The driving and steering capabilities of the six-wheeled independent robot enable it to adapt to complex terrain and perform a variety of tasks. But the traditional control method cannot fully utilize its flexibility of independent driving of each wheel, and the control effect is poor in complex environments. In this work , we propose a multi-wheel cooperative control method in a distributed framework. The method leverages the robot’s independent driving and steering capabilities, employing a distributed model predictive control framework to synchronize the motion of six wheels. This is achieved by formulating individual wheel predictive control objectives and establishing inter-wheel trajectory and synchronization constraints, enabling coordinated multi-wheel locomotion. Considering the unknown perturbation factors such as road slippage and rugged terrain in the actual environment, an error model-based extended state observer algorithm is designed as a feed-forward controller to estimate the unknown perturbations and generate the control volume gain matrix by combining the estimated values to improve the robot’s adaptability. The control method effectively solves the motion control problem of the robot in the complex environment and verifies its effectiveness and robustness in practical applications.

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.

This paper focuses on developing a real-time and flexible velocity estimation approach for serial revolute manipulator using only one inertial measurement unit (IMU) mounted on each link side of the manipulator. Particularly, the proposed approach has no requirement for the installation position and orientation of the IMU, which improves the flexibility of the implementation procedure. A joint velocity model is established based on the proposed principle of constructing coordinate system according to the robotic geometric information. The general solutions are derived in detail, thereby the proposed algorithm can be generalized into any other robots with the same geometric configuration. With the method, the joint rotational velocity measurements of static and dynamic robotic motion are provided compared to encoders. Experimental results based on the six degrees of freedom (DOF) collaborative manipulator have validated the feasibility and effectiveness of the proposed approach. The proposed method has the benefits of low cost and flexibility, which could work as a redundant velocity monitor criterion to provide assistant joint velocity measurements.