Robot Control Algorithms Research Articles

Clinical experimental research on adaptive robot-aided therapy control methods for upper-limb rehabilitation

SUMMARYThis study presents novel robotic therapy control algorithms for upper-limb rehabilitation, using newly developed passive and progressive resistance therapy modes. A fuzzy-logic based proportional-integral-derivative (PID) position control strategy, integrating a patient's biomechanical feedback into the control loop, is proposed for passive movements. This allows the robot to smoothly stretch the impaired limb through increasingly rigorous training trajectories. A fuzzy adaptive impedance force controller is addressed in the progressive resistance muscle strength training and the adaptive resistive force is generated according to the impaired limb's muscle strength recovery level, characterized by the online estimated impaired limb's bio-damping and bio-stiffness. The proposed methods are verified with a custom constructed therapeutic robot system featuring a Barrett WAM™ compliant manipulator. Twenty-four recruited stroke subjects were randomly allocated in experimental and control groups and enrolled in a 20-week rehabilitation training program. Preliminary results show that the proposed therapy control strategies can not only improve the impaired limb's joint range of motion but also enhance its muscle strength.

MAINBOT – Mobile Robots for Inspection and Maintenance in Extensive Industrial Plants

MAINBOT project is developing service robots applications to autonomously execute inspection tasks in extensive industrial plants in equipment that is arranged horizontally (using ground robots) or vertically (climbing robots). MAINBOT aims at using already available robotic solutions to deploy innovative systems in order to fulfill project industrial objectives: to provide a means to help measuring several physical parameters in multiple points by autonomous robots, able to navigate and climb structures, handling sensors or special non destructive testing equipment.MAINBOT will validate the proposed solutions in two solar plants (cylindrical-parabolic collectors and central tower), that are very demanding from mobile manipulation point of view mainly due to the extension (e.g. a thermal solar plant of 50Mw, seven hours of storage, with 400 hectares, 400.000 mirrors, 180km of absorber tubes, 140m tower height), the variability of conditions (outdoor, day-night), safety requirements, etc. The objective is to increase the efficiency of the installation by improving the inspection procedures and technologies. Robot capabilities are developed at different levels: (1) Simulation: realistic testing environments are created in order to validate the algorithms developed for the project using available robot, sensors and application environments. (2) Autonomous navigation: Hybrid (topological-metric) localization and planning algorithms are integrated in order to manage the huge extensions. (3) Manipulation: Robot arm movement planning and control algorithms are developed for positioning sensing equipment with accuracy and collision avoidance. (4) Interoperability: Mechanisms to integrate the heterogeneous systems taking part in the robot operation, from third party inspection equipments to the end user maintenance planning. (5) Non-Destructive Inspection: based on eddy current and thermography, detection algorithms are developed in order to provide automatic inspection abilities to the robots.

네트워크 연결성 유지를 위한 군집 로봇의 행동 제어 알고리즘

In swarm robot systems, it is vital to maintain network connectivity to ensure cooperative behavior between robots. This paper deals with the behavior control algorithm of the swarm robots for maintaining network connectivity. To do this, we divide swarm robots into search-robots, base-robots, and relay-robots. Using these robots, we propose behavior control algorithm to maintain network connectivity. The behavior control algorithms to maintain network connectivity are proposed for the local path planning using virtual force and global path planning using the Delaunay triangulation, respectively. Finally, we demonstrate the effectiveness and applicability of the proposed method through some simulations.

A Fast and Unified Method to Find a Minimum-Jerk Robot Joint Trajectory Using Particle Swarm Optimization

In robot trajectory planning, finding the minimum-jerk joint trajectory is a crucial issue in robotics because most robots are asked to perform a smooth trajectory. Jerk, the third derivative of joint position of a trajectory, influences how smoothly and efficiently a robot moves. Thus, the minimum-jerk joint trajectory makes the robot control algorithm simple and robust. To find the minimum-jerk joint trajectory, it has been formulated as an optimization problem constrained by joint inter-knot parameters including initial joint displacement and velocity, intermediate joint displacement, and final joint displacement and velocity. In this paper, we propose a fast and unified approach based on particle swarm optimization (PSO) with K-means clustering to solve the near-optimal solution of a minimum-jerk joint trajectory. This work differs from previous work in its fast computation and unified methodology. Computer simulations were conducted and showed the competent performance of our approach on a six degree-of-freedom robot manipulator.

