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Figure 2. Mechanical construction of the PERSIA humanoid robots-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

Figure 2 shows one of the constructions used for our robots. Knee joints are considered to bend in both directions which help faster response of the robot in backward walking. Efforts have been made to hold the proportions as much as possible human like. The PERSIA robot is 38cm tall and weighs about 1.6 kg. It has 18 degrees of freedom: 5 in each leg, 3 in each hand and 2 in head. To facilitate exchange of the players, all robots use mechanically the same structure.

Figure 4. (a)Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

Figure 4 shows the block diagram of the software which runs in the robot’s main processor. The program consists of 4 main blocks: • Hardware Interface: Contains all low level routines to access hardware of the robot including sensors and actuators. • Vision: Contains image processing algorithms such as recognition of landmarks and other object. Self localization is done using particle filtering. Particles are scored by comparing a simulated image from each particle with the current frame captured by camera. Using “Sampling-Importance Resampling” method, a new distribution of the particles is created after each step. Particles are also updated using a motion model. Final distribution of the particles converges to the real pose of the robot. • Planning: Planning system of the robot is based on a multi layer, and multi thread structure. The layers are named Strategy, Role, Behavior and Motion. Each layer contains a Scenario which runs in parallel with the scenarios in the other layers. A scenario in a higher level can terminate and change the scenario running in the lower level; however it is usually done in synchronization with the lower level scenario to avoid conflicts and instabilities. (Such as stopping the walking motion while one of the feet is still in the air). • Network: Mainly responsible for the wireless communication of the robot with the other robots or the referee box. This is done via WLAN. • Motion Control: manages all the actuators of the robot, and controls locomotion or any other action of the robot according to the requests from Cognition. • Sensor Control: manages other sensors, and interacts with the Sub-Controller.

Figure 8. Artificial Intelligence Algorithm-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

This module receives information from Artificial Intelligent unit. Total functions about Robot Behavior such as stability motors actions, robot path planning, turn camera, walking, shooting, dribbling; motion and etc are controlled in this section.

Figure 1. PERSIA Humanoid Robot in Robocup IranOpen2010 Competition-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

In this paper, we will at first describe the general hardware design of the PERSIA Humanoid Robocup Team, (section 2) and after that focus on our scientific approaches in sensor fusion and learning (section 3). Finally, section 4 concludes this paper. This document describes the current state of the project as well as the intended development for the RoboCup 2010 competition.

Figure 3. (a) Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

The PERSIA Humanoid robot designed for has multipurpose capability. This robot equipped with main board for motion control, vision sensor, other balancing sensors, servo motors and etc. Figure 3 shows picture of the robot and overview of the Persia humanoid robot control system.

opencc-by-4.0Sep 2019View details →

PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2019 Week 27

These data were collected from robot controller during solar tanks welding operation.

opencc-by-4.0Sep 2019View details →

PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2019 Week 28

These data were collected from robot controller during solar tanks welding operation.

opencc-by-4.0Oct 2023View details →

Eye Tracking in Robot Control Tasks

This table is part of a systematic review. It contains current work of researchers around the world, who work in the field of eye tracking control for robotic arms. These controls are used for assistive robotics, aiding physically impaired people in everyday life and in shared workspaces.This data set can also be found on git: https://github.com/AnkeLinus/EyeTrackingInRobotControlTasks.git If you use this table in other publications please cite as stated in the git repository. Also keep an eye open for updates.

dryad40/100

Data from: 3D printed digital pneumatic logic for the control of soft robotic actuators

Open the record for dataset details and reuse information.

publicJan 2024View details →

opencc-by-4.0Feb 2016View details →

P colonies and P swarms for controlling robot swarms. Experimental setups and demonstration videos

