed on the results after multisolution optimization in the joint angle, and the pros and cons of various interpolation results are analyzed. The trajectory planning and simulation of industrial robots in the Industry 4.0 environment are carried out by using a special toolbox. The advantages and disadvantages of the two planning methods are compared, and the joint space trajectory planning method is selected to study the planning of its third and fifth polynomials. The kinetic characteristics of the robot were simulated and tested by experimental methods, and the reliability of the simulation results of the kinetic characteristics was verified. The kinematic solutions of industrial robots and the results of multisolution optimization are simulated. The methods, theories, and strategies studied in this paper are slightly modified to provide theoretical and practical support for another dynamic simulation modeling of industrial robot kinematics with various geometries.

Research Progress on Trajectory Planning of Fracture Reduction and Deformity Correction Robot

Research Progress on Trajectory Planning of Fracture Reduction and Deformity Correction Robot

Research on Trajectory Optimization of Upper Limb Rehabilitation Robot

For the current wide application of robots in the field of rehabilitation, in order to improve the auxiliary rehabilitation effect of upper limb movement disorder patients in the rehabilitation period and enhance the security of patients during use, this paper proposes a method for trajectory planning and optimization of upper limb rehabilitation robot, which can achieve good and safe auxiliary rehabilitation effect by modifying the tangent offset parameters. In order to solve the problem of redundant inflection points and poor smoothness of traditional A Star Algorithm, a hierarchical progressive algorithm optimization strategy is proposed, such as collision detection algorithm and dynamic tangent smoothing algorithm with variable tangent points. By using this strategy, the technical problems such as the judgment and deletion of redundant inflection points and the excessive corner of poor convergence of planned path are solved. The accuracy of optimal path judgment is significantly improved, and the safety performance and auxiliary rehabilitation effect of upper limb rehabilitation machine are significantly improved. In this paper, the kinematics modeling of upper limb rehabilitation robot is carried out by MATLAB, and the trajectory simulation of the robot is carried out, and a trajectory optimization scheme based on A Star Algorithm is proposed. The generated path conforms to the trajectory of the rehabilitation operation, and can effectively assist the rehabilitation movement, which provides a basis for kinematics.

Research on Inverse Kinematics of Manipulator Based on Neural Network

The inverse kinematics solution of manipulator is an important part of robot dynamics, trajectory planning and trajectory tracking control. This paper presents an inverse kinematics solution method of manipulator in which the RBF network is used. This method overcomes many defects of traditional inverse kinematics solution methods, such as low precision and slow convergence speed, and provides a new solution for the inverse kinematics of manipulator. Experimental results show that the RBF network has compact topological structure, separation and learning of structural parameters, fast convergence speed and global approximation ability, which fundamentally solves the local optimization problem of BP network. The scheme designed in this paper is simple and practical, and the trained parallel neural network has fast convergence speed and high precision, which can meet the requirements of high-speed inverse kinematics in the real-time control of manipulator.

Hybrid Strategy of Parallel Tea Picking Robot Trajectory Planning Based on Optimal Time and Jerk

Hybrid Strategy of Parallel Tea Picking Robot Trajectory Planning Based on Optimal Time and Jerk

Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity

This paper presents a new quantum-behaved particle swarm optimization (QPSO) algorithm for the trajectory planning task of mobile robotic vehicles in static and dynamic environments—it is called enhanced diversity particle swarm optimization (EDPSO). The main characteristic of this algorithm is that it has peaks of diversity in its population, making it possible to escape from local minima effectively, avoiding stagnation. Through the proposed PSO, it is possible to obtain safe and efficient routes, avoiding energy waste and maintaining system integrity in several possible applications. The parameters of the proposed algorithm were tuned using the benchmarking functions. The same functions were used to compare the algorithm with those already established in the literature. Once the proposed algorithm showed promising results, it was simulated in four environments, each with different complexities, presenting dangerous regions and terrains unsuitable for robot navigation, and a large number of obstacles or even moving objects. Further, the algorithms used for comparison were also simulated and the EDPSO presented satisfactory results. Through simulations it was possible to notice that the proposed approach resulted in collision-free and planned routes, and the algorithm presented increased exploration features owing to the diversity peaks that occur during the optimization.

Deep reinforcement learning-based rehabilitation robot trajectory planning with optimized reward functions

Deep reinforcement learning (DRL) provides a new solution for rehabilitation robot trajectory planning in the unstructured working environment, which can bring great convenience to patients. Previous researches mainly focused on optimization strategies but ignored the construction of reward functions, which leads to low efficiency. Different from traditional sparse reward function, this paper proposes two dense reward functions. First, azimuth reward function mainly provides a global guidance and reasonable constraints in the exploration. To further improve the efficiency, a process-oriented aspiration reward function is proposed, it is capable of accelerating the exploration process and avoid locally optimal solution. Experiments show that the proposed reward functions are able to accelerate the convergence rate by 38.4% on average with the mainstream DRL methods. The mean of convergence also increases by 9.5%, and the percentage of standard deviation decreases by 21.2%–23.3%. Results show that the proposed reward functions can significantly improve learning efficiency of DRL methods, and then provide practical possibility for automatic trajectory planning of rehabilitation robot.

Time-optimal cornering trajectory planning for car-like mobile robots containing actuator dynamics

AbstractWe establish a highly feasible algorithm for time-optimal cornering trajectory planning (TP) for car-like mobile robots (CLMRs) based on a dynamic model that contains actuator dynamics. First, we formulate an accurate dynamic model of a robot that contains DC motor actuators; this includes steering braking (caused by the lateral force of the front steering wheel) and two types of friction (viscous and Coulomb) under a nonslip condition. Our TP algorithm can utilize the full power of the DC motor actuators within proper pulse width modulation bounds and generated torque limits. Then, we establish an algorithm for a time-optimal cornering trajectory planning for CLMRs (TOCTP-CLMR). Our algorithm divides the trajectory into five sections comprising three turnings and two translations to minimize the travel distance. Then, we utilize the quickest rotation when turning to construct the time-optimal trajectory that satisfies the bang-bang principle. In addition, simulations are performed to demonstrate the validity of this method. Finally, we conduct open-loop experiments to validate our dynamic model and a trajectory tracking experiment to demonstrate the feasibility of the TOCTP-CLMR trajectory.

Research on trajectory planning of closed loop grouting robot

In order to promote building intelligence and solve the disadvantages of traditional grouting technology, the trajectory planning of closed-loop grouting robot was designed. The minimumsnap optimization function was used to optimize the A* algorithm to realize the 2D trajectory planning, which could obtain a smooth, continuous route, and planning diagram of time distribution, speed, acceleration, and jerk. Further, the weight function of the improved A* algorithm was adjusted to perform 3D trajectory planning to reduce redundant nodes in the route. A new approaching law adaptive sliding mode control method was used to achieve precise trajectory tracking of the robotic arm and reduce the problem of chattering in sliding mode control. Through the design of closed-loop grouting robot and the research of trajectory planning, the two-dimensional and three-dimensional paths of grouting robot could be optimized. The system could realize automatic grouting operation. It could promote the development of high efficiency and safety in the construction grouting industry.

SBMPO: Sampling Based Model Predictive Optimization for robot trajectory planning

<h2>Abstract</h2> Sampling-Based Model Predictive Control (SBMPO) is a novel nonlinear MPC (NMPC) approach that enables motion planning with dynamic models. This tool is also well suited to solve traditional MPC problems and has been tested in various situations ranging from robotics, task scheduling, resource management, combustion processes, and general optimization.