The Research of Flight Control Algorithm of Flight-Sliding Type Inspection Robot

In order to further optimize the methods of patrol and inspection in transmission line, Flight-Sliding type inspection robot is designed, which combines the advantages of Quadrotor and inspection robot. The robot has a flight and online inspection function. Due to overshoot phenomenon can not occur in the process of the robot flying to power lines and hanging, Fuzzy PID control algorithm is selected. The algorithm complies with the requirements of flight control. Matlab simulations indicate that Fuzzy PID control algorithm is effective in flight control of Flight-Sliding type inspection robot.

An integrated robust probing motion planning and control scheme: A tube-based MPC approach

‘This paper introduces the integration of a probing scheme into a robust MPC-based robot motion planning and control algorithm. The proposed solution tackles the output-feedback tube-based MPC problem using the partially-closed loop strategy to incorporate future measurements in a computationally efficient manner. This combination will provide not only a robust controller but also avoids overly conservative planning which is a drawback of the original implementation of the output-feedback tube-based MPC. The proposed solution is composed of two controllers: (i) a nominal MPC controller with probing feature to plan a globally convergent trajectory in conjunction with active localization, and (ii) an ancillary MPC controller to stabilize the robot motion around the planned trajectory. The performance and real-time implementation of the proposed planning and control algorithms have been verified through both extensive numerical simulations and experiments with a mobile robot.

Implementation of Dynamic Simulation System on Six DOF Industrial Robot

A dynamic simulation system whose model objects were industrial robot was designed. An advanced and quickly inverse kinematic algorithm was used to solve inverse kinematic equation for 6DOF industrial robot, the time of solve was improved. The method of trajectory planning was developed. The vision simulation for industrial robot was completed by Creator and Vega software. Simulation result shows that the requirement of kinematic control has been achieved and provides an intuitive and convenient tool for research of the kinematic control algorithm for industrial robot.

Mobile Robot Destination Generation by Tracking a Remote Controller Using a Vision-aided Inertial Navigation Algorithm

A new remote control algorithm for a mobile robot is proposed, where a remote controller consists of a camera and inertial sensors. Initially the relative position and orientation of a robot is estimated by capturing four circle landmarks on the plate of the robot. When the remote controller moves to point to the destination, the camera pointing trajectory is estimated using an inertial navigation algorithm. The destination is transmitted wirelessly to the robot and then the robot is controlled to move to the destination. A quick movement of the remote controller is possible since the destination is estimated using inertial sensors. Also unlike the vision only control, the robot can be out of camera's range of view.

두개골 천공을 위한 NeuroMate 로봇의 경로 제어

This paper presents a linear path control algorithm for NeuroMate robot in a skull drilling system. For the path control inverse kinematics of the robot is analyzed and a linear interpolation algorithm is presented. A geometric approach is used for solving inverse kinematic equations for the robot. Four feasible solutions are found through the approach. The approach gives geometric insights for selecting the best solution from the feasible solutions. The presented linear interpolation algorithm computes a next position considering current velocity and remaining distance to the target position. Presented algorithm is implemented and tested in a skull drilling system.

A new hybrid intelligent control algorithm for a seven-link biped walking robot

In recent years, many studies on bipedal walking robots and control algorithms have been conducted. However, different conditions and circumstances that have to be taken into account make the control of biped walking robots a big challenge. This paper proposes the implementation of a new hybrid intelligent control approach for a seven-link biped walking robot to track the specified trajectory based on a compensatory neurofuzzy network and fuzzy controller. This algorithm consists of two main parts: the feedforward compensator includes an integrated compensatory neurofuzzy network for identification of the inverse dynamics model which attempts to cancel the dynamics of the robot, and the feedback controller which includes a Mamdani-type fuzzy controller to compensate the modeling error and the effect of noise on the system. Moreover, a new style of membership functions distribution for fuzzy controller with noise reduction capability is proposed and its influence on the performance of the robot under noise-free and noisy conditions is investigated. Simulations performed on a biped robot illustrate the methods and their performance. The results confirm the high tracking capability and effectiveness of the proposed control approach.

얇은막대 배치작업에 대한 N-R 과 EKF 방법을 이용하여 개발한 로봇 비젼 제어알고리즘의 평가

실제 산업현장에서 비젼 시스템을 적용하기에는 로봇 비젼 제어알고리즘의 기구학모델의 정확도, 로봇이 움직이는 동안 카메라 초점거리와 방위에 대한 보정, 3 차원 물리적 좌표에서 2 차원 카메라 좌표로의 매핑에 대한 이해 등 해결해야 할 많은 문제점들이 있다. 본 논문에 제안된 비젼 시스템 모델은 카메라와 로봇 사이의 상대적인 위치가 알려지지 않아도 제어가 가능하고, 카메라 보정 문제를 해결하기 위해 6 개의 카메라 매개변수를 가지는 비젼 시스템 모델을 제시하였으며, 이를 이용하여 로봇 비젼 제어알고리즘 개발에 N-R 방법과 EKF 방법을 적용하였다. 최종적으로 N-R 과 EKF 방법에 의하여 개발된 로봇 비젼 제어 알고리즘의 위치 정밀도와 데이터 처리 시간을 얇은 막대 배치작업을 수행하여 비교하였다.

