Path modification in the existence of obstacles based on task requirements

A novel and efficient path planning scheme is proposed for designing a kinematically feasible path in the presence of obstacles based on task requirements. Geometrical analysis is used to derive geometric expressions describing the relationship between the robot workspace, obstacles, and the planned path. The freedom available according to task requirements is used to modify the infeasible portions of the planned path. To demonstrate the proposed scheme, a 6R (revolute) wrist-partitioned type of robot manipulator and a spherical obstacle are used as a case study. The results are then extended to general wrist-partitioned types of robot manipulators, and arbitrarily shaped or multiple obstacles. Simulations are performed to demonstrated the proposed schemes. >

Simulation of obstacles’ effect on industrial robots’ working space using genetic algorithm

A general method for computing minimum cost trajectory planning for industrial robot manipulators is presented. The aim is minimization of a cost function with constraints namely joint positions, velocities, jerks and torques by considering dynamic equations of motion. A clamped cubic spline curve is used to represent the trajectory. This is a non-linear constrained optimization problem with five objective functions, 30 constraints and 144 variables. The cost function is a weighted balance of transfer time, mean average of actuators efforts and power, singularity avoidance, joint jerks and joint accelerations. The problem is solved by two evolutionary techniques such as Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE). Numerical applications for a six link robotic manipulator --- STANFORD robot (pick and place operation) and a two link planar manipulator (motion in the presence of obstacles) are illustrated. The results obtained from the Proposed techniques (NSGA-II and DE) are compared for different values of weighting coefficients. The influences of the algorithm parameters and weight factors on algorithm performance are analyzed. The DE algorithm converges quickly than NSGA-II. Also DE algorithm gives better results than NSGA-II in majority of cases. A comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain the optimal solutions of any complex problem using DE algorithm.

Fault tolerance is increasingly important in modern industrial robotic manipulators, especially those operated in remote and hazardous environment.Faults in robotic manipulator can cause economic and serious damages.So the robots need the ability to detect as well as tolerate failures, allow effectively coping with internal failures and continue performing designated tasks without the need for immediate human intervention.This saves time and cost involved in repairing the robot.This type of autonomous fault tolerance is also useful for industrial robots in that it decreases down-time by tolerating failures, identifies faulty components or subsystems to speed up the repair process and prevents the robot from damaging the products being manufactured.To support these fault tolerant capabilities, methods of detecting and tolerating failures must be perfected in robot manipulator.A number of researchers have proposed fault detection/tolerance architectures for robotic manipulators using the model based analytical, and redundancy approach.One of the main issues in the design of fault detection system is to model the rigid link robotic manipulators with modeling uncertainties.In this paper, a new approach hybrid intelligence based fault detection/tolerance for robot manipulators is discussed.A learning architecture, with neural network as on-line approximates the off-nominal system behavior, which is used for monitoring the robotic system for the faults.This generates the residual by comparing the actual output from robot.Fuzzy inference system is applied to identify and tolerate the faults which provide the adoptive threshold under the varying conditions.The new concepts discussed were validated through simulation study using a Scorbot ER 5plus manipulator robot mat lab toolbox.

Full teleoperation of mobile robots during the execution of complex tasks not only demands high cognitive and physical effort but also generates less optimal trajectories compared to autonomous controllers. However, the use of the latter in cluttered and dynamically varying environments is still an open and challenging topic. This is due to several factors such as sensory measurement failures and rapid changes in task requirements. Shared-control approaches have been introduced to overcome these issues. However, these either present a strong decoupling that makes them still sensitive to unexpected events, or highly complex interfaces only accessible to expert users. In this work, we focus on the development of a novel and intuitive shared-control framework for target detection and control of mobile robots. The proposed framework merges the information coming from a teleoperation device with a stochastic evaluation of the desired goal to generate autonomous trajectories while keeping a human-in-control approach. This allows the operator to react in case of goal changes, sensor failures, or unexpected disturbances. The proposed approach is validated through several experiments both in simulation and in a real environment where the users try to reach a chosen goal in the presence of obstacles and unexpected disturbances. Operators receive both visual feedback of the environment and voice feedback of the goal estimation status while teleoperating a mobile robot through a control-pad. Results of the proposed method are compared to pure teleoperation proving a better time-efficiency and easiness-of-use of the presented approach.

