Robot Interaction has always been a challenge in collaborative robotics. In taskscomprising Inter-Robot Interaction, robot detection is very often needed. Weexplore humanoid robots detection because, humanoid robots can be useful in manyscenarios, and everything from helping elderly people live in their own homes toresponding to disasters. Cameras are chosen because they are reach and cheapsensors, and there are lots of mature two-dimensional (2D) and 3D computervision libraries which facilitate Image analysis. To tackle humanoid robotdetection effectively, we collected a data set of various humanoid robots withdifferent sizes in different environments. Afterward, we tested the well-knowncascade classifier in combination with several image descriptors like Histogramsof Oriented Gradients (HOG), Local Binary Patterns (LBP), etc. on this data set.Among the feature sets, Haar-like has the highest accuracy, LBP the highestrecall, and HOG the highest precision. Considering Inter-Robot Interaction, itis evident that false positives are less troublesome than false negatives, thusLBP is more useful than the others.

This paper presents our preliminary research into the autonomous control of analpine skiing robot. Based on our previous experience with active balancing ondifficult terrain and developing an ice-skating robot, we have implemented asimple control system that allows the humanoid robot Jennifer to steer around asimple alpine skiing course, brake, and actively control the pitch and roll ofthe skis in order to maintain stability on hills with variable inclination.

The robot steers and brakes by using the edges of the skis to dig into the snow,by inclining both skis to one side the robot can turn in an arc. By rolling theskis outward and pointing the toes together the robot creates a snowplough shapethat rapidly reduces its forward velocity.

To keep the skis in constant contact with the hill we use two independentproportional-integral-derivative (PID) controllers to continually adjust therobot’s inclination in the frontal and sagittal planes.

Our experiments show that these techniques are sufficient to allow a smallhumanoid robot to alpine ski autonomously down hills of different inclinationwith variable snow conditions.

Obstacle avoidance is an important issue in robotics. In this paper, the particleswarm optimization (PSO) algorithm, which is inspired by the collectivebehaviors of birds, has been designed for solving the obstacle avoidanceproblem. Some animals that travel to the different places at a specific time ofthe year are called migrants. The migrants also represent the particles of PSOfor defining the walking paths in this work. Migrants consider not only thecollective behaviors, but also geomagnetic fields during their migration innature. Therefore, in order to improve the performance and the convergence speedof the PSO algorithm, concepts from the migrant navigation method have beenadopted for use in the proposed hybrid particle swarm optimization (H-PSO)algorithm. Moreover, the potential field navigation method and the designedfuzzy logic controller have been combined in H-PSO, which provided a goodperformance in the simulation and the experimental results. Finally, theFederation of International Robot-soccer Association (FIRA) HuroCup Obstacle RunEvent has been chosen for validating the feasibility and the practicability ofthe proposed method in real time. The designed adult-sized humanoid robot alsoperformed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing theproposed H-PSO.

This paper presents a parameterized gait generator based on linear invertedpendulum model (LIPM) theory, which allows users to generate a natural gaitpattern with desired step sizes. Five types of zero moment point (ZMP)components are proposed for formulating a natural ZMP reference, where ZMP movescontinuously during single support phases instead of staying at a fixed point inthe sagittal and lateral plane. The corresponding center of mass (CoM)trajectories for these components are derived by LIPM theory. To generate aparameterized gait pattern with user-defined parameters, a gait planningalgorithm is proposed, which determines related coefficients and boundaryconditions of the CoM trajectory for each step. The proposed parameterized gaitgenerator also provides a concept for users to generate gait patterns withself-defined ZMP references by using different components. Finally, thefeasibility of the proposed method is validated by the experimental results witha teen-sized humanoid robot, David, which won first place in the sprint event atthe 20th Federation of International Robot-soccer Association (FIRA) RoboWorldCup.

This study presents the algorithm for a humanoid robot to accomplish an obstaclerun in the FIRA HuroCup competition. It includes the integration of imageprocessing and robot motion. DARwIn-OP (Dynamic Anthropomorphic Robot withIntelligence–Open Platform) was used as the humanoid robot, and it isequipped with a webcam as a vision system to obtain an image of what is in frontof the robot. Image processing skills such as erosion, dilation, andeight-connected component labeling are applied to reduce image noise. Moreover,we use navigation grids with filters to avoid the obstacles. Fuzzy logic rulesare used to implement the robot’s motion, allowing a humanoid robot toaccess any routes using obstacle avoidance to perform the tasks in theobstacle-run event.

Usually, humanoid walking gaits are only roughly distinguished between stable andunstable. The evaluation of a stable humanoid walking gait is difficult toquantify in scales. And, it is extremely hard to adjust humanoid robots insuitable a walking gait for different movement objectives such as fast walking,uneven floor walking, and so on. This paper proposes a stability marginconstructed by center of pressure (COP) to evaluate the gait stability ofhumanoid walking. The stability margin is modeled by the COP regions that ahumanoid robot needs for stable standing. We derive the mathematical model forCOP position by dividing the walking gait into single and double support phasesin order to measure the stability of the COP regions. An actual measuring systemfor the stable COP regions is designed and implemented. The measured COPtrajectory of a walking gait is eventually evaluated with respect to the stableCOP regions for the stability margins. The evaluation focuses on weak stabilityareas to be improved for robust walking gaits. To demonstrate the robustness ofthe improved walking gait, we replicate the experiment on three differentterrains. The experiments demonstrate that the walking gaits developed based onstable COP region can be applied for different movement objectives.

This paper describes the motivation for the development of the HuroCupcompetition and follows the rule development from its inaugural competition from2002 to 2015. The history of HuroCup is broken down into its growing phase(2002–2006), a time of explosive growth (2007–2011), and currenttimes. This paper describes the main research focus of HuroCup, the multi-eventhumanoid robot competition: (a) active balancing, (b) complex motion planning,and (c) human–robot interaction and shows how the various HuroCup eventsrelate to those research topics. This paper concludes with some medium- andlong-term goals of the rule development for HuroCup.