The adaptability of hexapods for various locomotion tasks, especially in rescue and exploration missions, drives their application. Unlike controlled environments, these robots need to navigate ever-changing terrains, where ground irregularities impact foothold positions and origin shifts in contact forces. This dynamic interaction leads to varying hexapod postures, affecting overall system stability. This study introduces a posture control approach that adjusts the hexapod’s main body orientation and height based on terrain topology. The strategy estimates ground slope using limb positions, thereby calculating novel limb trajectories to modify the hexapod’s angular position. Adjusting the hexapod’s height, based on the calculated slope, further enhances main body stability. The proposed methodology is implemented and evaluated on the ATHENA hexapod (All-Terrain Hexapod for Environment Adaptability). Control feasibility is assessed through dynamic analysis of the hexapod’s multibody model on irregular surfaces, using computational simulations in Gazebo software. Environmental complexity’s impact on hexapod stability is tested on both a ramp and uneven terrain. Independent analyses for each scenario evaluate the controller’s effect on roll and pitch angular velocities, as well as height variations. Results demonstrate the strategy’s suitability for both environments, significantly enhancing posture stability.

In this paper, an analytical formula for the determination of the center of mass position in humanoid platforms is proposed and tested in a real humanoid robot. The formula uses the force-torque values obtained by the two force-torque sensors applied on the feet of the robot and the measured currents required from the motors to maintain balance as inputs. The proposed formula outputs the real center of mass position that minimizes the errors between real humanoid robots and virtual models. Data related to the Zero Moment Point positions and to the joint movements are compared with the target values, showing how the application of the proposed formula enables achieving better repeatability and predictability of the static and dynamic robot behaviour.

This paper tackles the problem of motion planning and control of a car-like robot in an obstacle-ridden workspace. A kinematic model of the vehicle, governed by a homogeneous system of first-order differential equations, is used. A solution to the multi-tasking problem of target convergence, obstacle avoidance, and posture control is then proposed. The approach of solving the problem is two-fold. Firstly, a novel velocity algorithm is proposed to drive the car-like robot from its initial position to the target position. Secondly, a single layer artificial neural network is trained to avoid disc-shaped obstacles and provide corresponding weights, which are then used to develop a function for the steering angles. Thus, our method does not need a priori knowledge of the environment except for the goal position. With the help of the Direct Method of Lyapunov, it is shown that the proposed forms of the velocity and steering angle ensure point stability. For posture stability, we model the two parallel boundaries of a row-structured parking bay as continua of disk-shaped obstacles. Thus, our method is extendable to ensuring posture stability, which gives the desired final orientation. Computer simulations of the generated path are presented to illustrate the effectiveness of the method.