A novel technique called laser simulator approach for visibility search graph-based path planning has been developed in this article to determine the optimum collision-free path in unknown environment. With such approach, it is possible to apply constraints on the mobile robot trajectory while navigating in complex terrains such as in factories and road environments, as the first work of its kind. The main advantage of this approach is the ability to be used for both global/local path planning in the presence of constraints and obstacles in unknown environments. The principle of the laser simulator approach with all possibilities and cases that could emerge during path planning is explained to determine the path from initial to destination positions in a two-dimensional map. In addition, a comparative study on the laser simulator approach, A* algorithm, Voronoi diagram with fast marching and PointBug algorithms was performed to show the benefits and drawbacks of the proposed approach. A case study on the utilization of the laser simulator in both global and local path planning has been applied in a road roundabout setting which is regarded as a complex environment for robot path planning. In global path planning, the path is generated within a grid map of the roundabout environment to select the path according to the respective road rules. It is also used to recognize the real roundabout from a sequence of images during local path planning in the real-world system. Results show that the performance of the proposed laser simulator approach in both global and local environments is achieved with low computational and path costs, in which the optimum path from the selected start position to the goal point is tracked accordingly in the presence of the obstacles.

This work introduces the design of an adaptive tracking control strategy for a 6-DOF Staubli TX2-60L industrial robot manipulator operating under dynamic model uncertainties caused by variable payloads. A complete kinematic and dynamic model of the robot is derived using Denavit–Hartenberg parameters and the Lagrangian formulation, respectively. To address the limitations of conventional control methods under payload variations, an adaptive controller is designed, which continuously updates its parameters based on real-time tracking errors. A Lyapunov stability analysis is conducted to rigorously establish that the closed-loop system guarantees convergent tracking errors and bounded internal states. Simulation studies with different payload scenarios, including no load, step load variation, and continuous load changes, show the performance of the proposed control strategy. The results confirm that the adaptive controller maintains precise trajectory tracking and robust stability despite significant model uncertainties, highlighting its suitability for industrial pick-and-place and high-precision automation tasks. Keywords: Robot manipulator, kinematic model, dynamic model, variable payloads, adaptive control, stability analysis.

An artificial neural network (ANN) using backpropagation algorithm is applied to solve inverse kinematics problems of industrial robot manipulator. 6R robot manipulator with offset wrist was chosen as industrial robot manipulator because geometric feature of this robot does not allow solving inverse kinematics problems analytically. In other words, there is no closed form solution for this problem. In order to define orientation of robot end-effector, three different representations are used here: homogeneous transformation matrix, Euler angles and equivalent angle axis. These representations were compared to obtain inverse kinematics solutions for 6R robot manipulator with offset wrist. Simulation results show that prediction performance from the approximation accuracy point of view is satisfactory with low effective errors based on 10 degrees data resolution

Workpiece placement plays a crucial role when performing complex surface machining task robotically. If the feasibility of a robotic task needs to be guaranteed, the maximum available capabilities should be higher than the joint capabilities required for task execution. This can be challenging, especially when performing a complex surface machining task with a collaborative robot, which tend to have lower motion capabilities than conventional industrial robots. Therefore, the kinematic and dynamic capabilities within the robot workspace should be evaluated prior to task execution and optimized considering specific task requirements. In order to estimate maximum directional kinematic capabilities considering the requirements of the surface machining task in a physically consistent and accurate way, the Decomposed Twist Feasibility (DTF) method will be used in this paper. Estimation of the total kinematic performance capabilities can be determined accurately and simply using this method, adjusted specifically for robotic surface machining purposes. In this study, we present the numerical results that prove the effectiveness of the DTF method in identifying the optimal placement of predetermined machining tasks within the robot’s workspace that requires lowest possible joint velocities for task execution. These findings highlight the practicality of the DTF method in enhancing the feasibility of complex robotic surface machining operations.

Finding realistic motions for redundant manipulators is essential for complex jobs such as home care and industrial assembly. Motion planning is complex when a task requires standing upright or moving through restricted spaces. This work provides an effective motion-planning strategy for 7-DOF manipulators that improves connections via redundancy. The analytic Cartesian-space-to-joint-space kinematic mapping models for 7-DOF redundant manipulators with diverse configurations are constructed first, and the feasible nodes are determined by sampling the Cartesian space without barriers to satisfy the task requirements. Each Cartesian-space sampling node can provide numerous feasible joint-space nodes because of the redundancy of the robot manipulators. To remove additional valid nodes from a singular position, joint configurations with the same end-effector position orientation are modified iteratively. Finally, we find the nearest nodes in the joint-space constraint manifold and build collision-free smooth pathways. The task constraint levels were varied for a 7-DOF manipulator in simulations and experiments. The proposed planner finds more viable nodes at the same end-position attitude than one-to-one projection. It does not require numerical iterations and achieves high planning efficiency and a high motion-planning success rate.

