Cooperative Robots Research Articles

Adaptive patient-cooperative compliant control of lower limb rehabilitation robot

With the increase in the number of stroke patients, there is a growing demand for rehabilitation training. Robot-assisted training is expected to play a crucial role in meeting this demand. To ensure the safety and comfort of patients during rehabilitation training, it is important to have a patient-cooperative compliant control system for rehabilitation robots. In order to enhance the motion compliance of patients during rehabilitation training, a hierarchical adaptive patient-cooperative compliant control strategy that includes patient-passive exercise and patient-cooperative exercise is proposed. A low-level adaptive backstepping position controller is selected to ensure accurate tracking of the desired trajectory. At the high-level, an adaptive admittance controller is employed to plan the desired trajectory based on the interaction force between the patient and the robot. The results of the patient–robot cooperation experiment on a rehabilitation robot show a significant improvement in tracking trajectory, with a decrease of 76.45% in the dimensionless squared jerk (DSJ) and a decrease of 15.38% in the normalized root mean square deviation (NRMSD) when using the adaptive admittance controller. The proposed adaptive patient-cooperative control strategy effectively enhances the compliance of robot movements, thereby ensuring the safety and comfort of patients during rehabilitation training.

Current Developments in Human - Robot Cooperation and Sustainable Automation within the Construction Sector

Current Developments in Human - Robot Cooperation and Sustainable Automation within the Construction Sector

Predictive Control of Cooperative Robots Sharing Common Workspace

Predictive Control of Cooperative Robots Sharing Common Workspace

System software architecture for advancing human robot interaction by cloud services and multi-robot cooperation

Human-like interactions with robots based on Conversational AI facilitate assistance and teamwork in various contexts. Those interactions are further enhanced by utilizing physical presence and context from the robot's hardware. Robot cooperation is also especially useful, when software or hardware resources have to be shared in a multi-robot system. Therefore, we propose a modular software architecture for multi-robot cooperation that extends the integration of Conversational AI into Socially Assistive Robots, previously suggested by authors. It utilizes a flow-based approach that involves shared repositories and direct or message-driven communication to convey natural language transcriptions among robots in order to support their cooperation. By experiments we evaluated the cooperation between NAOqi based robots and Furhat robot. Our experimental results demonstrate architecture's modularity and adaptability to different cloud services, along with its effectiveness for interactions involving multiple robots.

Research on dual robot collaboration method based on improved double ant colony algorithm

Purpose This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations. Design/methodology/approach A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy. Findings The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems. Originality/value This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

Online Planning for Autonomous Mobile Robots with Different Objectives in Warehouse Commissioning Task

Recently, multi-agent systems have become widespread as essential technologies for various practical problems. An essential problem in multi-agent systems is collaborative automating picking and delivery operations in warehouses. The warehouse commissioning task involves finding specified items in a warehouse and moving them to a specified location using robots. This task is defined as a spatial task-allocation problem (SPATAP) based on a Markov decision process (MDP). It is considered a decentralized multi-agent system rather than a system that manages and optimizes agents in a central manner. Existing research on SPATAP involving modeling the environment as a MDP and applying Monte Carlo tree searches has shown that this approach is efficient. However, there has not been sufficient research into scenarios in which all agents are provided a common plan despite the fact that their actions are decided independently. Thus, previous studies have not considered cooperative robot behaviors with different goals, and the problem where each robot has different goals has not been studied extensively. In terms of the cooperative element, the item exchange approach has not been considered effectively in previous studies. Therefore, in this paper, we focus on the problem of each robot being assigned a different task to optimize the percentage of picking and delivering items in time in social situations . We propose an action-planning method based on the Monte Carlo tree search and an item-exchange method between agents. We also generate a simulator to evaluate the proposed methods. The results of simulations demonstrate that the achievement rate is improved in small- and medium-sized warehouses. However, the achievement rate did not improve in large warehouses because the average distance from the depot to the items increased.

