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To deal with the problem of obstacle avoidance for redundant robots, an obstacle avoidance algorithm based on the internal motion of the 7-DOF redundant anthropomorphic arm is presented. The motion of that critical points move away from the closest points on the obstacles is defined as obstacle avoiding motion. Two transitioning variables were used...
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The performance of task-space tracking control of kinematically redundant robots regulating self-motion to ensure obstacle avoidance is studied and discussed. As the sub-task objective, the links of the kinematically redundant assistive robot should avoid any collisions with the patient that is being assisted. The shortcomings of the obstacle avoid...
Robots are rapidly becoming part of our lives, coexisting, interacting, and collaborating with humans in dynamic and unstructured environments. Mapping of human to robot motion has become increasingly important, as human demonstrations are employed in order to "teach" robots how to execute tasks both efficiently and anthropomorphically. Previous ma...
This paper addresses the problem of Learning from Demonstration (LfD) and subsequent robot safety control in an unstructured dynamic environment different from the demonstrations. Generally, LfD has been successfully exploited for task programming, but the existing methods have not solved the problem of allowing the entire arm to avoid obstacles wh...
This paper deals with the design of cooperative mobile manipulators, that are not simply considered as the union of an existing mobile platform with an arm, leading to a highly redundant system, but as a system redesigned from scratch with an original minimal kinematics suitable for robot-robot collaboration. In order to design cooperative mobile m...
Redundant motion, which is possible when robotic manipulators are over-actuated, can be used to control robot arms for a wide range of tasks. One of the best known methods for controlling redundancy is the null space projection, which assigns a priority while executing desired tasks. However, when the manipulator is projected into null space, its m...
Citations
... Furthermore, results show that the solution is subject to numerical oscillations, which can be minimized with some post-processing on the trajectories. We believe that these oscillations could be reduced, as is done in other works [28,44,45], by granting a smooth transition between the tasks, for example with a blending function instead of the current sudden transition. Moreover, from the sensitivity analysis, we have seen that the d min parameter, and, in general, the obstacle avoidance task, has a great influence on the fitness functions, thus reducing the robots' performances, which can be seen from the functions related to energy and jerk. ...
Controlling a chain of tethered mobile robots (TMRs) can be a challenging task. This kind of system can be considered kinematically as an open-chain robotic arm where the mobile robots are considered as a revolute joint and the tether is considered as a variable length link, using a prismatic joint. Thus, the TMRs problem is decoupled into two parallel problems: the equivalent robotic manipulator control and the tether shape computation. Kinematic redundancy is exploited in order to coordinate the motion of all mobile robots forming the chain, expressing the constraints acting on the mobile robots as secondary tasks for the equivalent robotic arm. Implementation in the Gazebo simulation environment shows that the methodology is capable of controlling the chain of TMRs in cluttered environments.
... El método de campos potenciales es ampliamente utilizado en el campo de navegación de robots móviles debido a su fácil implementación [7]. Sin embargo, también han sido implementados en simulación de manipuladores altamente redundantes [8], [9]. Asimismo, Rajendran [10] desarrolla un método de planificación de movimiento aplicado a manipuladores robóticos que se encuentran en espacios desorganizados y con muchos obstáculos. ...
This paper presents a new algorithm for obstacle avoidance tasks applied to a robotic manipulator of 6 degrees of freedom based on potential field algorithms. Firstly, this manipulator was designed using CAD software and it was intended to be used as our base model to develop the proposed algorithm. Then, the inverse kinematics model was developed using a multivariate iterative control process. Afterwards, the mathematical model was modified by adding a rotational vector. This vector was obtained by the repulsive forces between the obstacle and the six joints of the robot manipulator. Therefore, the manipulator was able to find possible routes that reached the final desired coordinate and avoided any possible obstacle along its path. To optimize these trajectories, a database was created of different trajectories. This database contains trajectories that depend on the initial, final and the obstacle coordinates, with the trajectories hyperparameters optimized. Finally, the simulations have shown that the manipulator was able to complete the task of reaching a point without falling in the obstacle points.
... The velocity of avoidance which in direct proportion to the gradient of optential field is used in the null-space movement so that can satisfy the task constraints. [10,11] only considered the distance between the closest point on the obstacle and the critical point in redundant robotic arm. In [10], the velocity of null-space movement is setted inversely propotional to this distance. ...
... [10,11] only considered the distance between the closest point on the obstacle and the critical point in redundant robotic arm. In [10], the velocity of null-space movement is setted inversely propotional to this distance. In [11], the velocity of null-space movement is setted in direct proportion to the gradient of this distance. ...