A fuzzy self-organizing map (SOM) network is proposed in this paper for visual motor control of a 7 degrees of freedom (DOF) robot manipulator. The inverse kinematic map from the image plane to joint angle space of a redundant manipulator is highly nonlinear and ill-posed in the sense that a typical end-effector position is associated with several joint angle vectors. In the proposed approach, the robot workspace in image plane is discretized into a number of fuzzy regions whose center locations and fuzzy membership values are determined using a Fuzzy C-Mean (FCM) clustering algorithm. SOM network then learns the inverse kinematics by on-line by associating a local linear map for each cluster. A novel learning algorithm has been proposed to make the robot manipulator to reach a target position. Any arbitrary level of accuracy can be achieved with a number of fine movements of the manipulator tip. These fine movements depend on the error between the target position and the current manipulator position. In particular, the fuzzy model is found to be better as compared to Kohonen self-organizing map (KSOM) based learning scheme proposed for visual motor control. Like existing KSOM learning schemes, the proposed scheme leads to a unique inverse kinematic solution even for a redundant manipulator. The proposed algorithms have been successfully implemented in real-time on a 7 DOF PowerCube robot manipulator, and results are found to concur with the theoretical findings.

As the development of automation industry, robot manipulators are needed to work in more and more complex and dynamic environments. An important issue to be considered is how to avoid static or moving obstacles in the workspace. Kinematically redundant manipulators are those having more degrees of freedom than required to perform end-effector moving tasks in a given workspace. Being dexterous and flexible, they have been used for avoiding obstacles or singularity, and optimizing various performance criteria in addition to tracking desired end-effector trajectories. Of those versatile applications, obstacle avoidance is extremely important for successful motion control in the presence of obstacles.

Robot kinematics deals with traits of redundancy, joint limitation, collision avoidance and singularity manifolds. Hence, kinematic analysis plays a vital role in the positioning task of a robot manipulator. This paper discusses the kinematic analysis to study and synthesize the motion of an ABB make IRB 1200 6DOF Industrial Robot Manipulator. The forward kinematic solution is calculated using DH parameters obtained from the main dimensions of the robot and its inverse kinematics is calculated purely algebraically. The results obtained are validated using ABB's official software, RobotStudio. The experimental studies are also done to verify the kinematic model developed.

Currently, an increasing number of robotic manipulators are discarded on the factory floor, because they are classified as unserviceable equipment, while still being able to be re-used. This problem occurs due to the fact that robot manufacturers use fully closed architecture control to their hardware and software systems. Thus, there is no interchangeability between the control cabinets and the programming languages used to generate kinematics, which make the repairs, modifications and implementations to system more expensive, due to these processes being dependent of each manufacturer. Therefore this paper proposes a methodology for the implementation of remanufacturing industrial robotic manipulators. In this methodology electronic components and electromechanical robot original can be re-used and its control systems are adapted to new technologies, using a fully open control architecture. Thus, the hardware devices are interchangeable with other processes and the programming language is not proprietary and can be modified or replaced by another.

Modifying the design of an existing system to meet the needs of a new task is a common activity in mechatronic system development. Often engineers seek to meet requirements for the new task via control design changes alone, but in many cases new requirements are impossible to meet using control design only; physical system design modifications must be considered. Plant-Limited Co-Design (PLCD) is a design methodology for meeting new requirements at minimum cost through limited physical system (plant) design changes in concert with control system redesign. The most influential plant changes are identified to narrow the set of candidate plant changes. PLCD provides quantitative evidence to support strategic plant design modification decisions, including tradeoff analyses of redesign cost and requirement violation. In this article the design of a counterbalanced robotic manipulator is used to illustrate successful PLCD application. A baseline system design is obtained that exploits synergy between manipulator passive dynamics and control to minimize energy consumption for a specific pick-and-place task. The baseline design cannot meet requirements for a second pick-and-place task through control design changes alone. A limited set of plant design changes is identified using sensitivity analysis, and the PLCD result meets the new requirements at a cost significantly less than complete system redesign.