This article presents a hierarchical model predictive control (MPC) framework for the end-effector tracking control problem of a legged mobile manipulator. In the high-level part, a kinematic MPC over a long-time horizon computes both base and joint trajectories, then a quadratic program (QP) based optimization solves ground-reaction-forces (GRFs) satisfying the robot’s centroidal dynamics and the friction cone constraints. In the low-level part, a kinodynamic MPC over a short-time horizon tracks command end-effector trajectories and outputs from the high-level part while satisfying all nonlinear dynamics constraints. Due to the complexity of MPC formulations and high real-time requirements, traditional MPC for legged mobile manipulators can only generate short-time horizon solutions for tracking tasks over longer time horizons, which may lead to the optimization falling into bad local minima. In our method, the long-term trajectories from the high-level part can guide the optimization of the short-term kinodynamic MPC to generate a better solution. We validate the effectiveness of our method through several simulation and hardware experiments. In comparison to traditional MPC, the proposed method improves the trajectory tracking accuracy of the robot’s end-effector while reducing the violations of the system’s physical limit constraints and environment-collision avoidance constraints.Note to Practitioners—Legged mobile manipulators have received increasing research attention in recent years due to their potential applications. In this paper, we particularly focus on the high-efficiency end-effector tracking control of legged mobile manipulators. We propose a hierarchical MPC framework incorporating a high-level part generating long-term whole-body motions and a low-level kinodynamic MPC replanning at a high frequency. Simulation and hardware experiments show that the proposed method improves the trajectory tracking accuracy while reducing the violations of the system’s physical limit constraints and environment-collision avoidance constraints in comparison to traditional MPC. In future research, we will address the end-effector tracking control problem when the end-effector contacts the environment.

Designing insect-scale robots with high mobility is becoming an essential challenge in the field of robotics research. Among the methods for fabricating the transmission mechanism of the insect-scale robot, the smart composite microstructure (SCM) method is getting more and more attention. This method can construct compact and functional miniature origami mechanisms through planarized fabrication and folding assembly processes. Our previous work has proposed an untethered robot S$^{\text{2}}$worm equipped with a novel 2-DoF origami transmission mechanism. The S$^{\text{2}}$worm is fabricated through SCM and holds a top speed of 27.4 cm/s. In this work, we propose a novel strategy for designing the insect-scale robot with high mobility, that is, applying Grassmann–Cayley Algebra (GCA) to avoid the singularity of the transmission mechanism. The experimental results prove that the singularity of the previous work has been solved. The new robot prototype S$^{\text{2}}$worm-G weighs 4.71 g, scales 4.0 cm, achieves a top speed of 75.0 cm/s and a relative speed of 18.8 bodylength/s. To the best of our knowledge, the 2-DoF origami transmission mechanism is the first parallel mechanism designed for the insect-scale robot and the singularity of the mechanism is found and solved here. The experimental results prove that the refined S$^{\text{2}}$worm-G robot is one of the best insect-scale robots for its size, mass, and mobility.

Designing a swarm of robots to address different tasks and adapt to various environments through self-assembly is one of the most challenging topics in the field of robotics research. Here, we present an assembly robotic system with inchworm robots as modules. The system is called BioARS (Bionic Assembly Robotic System). It can either work as a swarm of individual untethered inchworm robots or be assembled into a quadruped robot. The inchworm robots are connected by magnets using a "shoulder-to-shoulder" connecting method, which helps strengthen the magnetic connection. Central pattern generators are used to control the trot gait of the quadruped robot. Our experiments demonstrate that the bionic assembly system is adaptive in that it can pass through confined spaces in the form of inchworms and walk on rough terrain in the form of a quadruped robot. The proposed BioARS, therefore, combines the flexibility of inchworms and the adaptability of quadruped animals, which is promising for application in planetary exploration, earthquake search and rescue operations. We also provide several examples of directions for future research regarding our system.