Fuzzy Optimization Design of Adaptive Robust Control for Uncertain Cooperative Robots with Servo Constraints

Fuzzy Optimization Design of Adaptive Robust Control for Uncertain Cooperative Robots with Servo Constraints

Distributed Dynamic Task Allocation for Moving Target Tracking of Networked Mobile Robots Using k -WTA Network.

Tasks allocation plays a pivotal role in cooperative robotics. This study proposes a novel fully distributed task allocation method for target tracking, by which mobile robots only need to share state information with communication neighbors. The proposed method adopts a distributed k winners-take-all ( k -WTA) network to select the k mobile robots closest to the moving target to perform the target tracking task. In addition, an innovative robot control law is designed, incorporating speed feedback and nonlinear activation functions to achieve finite-time error convergence. Unlike previous approaches, our distributed task allocation method yields finite-time error convergence, does not rely on consensus filters, and eliminates the need for a central computing unit to get the k -WTA result during the control process. We demonstrate the effectiveness of the proposed method through theoretical analysis and simulations. Compared to traditional methods, our method leads to smaller total moving distances and speed norms, which underscores the significance of our method in enhancing the efficiency and performance of mobile robots in dynamic task allocation.

Skillful Manipulation of Electronic Musical-Note-Type Instrument Using Industrial Humanoid Robot

Industrial robots contribute significantly to productivity improvements, quality improvements, and cost reductions at production sites. Among the industrial robots, those that can cooperate with humans without safety measures or output limitations on the motors of their axes are known as cooperative robots. In the manufacturing field, there is a growing demand for production lines where humans and robots can coexist and cooperate instead of those where robots perform all processes on their own. In this study, we focus on a cooperative robot fabricated using a 3D resin printer and aim to improve its operation using the same tools as humans. As an example of such an application, we attempt to develop a sound-feedback-based motion for manipulating an electronic musical instrument called an “otamatone”. First, the hardware for grasping the object is created using a 3D printer, and notes on the modeling process are described. We then construct an advanced sound feedback system using the Robot Operating System (ROS) to identify the sounding position and pitch of the instrument. In this study, we propose a partial model-matching method for determining the Proportional–Integral–Derivative (PID) gains of the servomotors of each joint of a robot. Consequently, the accuracy of the robot’s motion improves and the accuracy of the intended musical performance is enhanced.

Research on Application of Dual-arm Cooperative Robot in Oil Drilling Field

Research on Application of Dual-arm Cooperative Robot in Oil Drilling Field

A Dual-Arm Robot Cooperation Framework Based on a Nonlinear Model Predictive Cooperative Control

Dual-arm robots show better task adaptability but face more constraints than single-arm robots. Inspired by human cooperation options, a nonlinear model predictive cooperative control (NMPCC) coupled with a cooperative index is proposed in this article. By adjusting the cooperative index, dual-arm cooperation can be classified into four modes: tracking, motion synchronization, impedance, and force priority. Thus, the cooperation operation problem is converted to a multiobjective optimization problem. Then, a nonlinear model predictive solver coupled with the ACADO toolkit is designed to solve the multiobjective optimization problem, where the robotic torque control input can be calculated in real time (less than 1 ms). The motion synchronization, cooperative transportation, and human dual-arm robot interaction experiments were conducted on dual Franka panda robots. Experiments reveal that the NMPCC control coupled with the cooperative index is easy to apply in dual-arm robots and can adapt to different complex manipulation tasks.

Optimal Motion Planning Under Dynamic Risk Region for Safe Human–Robot Cooperation

Optimal Motion Planning Under Dynamic Risk Region for Safe Human–Robot Cooperation

Human–Robot Cooperative Piano Playing With Learning-Based Real-Time Music Accompaniment

Human–Robot Cooperative Piano Playing With Learning-Based Real-Time Music Accompaniment

Analyzing Surgeon–Robot Cooperative Performance in Robot-Assisted Intravascular Catheterization

Analyzing Surgeon–Robot Cooperative Performance in Robot-Assisted Intravascular Catheterization

