International Journal of Humanoid Robotics

Many efforts to develop walking and running robots utilize a boom or other device to catch the robot when it falls, sense the position of the robot, and constrain the robot to operate in a two dimensional plane. However, publications usually focus on the robot, and the restraint system remains undocumented. Each group must start from scratch, or rely on word of mouth to build on the experience of others. This paper focuses solely on the artificial restraint system for legged robots, with discussion of various design options and documentation of existing systems.

If we are to one day rely on robots as assistive devices they should be capable of mitigating the impact of random disturbances and avoid falling. Humans are surprisingly apt at remaining on their feet when pushed; they rely on reflexes such as bending the ankles and/or the hips, or by taking a step if the magnitude of the disturbance is relatively large. This paper presents a fall avoidance scheme that is capable of applying both ankle and hip strategies on a humanoid robot. While both strategies serve the same purpose, the hip strategy can absorb larger disturbances but has a higher energy overhead and should be avoided when it is not necessary. Our system is capable of detecting at the onset of a disturbance if an ankle or hip strategy is more appropriate. The decision is taken based on a 'decision surface' that is delimited by threshold values of the robot's state variables. The control is based on the Virtual Model Control (VMC) approach. The system is tested on a simulated robot developed under Gazebo as well as on a real small-scale humanoid robot. Results show successful fall avoidance with an ability to choose the optimum fall avoidance strategy.

This paper proposes an energy control method for dynamic obstacle crossing by a planar biped. This approach was tested in a simulation where it was found to enable the biped robot to cross obstacles of different heights, due to inertial forces, by leaning with the front foot on the obstacles. The propulsion energy of the system is produced by the rear leg, which is endowed with four actuated degrees-of-freedom (hip, knee, ankle, toes), and is controlled by force control with four degrees-of-freedom in the non-singular case, and three degrees-of-freedom in the singular case. This paper identifies ten geometric, energetic and servo-control parameters necessary for dynamic obstacle crossing. The methodology presented allowed the dynamic crossing of an obstacle up to 20 cm high, at which point the joint torque limit for the propelling ankle was reached.

Enhancing energy efficiency of bipedal walking is an important research problem that has been approached by design of recently developed compliant bipedal robots such as CoMan. While compliance leads to energy efficiency, it also complicates the walking control system due to further under-actuated degrees of freedom (DoF) associated with the compliant actuators. This problem becomes more challenging as the constrained motion of the robot in double support is considered. In this paper this problem is approached from a multi-variable geometric control aspect to systematically account for the compliant actuators dynamics and constrained motion of the robot in double support phase using a detailed electro-mechanical model of CoMan. It is shown that the formulation of constraint subspace is non-trivial in the case of non-rigid robots. A step-wise numerical algorithm is provided and the effectiveness of the proposed method is illustrated via simulation, using a ten DoF model of CoMan.

In this paper, an evolutionary optimized footstep planner for the navigation of humanoid robots is proposed. A footstep planner based on a univector field navigation method is proposed to generate a command state (CS) as an input to a modifiable walking pattern generator (MWPG) at each footstep. The MWPG generates associated trajectories of every leg joint to follow the given CS. In order to satisfy various objectives in the navigation, the univector fields are optimized by evolutionary programming. The three objectives, shortest elapsed time to get to a destination, safety without obstacle collision, and less energy consumption, are considered with mechanical constraints of a real humanoid robot, that is, the maximum step length and allowable yawing range of the feet. The effectiveness of the proposed algorithm is demonstrated through both computer simulation and experiment for a small-sized humanoid robot, HanSaRam-IX.

Learning by imitation is a natural and intuitive way to teach social robots new behaviors. While these learning systems can use different sensory inputs, vision is often their main or even their only source of input data. However, while many vision-based robot learning by imitation (RLbI) architectures have been proposed in the last decade, they may be difficult to compare due to the absence of a common, structured description. The first contribution of this survey is the definition of a set of standard components that can be used to describe any RLbI architecture. Once these components have been defined, the second contribution of the survey is an analysis of how different vision-based architectures implement and connect them. This bottom–up, structural analysis of architectures allows to compare different solutions, highlighting their main advantages and drawbacks, from a more flexible perspective than the comparison of monolithic systems.

Traditional motion recognition methods developed on the basis of steady-state electromyography (EMG) signals cannot well deal with the transient EMG signals. Thus, a large quantity of incorrect classification outputs would be introduced during the dynamic stages of the motions. In order to achieve a high-accuracy recognition system especially for dynamic motion control, this paper combines the transient EMG and the steady-state EMG signals together for training the recognition system. A threshold decision method is utilized in the time-domain feature space to collect the combined EMG signals. Besides, a statistical classifier named support vector machine is adopted in the online recognition procedure to distinguish the motion types. Experiments are conducted to quantify the classification accuracy and response delay of the recognition systems, and compare these with traditional steady-state EMG-based methods. The results indicate that the recognition accuracy can be greatly improved and the detection delay of the motions can be significantly compressed in the transient stages of the hand motions. The method shows a promising application in the dynamic motion control of dexterous prosthetic hands in the future.