A Minimum-Energy Consumption Control Algorithm for Omni-Directional Mobile Robots

A minimum-energy consumption control algorithm is applied to motion control of a four-wheel drive omni-directional mobile robot (FDOMR) in actual application environment. After establishing the robot’s dynamic equations with motor model, we have chosen a practical cost function as the total energy drawn from the batteries. Considering the translation and rotation, we have found out the velocity curve by optimal control theory. Various simulations are performed and the consumed energy is compared to the normal control method that without minimum energy consumption control. Simulation results reveal that the energy saving is much more compared to the traditional control, the operational time of the FDOMR with given batteries is lengthened and the efficiency of battery is improved.

Distributed learning with biogeography-based optimization: Markov modeling and robot control

Biogeography-based optimization (BBO) is an evolutionary algorithm that is motivated by biogeography, which is the science that describes how biological species are geographically distributed. We extend the standard BBO algorithm to distributed learning, which does not require centralized coordination of the population. We call this new algorithm distributed BBO (DBBO). We derive a Markov model for DBBO, which provides an exact mathematical model of the DBBO population in the limit as the generation number approaches infinity. We use standard benchmark functions to compare BBO and DBBO with several other evolutionary optimization algorithms, and we show that BBO and DBBO give competitive results, especially for multimodal problems. Benchmark results show that DBBO performance is almost identical to BBO. We also demonstrate DBBO on a real-world application, which is the optimization of robot control algorithms, using both simulated and experimental mobile robots.

Development of Motion Control Algorithm of Walking Support Robot Based on User-Robot Positional Displacement

The purpose of this study is to develop motion control algorithm of a walking support robot to prevent user's fall by measuring relative distance between the user and the robot. The proposed algorithm is composed of following three steps: (i) User's walking stability which is computed based on the center of gravity is evaluated by two discriminant functions which estimate the user's walking posture. The discriminant functions are obtained by performing discriminant analysis on the distance data between representative points of the user's body and the robot. (ii) The robot warns the user by lightning LED lights to make the user recognize the unstable walking state according to the detection of his/her unstable walking posture based on the result of the walking stability evaluation. (iii) The robot prevents the user's fall and assists the user's motion to stabilize the user's walking posture. To confirm the effectiveness of the proposed algorithm, fall prevention experiments are conducted with the experimental system which simulates a human walking motion by using a humanoid doll and an arm robot. The results of experiments show that the proposed algorithm is effective for the fall prevention of the user.

近傍のロボットと障害物の識別を必要としない移動ロボット群の制御アルゴリズム

近傍のロボットと障害物の識別を必要としない移動ロボット群の制御アルゴリズム

Dynamic Formation Control for Car-like Mobile Robots

For the formation problem of the car-like mobile robots, a dynamic coordinated control algorithm for multiple robots is proposed. Firstly, a model of trajectory tracking system for the follower robot is established through the virtual robot, which is generated according to the desired formation parameters of the leader and follower robots. Then the robot's kinematics model is transformed into the chain form, and the Lyapunov function of the trajectory tracking system is constructed via the backstepping method. The trajectory tracking controller for the follower robot is obtained by making this Lyapunov function negative-definite. The formation control of the car-like mobile robots is realized in this way. A 3D simulation testbed is designed by the Microsoft Robotics Developer Studio 4(MRDS4), and the effectiveness of the proposed method is demonstrated through 3 sets of experiments.