Task-constrained motion planning (TCMP) is involved in many practical applications such as openning the door, openning the drawer, twisting the screw and so on. Because of the trend of man-machine collaboration and the exist of dynamic in environments, planning the collision-free motions for task-constrained manipulation is a significative problem. This paper explores the TCMP issue for mobile manipulation, which uses a mobile base to enhance the working range and flexibility but simultaneously makes the problem harder than that of single manipulation because of additional dofs (degree of freedom). We propose an optimization-based method to plan the obstacle avoidance motion in real time. At first, the global robotic Jacobian matrix which combines the omnidirectional base and the robotic arm is derived. At second, model predictive control is used to plan the control rule in order to maximize the closest distance between obstacles and the mobile manipulator and minimize the velocities of null-space at the same time. We have deduced the model with 4 differential equations which represent the law of the distance over time. At third, the distances are calculated and sended to the model to calculate the velocity of each joint of the arm and the base using ACADO which is an open source toolkit. Using the velocities, the mobile manipulator can move away from the approaching people while still fix the end of the arm to manipulate tool at the same time. Our method is verified on the mobile manipulator which consists of four mecanum wheels, a base, an UR10 arm and four kinect cameras in Gazebo simulation with using ROS operation.
... One of the most appealing features of a redundant robot arm is its ability to execute internal movements in the joints without affecting the assigned movement in task space. Kinematic redundancy can be exploited in many different ways with respect to the execution of a particular task such as obstacle avoidance [3], minimization of execution time or energy consumption [4]. However, only few methods for robot motion control [5][6][7] consider explicitly the hard constraints on the motion in the joints inherent to industrial robots. ...
Robot arms with redundant degrees of freedom are well known to match the flexibility of motion inherent to the human hand. This is typically useful for most industrial robots where one of the design requirements is to execute work operations with more natural kinds of movements. Such work tasks are performed subject to different constraints, while the end- effector tracks a given path in task space. In the general case task constraints can be mapped into admissible ranges of variation of the joint variables. This paper elaborates a method for kinematic path control in sliding mode employing vector space methods. The sliding mode control makes use of the redundant degrees of freedom of a robot arm to satisfy the constraints of the joint variables during the motion of the end- effector along a prescribed path in task space. In fact the proposed method allows the robot arm to reconfigure similarly to a human hand movement, while retaining the assigned end- effector movement. The method is computationally efficient and it is suitable for kinematic control when the number of the degrees of freedom of the robot arm is greater than the dimension of the task space.
... From now on, the task space tracking will be referred to as the main-task objective and the optimization measure for self-motion as the sub-task objective. In one of the previous studies by Shen et al. [28], obstacle avoidance is applied in numerical simulations on a seven DoF robot manipulator. However, the control is accomplished in the kinematic level, unlike the control presented in this paper. ...
... where the positive definiteness of the inertia matrix was also utilized [21]. Equating (27) and (7) results in the following closed-loop error system 0 v p e K e K e + + = & && (28) where J J + = I was utilized. The proper choice of diagonal elements of K v and K p with s 2 + K v s + K p being a Hurwitz polynomial in Laplace variable s in (28) implies that the task space tracking error e goes to zero exponentially. ...
... Equating (27) and (7) results in the following closed-loop error system 0 v p e K e K e + + = & && (28) where J J + = I was utilized. The proper choice of diagonal elements of K v and K p with s 2 + K v s + K p being a Hurwitz polynomial in Laplace variable s in (28) implies that the task space tracking error e goes to zero exponentially. ...
The performance of task-space tracking control of kinematically redundant robots regulating self-motion to ensure obstacle avoidance is studied and discussed. As the sub-task objective, the links of the kinematically redundant assistive robot should avoid any collisions with the patient that is being assisted. The shortcomings of the obstacle avoidance algorithms are discussed and a new obstacle avoidance algorithm is proposed. The performance of the proposed algorithm is validated with tests that were carried out using the virtual model of a seven degrees-of-freedom robot arm. The test results indicate that the developed controller for the robot manipulator is successful in both accomplishing the main-task and the sub-task objectives.
Purpose
This paper aims to use redundant manipulators to solve the challenge of collision avoidance in construction operations such as welding and painting.
Design/methodology/approach
In this paper, a null-space-based task-priority adjustment approach is developed to avoid collisions. The method establishes the relative position of the obstacle and the robot arm by defining the “link space,” and then the priority of the collision avoidance task and the end-effector task is adjusted according to the relative position by introducing the null space task conversion factors.