These eleven videos present different experimental scenarios used to test the flexibility and functioning of the LULU P colony/P swarm simulator and of the associated application Lulu_Kilobot for controlling robot swarms. Both applications will be published on Github under an open-source license. The input P colony (input) file, swarm configuration (config) file and V-REP scene (.ttt) are available for each video in the associated .zip archive. The LULU simulator was included as a Python module in Lulu_Kilobot in order to test robot controllers based on P colonies, XP colonies, and P swarms for swarms of up to 10 Kilobot robots. In the following sections, we present a small description for each of the eleven attached videos. ----------------------------------------------------------------------------------------------- 1_clone_10_circle This video demonstrates the use of the robot cloning function of the vrep_bridge script in order to create 9 distinct copies of the source robot and distribute them on a circle around the source robot. The copies are so positioned by a distribution function that can be adapted to other forms. This cloning function allows one to generate large swarms of robots with ease. ----------------------------------------------------------------------------------------------- 2_one_pcolony_for_three_kilobots In this video, we simulate a simple P minus colony using Lulu_Kilobot, on three different robots. At each subtraction, the robots move one step forward. At the beginning of the clip one can see the Robot - P colony association table, where each robot has a distinct copy of the original P colony. ----------------------------------------------------------------------------------------------- 3_pswarm_5_robots_3_colonies This video demonstrates the flexibility offered by the config file of Lulu_Kilobot. From the config file we explicitly specify that the first two robots should use the go straight P colony. For the other colonies, we specify the number of robots that should be assigned, go left = 1 and go right = 2. From the Robot - P colony association table, one can see that the first robot that is assigned a P colony uses the original P colony while the others use an independent copy of the P colony. ----------------------------------------------------------------------------------------------- 4_pswarm_2_robots_avoid_collision In this experiment, we test the msg_distance agent from the input module, by continuously checking the distance from another robot. If the distance is short, then we stop the movement and otherwise continue to subtract f objects from the environment and move forward. Each of the two robots has a different P colony that was designed to check the distance from the other robot (robot_0 checks the distance from robot_1). ----------------------------------------------------------------------------------------------- 5_pswarm_2_robots_xp_colonies_15_steps In this video we employ the exteroceptive communication rules (denoted by <=>) in order to synchronize the movement of two robots. The first robot moves forward 15 steps and after it stops, it signals the second robot to start moving. At this signal, the second robot starts to turn left 15 steps. This shows the utility of exteroceptive rules that allow XP colonies (P colonies with exteroceptive rules) to communicate using the global P swarm environment. ----------------------------------------------------------------------------------------------- 6_1_pswarm_10_robots_disperse_steps_infinite_loop In this video we run a more complex algorithm that involves the use of the following modules: msg_distance, led_rgb, and motion. This video demonstrates dispersion, which is a typical self-deploying scenario in swarm robotics. The robots should position themselves away from one another, so that each robot is at least at a minimum distance from each of its neighbours. All decisions are taken by the command module, on the basis of the received input data from msg_distance. A new direction of motion (and color) is randomly chosen if there are other robots closer than a pre-set threshold distance. ----------------------------------------------------------------------------------------------- 6_2_pswarm_10_robots_optimized_disperse_infinite_loop This video is an optimized version of 6.1 (10 robots disperse). The optimization consists in only exchanging data with V-REP when a new input request is detected in the input agents or likewise a new command object is detected in the output agents. This results in a step-less movement of the robots and reduces the time needed for a new decision to be applied resulting in a faster overall simulation time. ----------------------------------------------------------------------------------------------- 7_pswarm_10_robots_optimized_disperse_infinite_loop_with_intruder In this video we use the previously presented optimized dispersion algorithm (6.2) and introduce an intruder robot into the scene in order to evaluate the influence that this intruder has over the behaviour of the swarm. One can see that robots that have stopped their movement, restart dispersing when the intruder robot is brought close enough. This can cause a chain reaction and ultimately cause the swarm to reposition. ----------------------------------------------------------------------------------------------- 8_1_pswarm_10_robots_secure_disperse_fast In this video we test the proposed security protocol (based on entity authentification and P colony based id check) using the non-optimized dispersion algorithm. In this film we see that the robots ignore the intruder robot even though it is placed in the middle of the swarm. On the other hand, if we bring a swarm member robot close to another swarm member robot, these two will start to disperse normally. ----------------------------------------------------------------------------------------------- 8_2_pswarm_10_robots_secure_disperse_infinite_loop_2 In this video we present the optimized (see 6.2 for details) version of the secured dispersion algorithm. As was the case of the secured un-optimized version (8.1), in this secured version we test the influence of the intruder on the behaviour of the swarm by moving the intruder close to the center of the swarm and also moving the intruder close to the swarm after the dispersion is finished. We also note that if two member robots approach, the algorithm continues to work normally. ----------------------------------------------------------------------------------------------- 9_pswarm_10_robots_secure_d_min_disperse_infinite_loop In this clip we show the effects of transparent input data processing. The command agent always requests the smallest distance available from the neighbour list, by using the d_min command. When the intruder robot is the nearest robot (the smallest value in the list) d_min will always return the intruder robot which cannot be processed because it is unknown to the swarm members. For this reason, a member robot that is in this situation will be blocked by the intruder robot.

