Real-time gait trajectory planning is challenging for legged robots walking on unknown terrain. In this paper, to realize a more efficient and faster motion control of a quadrupedal robot, we propose an optimized gait planning generator (GPG) based on the decision tree (DT) and random forest (RF) model of the robot leg workspace. First, the framework of this embedded GPG and some of the modules associated with it are illustrated. Aiming at the leg workspace model described by DT and RF used in GPG, this paper introduces in detail how to collect the original data needed for training the model and puts forward an Interpolation Labeling with Dilation and Erosion (ILDE) data processing algorithm. After the DT and RF models are trained, we preliminarily evaluate their performance. We then present how these models can be used to predict the location relation between a spatial point and the leg workspace based on its distributional features. The DT model takes only 0.00011 s to process a sample, while the RF model can give the prediction probability. As a complement, the PID inverse kinematic model used in GPG is also mentioned. Finally, the optimized GPG is tested during a real-time single-leg trajectory planning experiment and an unknown terrain recognition simulation of a virtual quadrupedal robot. According to the test results, the GPG shows a remarkable rapidity for processing large-scale data in the gait trajectory planning tasks, and the results can prove it has an application value for quadruped robot control.

This paper studies the gait characteristics of a quadruped rover that mimics domestic cats, and attempts to optimize these characteristics. The kinematics and dynamics formulation of the rover’s three-dimensional model is developed, and its gait, pose and corresponding control parameters are computed to minimize torque or maximize speed, using a genetic algorithm. The optimization model consists of a set of equality and inequality constraints that ensure the feasibility and stability of the gaits, while considering the entire gait spectrum that feline species exhibit. The optimal gaits for minimizing the torque closely resemble lateral sequence gaiting, with a trotting behaviour as speed increases. A running gait is obtained at the maximum speed. The optimization results appear to conform to the biological observations of feline species, suggesting the tendency of conserving energy in biological gaiting.

An optimization model is developed in this paper for the joint trajectories of a hopping robot with a four-bar linkage leg. The dynamic behaviour of the one-legged robot is investigated during the stance and swing phases, and their impacts on gait planning are analysed. Certain constraints characterizing the continuous and cyclic motion of the robot are obtained. The optimization model is solved for the minimum torque and maximum velocity objective functions separately, and the results are compared with those in nature.

This is the first publication presenting the minihumanoid robot THBIP-2, the second-generation biped of Tsinghua University. It is 70 cm in height and 18 kg in weight with 24 degrees of freedom. This paper mainly addresses its mechatronics system realization, including the conceptual design, actuation system, sensing system, and control system. In addition, a walking stability controller based on zero moment point criterion and the walking simulation are presented. Finally, experiments validate and confirm the efficiency of the design.

Adaptive gait planning is an important aspect in the development of control systems for multi-legged robots traversing on rough terrain. The problem of adaptive gait generation can be viewed as one of finding a sequence of suitable foothold on rough terrain so that legged systems maintain static stability and motion continuity. Due to the limit of static stability, deadlock situation may occur in the process of searching for a suitable foothold, if terrain contains a large number of forbidden zones. In this paper, an improved method for adaptive gait planning is presented by active compensation of stability margin, through center of gravity (CG) adjustment in the longitudinal axis and/or body translation in the lateral direction. An algorithm for the proposed method is developed and embedded in a computer program. Simulation results show that the method provides legged machines with a much larger terrain adaptivity and better deadlock-avoidance ability.