In this paper, we propose a new path planning algorithm based on the probabilistic roadmaps method (PRM), in order to effectively solve the autonomous path planning of mobile robots in complex environments with multiple narrow channels. The improved PRM algorithm mainly improves the density and distribution of sampling points in the narrow channel, through a combination of the learning process of the PRM algorithm and the APF algorithm. We also shortened the required time and path length by optimizing the query process. The first key technology to improve the PRM algorithm involves optimizing the number and distribution of free points and collision-free lines in the free workspace. To ensure full visibility of the narrow channel, we extend the obstacles through the diagonal distance of the mobile robot while ignoring the safety distance. Considering the safety distance during movement, we re-classify the all sampling points obtained by the quasi-random sampling principle into three categories: free points, obstacle points, and adjacent points. Next, we transform obstacle points into the free points of the narrow channel by combining the APF algorithm and the characteristics of the narrow channel, increasing the density of sampling points in the narrow space. Then, we include potential energy judgment into the construction process of collision-free lines shortening the required time and reduce collisions with obstacles. Optimizing the query process of the PRM algorithm is the second key technology. To reduce the required time in the query process, we adapt the bidirectional A* algorithm to query these local paths and obtain an effective path to the target point. We also combine the path pruning technology with the potential energy function to obtain a short path without collisions. Finally, the experimental results demonstrate that the new PRM path planning technology can improve the density of free points in narrow spaces and achieve an optimized, collision-free path in complex environments with multiple narrow channels.

The motion control of robot manipulators is a crucial problem concerning automatically controlled robots. In this work, the model predictive control method with an integral compensation (MPC-I), which compensates for the matched uncertainties due to unmodeled dynamics, is proposed to solve the trajectory tracking problem of robot manipulators in joint and task spaces. First, this paper decouples the joint variables of the robot manipulator using a computed torque control method. The MPC-I method is, thereafter, derived to realize the motion control of the robot manipulators in joint space. To realize the motion control of the robot manipulator in task space, the task space is, thereafter, converted into the joint space, in which the MPC-I method is executed, afterward, to control the robot. Furthermore, an MPC-I variation, in which the inverse kinematics is calculated indirectly, is proposed to achieve the motion control in task space. The novelty of this paper is to propose the MPC-I method and the method of converting task space to joint space with indirect inverse kinematics calculation. The former is suitable for the dynamic control of the robot manipulators in the joint space, and the latter can extend the MPC-I method to dynamic control in task space. To evaluate the performance of the proposed control method, motion control simulations are performed in the task and joint spaces, respectively. Simulation results and comparisons verify the effectiveness of the proposed control approach for the dynamic control of the UR5 robot manipulator.

This study proposes a method for judging the existence of closed-form inverse kinematics solutions based on the Denavit–Hartenberg (DH) model. In this method, serial robots with closed-form solutions are described using three types of sub-problems from the viewpoint of solving algebraic equations. If a serial robot can be described using these three types of sub-problems, i.e., if the inverse kinematics problems can be solved by several basic problems, then there is a closed-form solution. Based on the above method, we design a set of universal closed-form inverse kinematics solving algorithms. Since there is a definite formula solution for the three types of sub-problems, the joint angles can be rapidly determined. In addition, because the DH parameters can directly reflect the linkage of the robot, the judgment of the sub-problems is also quick and accurate. More importantly, the algorithm can be applied to serial robots with low degrees of freedom. This enables the algorithm to not only quickly and accurately solve inverse kinematics problems but also to exhibit high universality. This proposed theory improves the existence conditions for closed-form reverse solutions and further promotes the development of motion control techniques for serial robots.

In this paper, a third-order S-curve is used to model the motion issue. A closed method of solving is derived for the optimal point-to-point(P2P) time issue based on the model. Based on the P2P time-optimal solution, a closed multi-axis synchronization algorithm (MASA) method is proposed. This method considers the kinematic equation of the S-curve based on the inverse function of the multivariate function. Therefore, the inverse function of the execution time is derived via the kinematic equation of the P2P time optimization issue and solving a set of smaller bandwidth parameters is an important attempt in this paper. Otherwise, based on the MASA proposed in this paper, a synchronous computing framework suitable for the planning of robot space poses was designed. This framework can effectively solve problems with robot pose planning. The innovations of this article can be summarized as:(1)Based on the third-order S-curve, this paper deduced a complete method for solving the P2P time-optimal curve.(2) Based on the method for solving the time-optimal curve, this paper further derived three synchronization methods based on time constraints.(3) Based on the MASA proposed in this paper, a synchronous computing framework suitable for the planning of robot space poses was designed. The continuity and smoothness of the robot joint spatial motion can be guaranteed in the Descartes space. Few scholars have studied the closed solution method of multi-axis synchronization. The research in this paper promotes the theoretical research of motion control.

In this paper, we propose an optimized differential evolution algorithm based on kinematic limitations and structural complexity constraints to solve the trajectory tracking problem for a mobile manipulator robot. The traditional method mainly involves obtaining the speed of the control variable based on the Jacobian inverse or linearization of the robot’s kinematic model, which cannot avoid the singularity position and/or self-collision phenomena. To address these problems, we directly design an optimized differential evolution algorithm to solve the trajectory planning problem for mobile manipulator robots. First, we analyze various constraints on the actual movement and describe them specifically using various equations or inequalities, including non-holonomic constraints on the mobile platform, the physical limitations of the driving motors, self-collision avoidance restriction, and smoothly traversing the singularity position. Next, we re-define the trajectory tracking of a mobile manipulator robot as an optimization problem under multiple constraints, including the trajectory tracking task and various constraints simultaneously. Then, we propose a new differential evolution (DE) algorithm by optimizing some critical operations to solve the optimization problem, such as improving the population’s distribution, limiting the population distribution range, and adding a success index. Additionally, we design two simple trajectories and two complex trajectories to determine the performance of the optimized DE algorithm in solving the trajectory tracking problem. The results demonstrate that the optimized DE algorithm can effectively realize the high-precision trajectory tracking task of a differential wheeled mobile manipulator robot through the consideration of kinematic limitations and self-collision avoidance.

Based on the character that the hexapod walking robot is composed by parallel closed loop and series open loop, choosing proportion of the robot's each leg as the cut-in point, this paper analyzes the specific method of hexapod walking bio-robot structure optimizing, works out the robot's feet space using the numerical analysis method calculates the robot's degree of agility utilizing virtual prototyping technique, measures the flexibility of the robot's leg and body fully. Hereby we review the robot's whole working capability, which provides the theory basis on the hexapod walking bio-robot's proper drive and exact control.