opencc-by-4.0Apr 2020View details →

Motor-Imagery EEG Dataset During Robot-Arm Control

Experiment Description: This experiment involved 12 healthy subjects with no prior experience on neurofeedback or BCI, and without any known neurological disorders. All participants are right-handed, except one ambidextrous (participant #5). All participants have provided their signed informed consent for participating in the study in accordance with the 1964 Declaration of Helsinki. The experiment had been conducted in a laboratory environment under controlled conditions. The subjects went through three sessions lasting maximum two hours, during three consecutive days and each day at approximately at the same hour. During each session, participants underwent three different conditions. The first condition was always the ”resting-state”: the user was asked to keep the eyes open for two minutes staring at a screen with a green cross and a red arrow pointing up, and then closed for the other two minutes. After this, two more conditions followed related to a Motor Imagery (MI) task performed in a randomized order between left|right-hand movement. The two MI conditions consisted of two phases each: a training phase and a test phase. The general experimental routine for both of them was the same: each trial lasted 6 seconds (2 seconds baseline and 4 seconds MI), forewarned by the appearance of a green cross on the screen and a concomitant beep-sound a second before the onset of the task. Then, an arrow was appearing pointing left or right, and the subject had to imagine the movement of the corresponding arm reaching an object in front of the Baxter Robot (Rethink Robotics, Bochum, Germany). For both phases, 20 trials from left and 20 trials for right MI were generated in a randomized order, for a total of 40 trials. Finally, there was an inter-trial interval that extended randomly between 1.5 and 3.5 seconds. Overall, this study resulted into 180 EEG datasets.   Data Description: <table> <tbody> <tr> <td>Data Format</td> <td>General Data Format (GDF)</td> </tr> <tr> <td>Sampling Rate</td> <td>250 Hz</td> </tr> <tr> <td>Channels</td> <td>32 EEG + 3 ACC.</td> </tr> <tr> <td>EEG system</td> <td>LiveAmp 32 with active electrodes actiCAP (Brain Products GmbH, Gilching, Germany)</td> </tr> </tbody> </table>   Events: <table> <caption> </caption> <tbody> <tr> <td>Code</td> <td>Description</td> </tr> <tr> <td>32775</td> <td>Baseline Start</td> </tr> <tr> <td>32776</td> <td>Baseline Stop</td> </tr> <tr> <td>768</td> <td>Start of Trial, Trigger at t=0s</td> </tr> <tr> <td>786</td> <td>Cross on screen (BCI experiment)</td> </tr> <tr> <td>33282</td> <td>Beep</td> </tr> <tr> <td>769</td> <td>class1, Left hand    - cue onset</td> </tr> <tr> <td>770</td> <td>class2, Right hand    - cue onset</td> </tr> <tr> <td>781</td> <td>Feedback (continuous) - onset</td> </tr> <tr> <td>800</td> <td>End Of Trial</td> </tr> <tr> <td>1010</td> <td>End Of Session</td> </tr> <tr> <td>33281</td> <td>Train</td> </tr> <tr> <td>32770</td> <td>Experiment Stop</td> </tr> </tbody> </table>   Directory Tree: ROOT | chanlocs.locs | | +--- USER # |            +---SESSION # |            |            +---CONDITION # |            |            |            \---RESTING_STATE |            |            |            +---1st_PERSON |            |            |             |           TRAINING |            |            |             |           ONLINE |            |            |            +---3rd_PERSON |            |            |             |           TRAINING |            |            |             |           ONLINE

opencc-by-4.0Dec 2017View details →

Bilateral Human-Robot Control for Semi-Autonomous UAV Navigation

This video demonstrates the work towards a novel control architecture for UAV navigation. In general, UAVs are not easy to operate and skilled pilots are required for a good performance in manual flight. However, currently it is impossible to capture every possible situation an UAV could encounter in the autonomous control. To avoid overly complicated control, a semi-autonomous control approach can be used, so the drone is partly autonomously and partly manually piloted. The novelty of the approach presented here is in the way this semi-autonomy is defined. As the UAV regularly operates autonomously, it is not desirable to switch to manual control in dangerous procedures. Instead, a more supervisory method of control can be applied in which the UAV is always controlled by the onboard computer, but the boundaries of control are controlled by the operator. Whenever a situation requires bigger risks, the operator is informed requested by the drone for help, which he\she can offer by softening certain boundaries of the UAV. This video demonstrates the concept.

