Aiming at improving the efficiency of reconfiguration of mobile modular robots, the optimal reconfiguration planning and parallel control method are proposed in this work. The number of moving modules, the number of undocking actions, and the total moving distance are considered in reconfiguration optimization with the lexicographic framework. The maximum common subgraph matching and searching tree are proposed to search the Pareto optimal solution. A distributed controller based on local consensus is designed for components that are rigidly connected by different numbers of modules and under different shapes. Parallel motion in the shared workspace brings a high efficiency of reconfiguration. The simulations and real-world experiments verify the effectiveness of our method.

The orientation information is critical for the mobile robot. However, the robot’s constrained locomotion, complicated magnetic field environment, and the coupled orientation estimation present challenges for using the microelectromechanical magnetometer in real-time robot orientation determination. This article focuses on calibrating the magnetometer in constrained robot locomotion and achieving the robot’s decoupled roll, pitch, and yaw estimates. A magnitude-free and reference-free calibration approach is proposed to calibrate the magnetometer using the measurements collected from the principal plane of the robot locomotion. Moreover, a decoupled orientation estimator is created under the framework of the extended Kalman filter using the calibrated triaxial magnetic, angular rate, and gravity sensor readings. Two independent experiments validate the effectiveness of magnetometer calibration and orientation decoupling, respectively. A case study of estimating the mobile robot’s orientation shows that the proposed methods can achieve accurate heading estimation and decouple the roll and pitch from the magnetic field data.

Real-time motion planning in dynamic environments presents a significant challenge for robotic manipulators. This paper introduces an innovative parallel Model Predictive Path Integral (MPPI) algorithm enabling the robot to navigate swiftly and safely in such environments. Unlike the conventional MPPI methods that rely on a single sequence of Gaussian means for trajectory sampling, the proposed Parallel MPPI (PMPPI) concurrently runs multiple planners with different strategies and adaptively integrates planned paths based on the current state, leveraging the advantages of different strategies and greatly improving the MPPI's exploration capability. Moreover, a Gradient-Velocity Modulated Signed Distance Field (SDF) cost function that dynamically adjusts costs based on the robot's velocity and the SDF gradient is defined, thereby promoting safer and purposeful motion planning. In the implementation, techniques like utilizing inverse kinematics solver for path guidance and Sparse Reward to expedite reaching time are integrated into the MPPI cost function design. Comparative evaluations against the traditional MPPI architecture and standard SDF cost designs demonstrate the superiority of the new method. Real-world experiments, including human-robot interaction, obstacle-crossing, and grasping tasks, validate the robustness and universality of our methodology, with average and maximum end effector speeds of 0.523 m/s and 1.225 m/s respectively.