The article proposes a method for classifying human postures using an improved probabilistic neural network (PNN) with different distance measures and different probabilistic distribution functions (PDF). We found that the PNN with angular distance provides better accuracy, precision, and recall for the postures classification tasks than the PNN with conventional Euclidean distance. The k Nearest Neighbors (kNN) method gives slightly better prediction results than PNN, but our PNN is much faster. Such good computational performance is beneficial for posture recognition tasks that require real-time functions. An example is the needs of cobots or service robots. The article also proposes a method for selecting the distribution smoothing parameter ($\sigma$) using the sub-optimization process based on the improved Gray Wolf optimization (I-GWO) algorithm. It was found that the impact of PDF differences on the quality of the results can be reduced by choosing the best possible $\sigma$. In order to evaluate the developed method, selected human activities were recorded. The datasets were created using two different RGB-D systems located in two different laboratories.

This research formulates a path-following control problem subjected to wheel slippage and skid and solves it using a logic-based control scheme for a wheeled mobile robot (WMR). The novelty of the proposed scheme lies in its methodology that considers both longitudinal and lateral slip components. Based on the derived slip model, the controller for longitudinal motion slip has been synthesized. Various control parameters have been studied to investigate their effects on the performance of the controller resulting in selection of their optimum values. The designed controller for lateral slip or skid is based on the proposed side friction model and skid check condition. Considering a car-like WMR, simulation results demonstrate the effectiveness of the proposed control scheme. The robot successfully followed the desired circular trajectory in the presence of wheel slippage and skid. This research finds its potential in various applications involving WMR navigation and control.

An operational space dynamic model of a redundantly actuated wheeled mobile robot taking into account the wheel-ground interaction is derived based on vehicle dynamics. The conditions for the robot to avoid slip are derived for both torque and motion. Two torque distribution schemes based on different criteria are proposed. The non-slip condition for motion is utilized to plan slip avoidance paths. The null space joint torques are utilized to fulfil the non-slip condition for torque and avoid actuator torque limits violation. Simulations results are presented to demonstrate the effects of wheel-ground interaction and performance of the proposed torque distribution schemes

This paper presents a method for optimally generating stable bipedal walking gaits, based on a truncated Fourier series formulation with coefficients tuned by genetic algorithm. It also provides a way to adjust the stride-frequency, step-length or walking pattern in real-time. The proposed approach to gait synthesis is not limited by the robot kinematic structure and can be used to satisfy various motion assumptions. It is also easy to generate optimal gaits on terrains of different slopes or on stairs under different motion requirements. Dynamic simulation results show the validity and robustness of the approach. The gaits generated resulted in human-like motions optimized for stability, even walking speed and lower leg-strike velocity of the swing foot

An essential consideration in the development of any motion planning method for a multilegged vehicle is to maintain stability during walking. We propose a leg-end supporting moment (LSM) method, to measure the stability margin of walking robots. The LSM is defined as the product of the force acting on a supporting leg and the distance between the supporting leg and the opposite edge of the support polygon. These forces and distances can be obtained online from the internal robot sensors. The stability margin evaluated by the LSM method, expressed as LSM/sub m/, is defined as the quotient of the leg-end supporting moment over the weight of the robot. If the LSM/sub m/ is greater than zero, the robot is in stable state. Otherwise the robot will be in marginally stable state or will lose its stability. The simulation results show that the LSM method is suitable for the stability analysis in cases with and without external forces acting on the body. The method is not only effective for flat terrain, but also for uneven terrain. These characteristics enable the LSM method to measure the stability margin of a walking robot in the case of drilling, dragging and manipulating, also in the case when the robot is used in forestry.

A method of free gait generation for quadrupeds is presented by introducing the primary/secondary gait. The primary gait is a fixed sequence of leg transfers with modified leg-ends kinematic limits according to the obstacle presence, while the secondary gait is a flexible gait which is generated to adjust the leg-end position. Using the proposed method, a machine tends to move with primary gait during the motion. When the primary gait cannot move the vehicle, the secondary gait is adopted to serve as a complement of the primary gait. Four constraints, namely, stability constraint, kinematic constraint, sequential constraint and neighboring constraint are considered when gaits are generated. The influence of the obstacle is taken into account to modify the gait parameters. With the proposed method based on primary/secondary gait, it is expected to improve the efficiency of free gait generation while maintaining the mobility of the vehicle. Simulation results are given to demonstrate the efficiency of the proposed algorithm.