This paper presents a complete planner and controller scheme for bipedal robots, designed to enhance robustness against external disturbances. The high-level planner utilizes model predictive control (MPC) to optimize both the foothold location and step duration based on the divergent component of motion (DCM) to increase the robustness of generated gaits. For low-level control, we employ a momentum-based observer capable of estimating external forces acting on both stance and swing legs. The full-body dynamics, incorporating estimated disturbances, are integrated into a weighted whole-body control (WBC) to obtain more accurate ground reaction forces needed by the momentum-based observer. This approach eliminates the dependency on foot-mounted sensors for ground reaction force measurement, distinguishing our method from other disturbance estimation methods that rely on direct sensor measurements. Additionally, the controller incorporates trajectory compensation mechanisms to mitigate the effects of external disturbances. The effectiveness of the proposed framework is validated through comprehensive simulations and experimental evaluations conducted on BRUCE, a miniature bipedal robot developed by Westwood Robotics (Los Angeles, CA, USA). These tests include walking under swing leg disturbances, traversing uneven terrain, and simultaneously resisting upper-body pushes.

This paper presents a complete planner and controller scheme to achieve balance and walking for a biped robot, which does not need to distinguish the robot’s dynamic model parameters. The high-level planner utilizes model predictive control to optimize both the foothold location and step duration based on the Divergent Component of Motion (DCM) model to enhance the robustness of generated gaits. For low-level control, we use quadratic programming (QP) to optimize the contact force distribution under the contact constraints to achieve the virtual wrench exerted on the base of the robot. Then, the joint torques sent to the robot are derived from three parts: first, the torques mapped from the contact force; second, the swing leg tracking; and third, the stance foot stabilization. The simulation and experiment on BRUCE, a miniature bipedal robot from Westwood Robotics (Los Angeles, CA, USA), testify to the performance of the control scheme, including push recovery, Center of Mass (CoM) tracking, and omnidirectional walking.

Various constraints exist in bipedal movement. Due to the natural ability of effectively handling constraints, trajectory optimization has become one of the mainstream methods in biped gait planning, especially when constraints become much more complex on non-flat terrain. In this paper, we propose a multi-modal biped gait planner based on DIRCON, which can generate different gaits for multiple, non-flat terrains. Firstly, a virtual knot is designed to model the state transitions when the swing foot contacts terrain and is inserted as the first knot of the target trajectory of the current support phase. Thus, a complete gait or multi-modal gaits sequence can be generated at one time. Then, slacked complementary constraints, which can avoid undesired trajectories, are elaborated to describe the coupling relationships between terrain information and bipedal motion for trajectory optimization based gait planning. The concrete form of the gait planner is also delivered. Finally, we verify the performance of the planner, as well as the structural design of our newly designed biped robot in CoppeliaSim through flat terrain walking, stairs terrain walking and quincuncial piles walking. The three experiments show that the gaits planned by the proposed planner can enable the robot to walk stably over non-flat terrains, even through simple PD control.

Biped locomotion provides more mobility and effectiveness compared with other methods. Animals have evolved efficient walking patterns that are pursued by biped robot researchers. Current researchers have observed that symmetry is a critical criterion to achieve efficient natural walking and usually realize symmetrical gait patterns through morphological characteristics using simplified dynamic models or artificial priors of the center of mass (CoM). However, few considerations of symmetry and energy consumption are introduced at the joint level, resulting in inefficient leg motion. In this paper, we propose a full-order biped gait planner in which the symmetry requirement, energy efficiency, and trajectory smoothness can all be involved at the joint level, and CoM motion is automatically determined without any morphological prior. In order to achieve a symmetrical and efficient walking pattern, we first investigated the characteristic of a completely symmetrical gait, and a group of nearly linear slacked constraints was designed for three phases of planning. Then a Constrained Direct Collocation (DIRCON)-based full-order biped gait planner with a weighted cost function for energy consumption and trajectory smoothness is proposed. A dynamic simulation with our newly designed robot model was performed in CoppliaSim to test the planner. Physical comparison experiments on a real robot device finally validated the symmetry characteristic and energy efficiency of the generated gait. In addition, a detailed presentation of the real biped robot is also provided.

The current morphing technologies are mostly regarded as auxiliary tools, providing additional control torques to enhance the flight maneuverability of unmanned aerial vehicles (UAVs), and they cannot exist independently of the traditional control surfaces. In this paper, we propose a tandem-wing micro aerial vehicle (MAV) with multiple variable-sweep wings, which can reduce the additional inertia forces and moments and weaken the dynamic coupling between longitudinal and lateral motion while the MAV morphs symmetrically for pitch control or asymmetrically for roll control, thereby flying without the traditional aileron and elevator. First, load experiments were conducted on the MAV to verify the structural strength of the multiple variable sweep wings, and the control moments caused by the morphing of the MAV were presented through numerical simulations. Then, the effects caused by symmetric and asymmetric morphing were investigated via dynamic response simulations based on the Kane dynamic model of the MAV, and the generated additional inertia forces and moments were also analyzed during morphing. Finally, dynamic response experiments and open-loop flight experiments were conducted. The experimental results demonstrated that the morphing mode in this study could weaken the coupling between the longitudinal and lateral dynamics and that it was feasible for attitude control without the traditional aileron and elevator while flying.

This article introduces a novel perspective on designing a stepping controller for bipedal robots. Typically, designing a state-feedback controller to stabilize a bipedal robot to a periodic orbit of step-to-step (S2S) dynamics based on a reduced-order model (ROM) can achieve stable walking. However, the model discrepancies between the ROM and the full-order dynamic system are often ignored. We introduce the latest results from behavioral systems theory by directly constructing a robust stepping controller using input-state data collected during flat-ground walking with a nominal controller in the simulation. The model uncertainty discrepancies are equivalently represented as bounded noise and over-approximated by bounded energy ellipsoids. We conducted extensive walking experiments in a simulation on a 22-degrees-of-freedom small humanoid robot, verifying that it demonstrates superior robustness in handling uncertain loads, various sloped terrains, and push recovery compared to the nominal S2S controller.