1,721,111 research outputs found
A simple technique to improve the set-point following performance of Predictive Functional Control
No full textIn this paper we present a simple technique to improve the set-point following performance of the basic Predictive Functional Control algorithm. The technique is based on a first-order-plus-dead-time model of the process and on a suitable selection of the set-point signal, so that a process variable transition from a steady-state value to another one is achieved in a predefined time interval. The additional design effort, which is practically negligible, is discussed. Illustrative examples are given to demonstrate the effectiveness of the proposed solution
Safety-Aware Time-Optimal Motion Planning With Uncertain Human State Estimation
Full text linkHuman awareness in robot motion planning is crucial for seamless interaction with humans. Many existing techniques slow clown, stop, or change the robot's trajectory locally to avoid collisions with humans. Although using the information on the human's state in the path planning phase could reduce future interference with the human's movements and make safety stops less frequent, such an approach is less widespread. This paper proposes a novel approach to embedding a human model in the robot's path planner. The method explicitly addresses the problem of minimizing the path execution time, including slowdowns and stops owed to the proximity of humans. For this purpose, it converts safety speed limits into configuration-space cost functions that drive the path's optimization. The costmap can be updated based on the observed or predicted state of the human. The method can handle deterministic and probabilistic representations of the human state and is independent of the prediction algorithm. Numerical and experimental results on an industrial collaborative cell demonstrate that the proposed approach consistently reduces the robot's execution time and avoids unnecessary safety speed reductions
Comparison of different sample-based motion planning methods in redundant robotic manipulators
No full textThe main objective of a motion planning algorithm is to find a collision-free path in the workspace of a robotic manipulator in a point-to-point motion. Among the various motion planning methods available, sample-based motion planning algorithms are easy to use, quick and powerful in redundant robotic systems applications. In this study, different sampling-based motion planning algorithms are employed to select the most appropriate method for efficient collision-free motion planning. As a case study, finding a collision-free robotic displacement for welding a main pipe with other intersecting pipes and joints is considered. The robotic manipulator employed in this study has seven degrees of freedom, where six degrees are related to the manipulator joints and one degree is related to its base linear movement suspended from ceiling. Five criteria, time, path length, path time, path smoothness and process time are used to evaluate the efficiency of different sample-based motion planning algorithms. Finally, a smaller set of more efficient algorithms are introduced based on the defined efficiency evaluation criteria
Adaptive Impedance Controller for Human-Robot Arbitration based on Cooperative Differential Game Theory
No full textInverse Optimal Control for the identification of human objective: a preparatory study for physical Human-Robot Interaction
No full textNowadays, many applications involving humans and robots working together require physical interaction. It is known that, during an interaction, the mutual understanding and knowledge of the partner's goal improves and allows natural interaction. For this purpose, this work proposes Inverse Optimal Control (IOC) to recover the cost function of a human performing a reaching task with a robot in passive impedance control. This work presents the potentialities and limitations of the presented IOC method to describe human objectives. This work represents a preparatory study toward smooth and natural physical Human-Robot Interaction (pHRI), intending to understand the basic information on humans' behavior
Inverse Kinematics of Redundant Manipulators With Dynamic Bounds on Joint Movements
Full text linkRedundant manipulators are usually required to perform tasks in the operational space, but collision-free path planning is computed in the configuration space. Limiting the deviation with respect to the collision-free configuration-space trajectory may allow the robot to avoid collisions without modifying the primary task. This letter proposes a method to guarantee that the solution of the inverse kinematic problem deviates from the nominal joint-space trajectory less than a desired threshold. The excursion limitation is ensured by means of linear constraints and the automatic regulation of the weights of secondary tasks. Numerical and experimental results prove the validness of the proposed approach
Obstacle avoidance of redundant robotic manipulators using safety ring concept
No full textObstacle avoidance is one of the basic, but computationally, costly issues of robotic science. In this paper, a safety ring around the work piece was defined, so that since the wrist of the robot is inside the ring, it is guaranteed that no collision will occur. Considering the wrist of the robot, instead of a tooltip, reduces the computational cost due to removal of the three last robot joints from calculations. The robot wrist is able to move freely inside the safety ring volume instead of a specific curve. The rotation of the end effector around its axis in some applications like welding, cutting, etc. is not important; therefore, this functional redundancy was used in inverse kinematic solution in order to search in the null space to find the optimal joint angles for moving the robot wrist inside the safety ring. In addition, inherent redundancy of the robot base movement on the rail was used to avoid joint limits. The algorithm was applied to a common industrial application i.e. intersecting pipes welding
A Robust Linear Control Strategy to Enhance Damping of a Series Elastic Actuator on a Collaborative Robot
No full textDealing with the physical interaction between humans and robots, Series Elastic Actuators (SEAs) are identified as one solution to overcome many limits, such as reducing contact forces or detect collisions. Nevertheless, the low-damping dynamic of a SEA can lead to undesired behaviours, especially during particular applications where a high level of precision is required. In this paper, a linear control architecture to enhance the damping performance of a SEA is presented. The proposed structure consists in a cascade control where loops are regulated using three types of controllers: PI, PD and a generalized controller specifically designed to damp oscillations. A frequency-domain approach with related constraints could not satisfy the time-domain goal in term of oscillation damping, for this reason an optimization problem able to consider them both is taken into account. A robust design is mandatory to the model mismatch introduced by neglecting coupling between motor. Therefore, robustness constraints are introduced in the optimization procedure. Indeed, the effectiveness of the control architecture is tested on a real compliant robot with six degrees of freedom equipped with as many SEAs. Each test aims to highlight the damping performance of the controlled system while the robot performs various tasks or it is subject to external disturbances
Human–Robot Role Arbitration via Differential Game Theory
Full text linkThe industry needs controllers that allow smooth and natural physical Human-Robot Interaction (pHRI) to make production scenarios more flexible and user-friendly. Within this context, particularly interesting is Role Arbitration, which is the mechanism that assigns the role of the leader to either the human or the robot. This paper investigates Game-Theory (GT) to model pHRI, and specifically, Cooperative Game Theory (CGT) and Non-Cooperative Game Theory (NCGT) are considered. This work proposes a possible solution to the Role Arbitration problem and defines a Role Arbitration framework based on differential game theory to allow pHRI. The proposed method can allow trajectory deformation according to human will, avoiding reaching dangerous situations such as collisions with environmental features, robot joints and workspace limits, and possibly safety constraints. Three sets of experiments are proposed to evaluate different situations and compared with two other standard methods for pHRI, the Impedance Control, and the Manual Guidance. Experiments show that with our Role Arbitration method, different situations can be handled safely and smoothly with a low human effort. In particular, the performances of the IMP and MG vary according to the task. In some cases, MG performs well, and IMP does not. In some others, IMP performs excellently, and MG does not. The proposed Role Arbitration controller performs well in all the cases, showing its superiority and generality. The proposed method generally requires less force and ensures better accuracy in performing all tasks than standard controllers. Note to Practitioners—This work presents a method that allows role arbitration for physical Human-Robot Interaction, motivated by the need to adjust the role of leader/follower in a shared task according to the specific phase of the task or the knowledge of one of the two agents. This method suits applications such as object co-transportation, which requires final precise positioning but allows some trajectory deformation on the fly. It can also handle situations where the carried obstacle occludes human sight, and the robot helps the human to avoid possible environmental obstacles and position the objects at the target pose precisely. Currently, this method does not consider external contact, which is likely to arise in many situations. Future studies will investigate the modeling and detection of external contacts to include them in the interaction models this work addresses
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