Findings
Numerical simulations demonstrate that the proposed method can realize collision-free maneuvers for redundant manipulators and guarantee the tracking precision of the end-effector task. The experimental results show that the method can avoid dynamic obstacles in redundant manipulator welding tasks.
Originality/value
A new formula for task priority adjustment for collision avoidance of redundant manipulators is proposed, and the original task tracking accuracy is guaranteed under the premise of safety.
Anthropomorphic criteria are widely adopted for developing socially interactive robots since they can improve human capability to interpret and predict robot motion, with an impact on robot acceptability and human–robot interaction safety. Learning by demonstration approaches based on dynamic movement primitives are a suitable solution for planning the robot motion in human-like fashion and endow robots with generalization capabilities and robustness against perturbation. Objective of this work is to propose a new formulation of the learning by demonstration approach based on dynamic movement primitives (DMPs), called hybrid joint/Cartesian DMPs, for redundant robots with the twofold purpose of avoiding obstacles on the path and obtaining anthropomorphic motion in the joint as well as the task space. The proposed approach, suitable for assistive purposes, was experimentally validated on the anthropomorphic robot arm Kuka Light Weight Robot 4+. Trajectories recorded by an optoelectronic system (i.e. the BTS Smart D) from human demonstrators performing point-to-point reaching and pouring tasks were adopted to teach the robot how to move. A comparative analysis with current methods of learning by demonstration in the literature, i.e. Cartesian DMP and inverse kinematics, was performed. Performance indicators were introduced to (1) assess the robot accuracy of the motion performing, (2) evaluate the level of anthropomorphism of the computed motion, (3) measure the success rate in obstacle avoidance. Moreover, questionnaires were administered to ten human subjects who observed robot motion. The obtained results demonstrated that the proposed approach guarantees anthropomorphic motion of the robot both in the joint and in the task spaces ensuring obstacle avoidance along the robot kinematic chain. In particular, the anthropomorphic robot, while operating with the proposed method, exhibits behaviours in the joint space that are similar to the one recorded during the demonstration and has a level of anthropomorphism rather higher than the one obtained with Cartesian DMP and inverse kinematics (a configuration of the robot joints within the physiological limits is always ensured and the maximum convex hull created between the human and the robot arm is 0.0085 m\(^3\)). In presence of obstacles, an acceptable level of the Cartesian accuracy (\(Position~err<0.005\) m and \(Orientation~err<0.02\) rad) is achieved. The obstacles are avoided with a 100% of success. The questionnaire results showed that the users feel more comfortable and less nervous to interact with a robot that moves in human-like manner.
This research studies multiple inverse kinematics solutions for a 7-DOF human redundant manipulator with a special joint configuration. A method is proposed for determining the continuous joint angle vector by selecting the inverse solution from discrete multiple solutions to the continuous end path of the mechanical arm. The elbow angle constraint is introduced, and the mapping relationship from the elbow angle to the joint angle is established. Subspaces are found in the multiple solution spaces to avoid joints exceeding the limit to obtain elbow angle interval, then combined with the collision detection technique, subspaces are sought in multiple solution spaces to avoid collisions between robotic arms and obstacles. Two subspaces are then obtained, and with use of their intersection, all feasible manipulator inverse kinematic solutions that avoid the joint limit and the obstacles at a given pose are obtained. The above method explicitly determines the complete feasible kinematic inverse solution of redundant manipulators. Finally, the validity of the methods is verified via kinematic simulations.
Purpose
This paper aims to use the redundancy of a 7-DOF (degree of freedom) serial manipulator to solve motion planning problems along a given 6D Cartesian tool path, in the presence of geometric constraints, namely, obstacles and joint limits.
Design/methodology/approach
This paper describes an explicit expression of the task submanifolds for a 7-DOF redundant robot, and the submanifolds can be parameterized by two parameters with this explicit expression. Therefore, the global search method can find the feasible path on this parameterized graph.
Findings
The proposed planning algorithm is resolution complete and resolution optimal for 7-DOF manipulators, and the planned path can satisfy task constraint as well as avoiding singularity and collision. The experiments on Motoman SDA robot are reported to show the effectiveness.
Research limitations/implications
This algorithm is still time-consuming, and it can be improved by applying parallel collision detection method or lazy collision detection, adopting new constraints and implementing more effective graph search algorithms.
Originality/value
Compared with other task constrained planning methods, the proposed algorithm archives better performance. This method finds the explicit expression of the two-dimensional task sub-manifolds, so it’s resolution complete and resolution optimal.