Дискретный алгоритм управления по отклонению мобильным роботом с омниколесами

Дискретный алгоритм управления по отклонению мобильным роботом с омниколесами

A Comparative Analysis of Speed Profile Models for Wrist Pointing Movements

Following two decades of design and clinical research on robot-mediated therapy for the shoulder and elbow, therapeutic robotic devices for other joints are being proposed: several research groups including ours have designed robots for the wrist, either to be used as stand-alone devices or in conjunction with shoulder and elbow devices. However, in contrast with robots for the shoulder and elbow which were able to take advantage of descriptive kinematic models developed in neuroscience for the past 30 years, design of wrist robots controllers cannot rely on similar prior art: wrist movement kinematics has been largely unexplored. This study aimed at examining speed profiles of fast, visually evoked, visually guided, target-directed human wrist pointing movements. One thousand three-hundred ninety-eight (1398) trials were recorded from seven unimpaired subjects who performed center-out flexion/extension and abduction/adduction wrist movements and fitted with 19 models previously proposed for describing reaching speed profiles. A nonlinear, least squares optimization procedure extracted parameters' sets that minimized error between experimental and reconstructed data. Models' performances were compared based on their ability to reconstruct experimental data. Results suggest that the support-bounded lognormal is the best model for speed profiles of fast, wrist pointing movements. Applications include design of control algorithms for therapeutic wrist robots and quantitative metrics of motor recovery.

Modeling the insect mushroom bodies: Application to a delayed match-to-sample task

Despite their small brains, insects show advanced capabilities in learning and task solving. Flies, honeybees and ants are becoming a reference point in neuroscience and a main source of inspiration for autonomous robot design issues and control algorithms. In particular, honeybees demonstrate to be able to autonomously abstract complex associations and apply them in tasks involving different sensory modalities within the insect brain. Mushroom Bodies (MBs) are worthy of primary attention for understanding memory and learning functions in insects. In fact, even if their main role regards olfactory conditioning, they are involved in many behavioral achievements and learning capabilities, as has been shown in honeybees and flies. Owing to the many neurogenetic tools, the fruit fly Drosophila became a source of information for the neuroarchitecture and biochemistry of the MBs, although the MBs of flies are by far simpler in organization than their honeybee orthologs. Electrophysiological studies, in turn, became available on the MBs of locusts and honeybees. In this paper a novel bio-inspired neural architecture is presented, which represents a generalized insect MB with the basic features taken from fruit fly neuroanatomy. By mimicking a number of different MB functions and architecture, we can replace and improve formerly used artificial neural networks. The model is a multi-layer spiking neural network where key elements of the insect brain, the antennal lobes, the lateral horn region, the MBs, and their mutual interactions are modeled. In particular, the model is based on the role of parts of the MBs named MB-lobes, where interesting processing mechanisms arise on the basis of spatio-temporal pattern formation. The introduced network is able to model learning mechanisms like olfactory conditioning seen in honeybees and flies and was found able also to perform more complex and abstract associations, like the delayed matching-to-sample tasks known only from honeybees. A biological basis of the proposed model is presented together with a detailed description of the architecture. Simulation results and remarks on the biological counterpart are also reported to demonstrate the possible applications of the designed computational model. Such neural architecture, able to autonomously learn complex associations is envisaged to be a suitable basis for an immediate implementation within an robot control architecture.

Motion control of walking assistant robot based on comfort

– A walking assistant robot can help elderly people walk around independently, which could improve the life quality of the elderly and benefit our aging society. Ensuring the elderly person's walking comfort with such a robot is very important. At present, the majority of walking assistant robot research does not focus on this field. The purpose of this paper is to examine the requirements of comfortable walking and outline the design of a motion control algorithm for a walking assistant robot, Walkmate III, based on comfort., – During walking, the walking assistant robot should be able to capture the intent of user, guide the user and move at the same pace as the user. Usually, force or haptic interface is used to detect the user's walking intention. The motion control system then transforms the forces applied by the user into the robot's motion. By surveying the elderly people at a nursing home, the authors find that this transformation is important to the walking comfortableness and should be carefully designed. In this paper, the model of walking assisting process with such kind of walking assistant robot is derived at first. Based on this model, a new motion control algorithm is then designed., – The elderly hoped that, in all topographic conditions, only small forces were needed to drive the walker during walking. Also, good maneuverability was also very important for a walker, to offer the user comfort, which meant the walking assistant robot should be able to respond to the input forces quickly and precisely. Currently widely‐used motion control algorithms cannot satisfy all those requirements. In this paper, a new motion control algorithm is proposed, which can get a fast and precise response to the input forces and the input forces needed to drive the robot are kept at a preferred small level, so that the user will not feel tired during walking. Furthermore, by modifying, force feedback can be realized to improve the comfortableness of walking., – The availability of walking assistant robot with improved walking comfortableness might encourage a wider adoption of robotics in our daily life. It could also benefit our aging society by improving the life quality of the elderly and reducing the pressure deriving from nursing labor shortages., – This paper is of value to engineers and researchers developing walking assistant robots for the elderly people.