Probability-Based Strategy for a Football Multi-Agent Autonomous Robot System

The strategies of multi-autonomous cooperative robots in a football game can be solved in multiple ways. Still, the most common is the “Skills, Tactics and Plays (STP)” architecture, developed so that robots could easily cooperate based on a group of predefined plays, called the playbook. The development of the new strategy algorithm presented in this paper, used by the RoboCup Middle Size League LAR@MSL team, had a completely different approach from most other teams for multiple reasons. Contrary to the typical STP architecture, this strategy, called the Probability-Based Strategy (PBS), uses only skills and decides the outcome of the tactics and plays in real-time based on the probability of arbitrary values given to the possible actions in each situation. The action probability values also affect the robot’s positioning in a way that optimizes the overall probability of scoring a goal. It uses a centralized decision-making strategy rather than the robot’s self-control. The robot is still fully autonomous in the skills assigned to it and uses a communication system with the main computer to synchronize all robots. Also, calibration or any strategy improvements are independent of the robots themselves. The robots’ performance affects the results but does not interfere with the strategy outcome. Moreover, the strategy outcome depends primarily on the opponent team and the probability calibration for each action. The strategy presented has been fully implemented on the team and tested in multiple scenarios, such as simulators, a controlled environment, against humans in a simulator, and in the RoboCup competition.

Robust approximate constraint-following control design based on Udwadia–Kalaba approach and experimental validation for the joint module of cooperative robot

Robust approximate constraint-following control design based on Udwadia–Kalaba approach and experimental validation for the joint module of cooperative robot

Force-based organization and control scheme for the non-prehensile cooperative transportation of objects

AbstractOver decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots’ positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper’s purpose is to overcome these challenges using a novel force-based approach taking into account the robots’ actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object’s shape. The controller’s functionality and performance as well as the scheme’s versatility are demonstrated by several hardware experiments.

Experiments with cooperative robots that can detect object’s shape, color and size to perform tasks in industrial workplaces

Experiments with cooperative robots that can detect object’s shape, color and size to perform tasks in industrial workplaces

Cooperative Grape Harvesting Using Heterogeneous Autonomous Robots

The development of agricultural robots is an increasingly popular research field aiming at addressing the widespread labor shortages in the farming industry and the ever-increasing food production demands. In many cases, multiple cooperating robots can be deployed in order to reduce task duration, perform an operation not possible with a single robot, or perform an operation more effectively. Building on previous results, this application paper deals with a cooperation strategy that allows two heterogeneous robots to cooperatively carry out grape harvesting, and its implementation is demonstrated. More specifically, the cooperative grape harvesting task involves two heterogeneous robots, where one robot (i.e., the expert) is assigned the grape harvesting task, whereas the second robot (i.e., the helper) is tasked with supporting the harvesting task by carrying the harvested grapes. The proposed cooperative harvesting methodology ensures safe and effective interactions between the robots. Field experiments have been conducted in order firstly to validate the effectiveness of the coordinated navigation algorithm and secondly to demonstrate the proposed cooperative harvesting method. The paper reports on the conclusions drawn from the field experiments, and recommendations for future enhancements are made. The potential of sophisticated as well as explainable decision-making based on logic for enhancing the cooperation of autonomous robots in agricultural applications is discussed in the context of mathematical lattice theory.

Factors affecting trust in the transparency index for stable and intuitive physical human–robot cooperation

Safety, stability, and intuitive control are the three most basic requirements for the design of a physical human–robot cooperation (HRC) system. High transparency is one of the main design objectives for dynamically coupled HRC systems. However, determinants of system transparency and its influence on system performance are unachievable with the current methods available. This paper presents an overview of admittance control as a method of physical interaction control between robots and human operators, in particular on the analysis of the influencing factors to the computational transparency index. The influence of frequency, sampling point number, and cutoff frequency of the weighting function on the transparency of the admittance controller were analyzed and experimentally verified. The controller was implemented on a four degree-of-freedom interactive manipulator to verify system transparency and stability. The experimental results validate the proposed framework.