opencc-by-4.0Dec 2017View details →

Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot

This video is a supporting material of the paper "Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot". The paper presents a plant root behavior-based approach to defining the control architecture of a plant-root-inspired robot, which is composed of three root-agents for nutrient uptake and one shoot-agent for nutrient redistribution. By taking inspiration and extracting key principles from the uptake of nutrient, movements and communication strategies adopted by plant roots, we developed an uptake–kinetics feedback control for the robotic roots. Exploiting the proposed control, each root is able to regulate the growth direction, towards the nutrients that are most needed, and to adjust nutrient uptake, by decreasing the absorption rate of the most plentiful one. Results from computer simulations and implementation of the proposed control on the robotic platform, Plantoid, demonstrate an emergent swarming behavior aimed at optimizing the internal equilibrium among nutrients through the self-organization of the roots. Plant wellness is improved by dynamically adjusting nutrients priorities only according to local information without the need of a centralized unit delegated for wellness monitoring and task allocation among the agents. Thus, the root-agents can ideally and autonomously grow at the best speed, exploiting nutrient distribution and improving performance, in terms of exploration capabilities and exploitation of resources, with respect to the tropism-inspired control previously proposed by the same authors. The supplementary video (Supplementary Video S1) shows how each agent independently moves according to their internal state and local perception, and the immediate response of the uptake–kinetics mechanism that, as soon as the missing nutrient is inserted in the environment, leads to a decreasing of the imbalance of nutrients in the whole plant.

Figure 6. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

The image sequences of sideward walking and turning are shown in Figure 6 respectively.

opencc-by-4.0Oct 2018View details →

Videos of a robot controlled by Belousov-Zhabotinsky liquid marbles. Supplementary materials to the paper "Belousov-Zhabotinsky liquid marbles in robot control".

Videos and snapshots of experiments with robot controlled by liquid marbles made of Belousov-Zhabotinsky solution.  Supplementary materials for the paper    M.-A. Tsomapanas, C. Fullarton, A. Adamatzky. Belousov-Zhabotinsky liquid marbles in robot control. (2018).  Abstract  We show how to control the movement of a wheeled robot using on-board liquid marbles made of Belousov-Zhabotinsky solution coated by polyethylene powder. Two platinum-iridium electrodes were inserted in a marble and the electrical potential recorded was used to control the robot's motor. We stimulated the marble with a laser beam. It responded to the stimulation by pronounced change of the electrical potential output. The electrical output was detected by robot. The robot was changing its trajectory in response to the stimulation. This research was supported by the EPSRC with grant EP/P016677/1 ``Computing with Liquid Marbles''.

dryad36/100

Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits

Most soft robots are pneumatically actuated and fabricated by molding and assembling processes that typically require many manual operations and limit complexity. Furthermore, complex control components (for example, electronic pumps and microcontrollers) must be added to achieve even simple functions. Desktop fused filament fabrication (FFF) three-dimensional printing provides an accessible alternative with less manual work and the capability of generating more complex structures. However, because of material and process limitations, FFF-printed soft robots often have a high effective stiffness and contain a large number of leaks, limiting their applications. We present an approach for the design and fabrication of soft, airtight pneumatic robotic devices using FFF to simultaneously print actuators with embedded fluidic control components. We demonstrated this approach by printing actuators an order of magnitude softer than those previously fabricated using FFF and capable of bending to form a complete circle. Similarly, we printed pneumatic valves that control a high-pressure airflow with low control pressure. Combining the actuators and valves, we demonstrated a monolithically printed electronics-free autonomous gripper. When connected to a constant supply of air pressure, the gripper autonomously detected and gripped an object and released the object when it detected a force due to the weight of the object acting perpendicular to the gripper. The entire fabrication process of the gripper required no posttreatment, postassembly, or repair of manufacturing defects, making this approach highly repeatable and accessible. Our proposed approach represents a step toward complex, customized robotic systems and components created at distributed fabricating facilities.

opencc-zeroJul 2023View details →

restrictedIPD-UNDECIDEDFeb 2026View details →

Cardiovascular Rehabilitation Early After Stroke Using Feedback-controlled Robotics-assisted Treadmill Exercise

ClinicalTrials.gov study NCT01679600. IPD Sharing: Not stated. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →

Laparoscopy vs. Robotic Surgery for Endometriosis (LAROSE): a Prospective Randomized Controlled Trial

ClinicalTrials.gov study NCT01556204. IPD Sharing: NO. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

Effectiveness of Intelligent Rehabilitation Robot Training System Combined With Repetitive Facilitative Exercise on Upper Limb Motor Function After Stroke: a Randomized Control Trial.

ClinicalTrials.gov study NCT06435624. IPD Sharing: Not stated. Countries: 1. Publications: 1.