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The sanctity of human life mandates the replacement of individuals with robotic systems in the execution of hazardous tasks. Explosive Ordnance Disposal (EOD), a field fraught with mortal danger, stands at the forefront of this transition. In this study, we explore the potential of robotic telepresence as a safeguard for human operatives, drawing on the robust capabilities demonstrated by legged manipulators in diverse operational contexts. The challenge of autonomy in such precarious domains underscores the advantages of teleoperation—a harmonious blend of human intuition and robotic execution. Herein, we introduce a cost-effective telepresence and teleoperation system employing a legged manipulator, which combines a quadruped robot, an integrated manipulative arm, and RGB-D sensory capabilities. Our innovative approach tackles the intricate challenge of whole-body control for a quadrupedal manipulator. The core of our system is an IMU-based motion capture suit, enabling intuitive teleoperation, augmented by immersive visual telepresence via a VR headset. We have empirically validated our integrated system through rigorous real-world applications, focusing on loco-manipulation tasks that necessitate comprehensive robot control and enhanced visual telepresence for EOD operations. [ABSTRACT FROM AUTHOR]

This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot's stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot's own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog's own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains. [ABSTRACT FROM AUTHOR]

A redundant Hybrid Cable-Driven Robot (HCDR), which is an integration platform of a robotic manipulator and a Cable-Driven Parallel Robot (CDPR) has numerous advantages including an extensive operational range and high performance in task execution. However, it is difficult to control the redundant HCDR due to the dynamic coupling between a manipulator and the CDPR and the redundancy resolution. To solve these issues, this paper introduces a novel whole-body controller strategy for the redundant HCDR by using Hierarchical Quadratic Programming (HQP). First, the whole-body nonlinear dynamics with cable tensions and manipulator torques is proposed. Then, with the HQP-based scheme, the proposed controller can generate the optimal tensions and torques for the redundant HCDR to generate whole-body motions without dynamic interference between the CDPR and the manipulator. In addition, singularity and joint limit avoidance algorithms are proposed in this paper, because the proposed controller can handle prioritized tasks with inequality constraints as well as equality constraints. The proposed control scheme has been implemented on the redundant HCDR with 8-cables and 7-DoFs manipulator, and its performance is demonstrated through various scenarios in high-precision simulator. [ABSTRACT FROM AUTHOR]

The sanctity of human life mandates the replacement of individuals with robotic systems in the execution of hazardous tasks. Explosive Ordnance Disposal (EOD), a field fraught with mortal danger, stands at the forefront of this transition. In this study, we explore the potential of robotic telepresence as a safeguard for human operatives, drawing on the robust capabilities demonstrated by legged manipulators in diverse operational contexts. The challenge of autonomy in such precarious domains underscores the advantages of teleoperation—a harmonious blend of human intuition and robotic execution. Herein, we introduce a cost-effective telepresence and teleoperation system employing a legged manipulator, which combines a quadruped robot, an integrated manipulative arm, and RGB-D sensory capabilities. Our innovative approach tackles the intricate challenge of whole-body control for a quadrupedal manipulator. The core of our system is an IMU-based motion capture suit, enabling intuitive teleoperation, augmented by immersive visual telepresence via a VR headset. We have empirically validated our integrated system through rigorous real-world applications, focusing on loco-manipulation tasks that necessitate comprehensive robot control and enhanced visual telepresence for EOD operations.

Bipedal locomotion requires a multi-level control strategy for balance and tracking, with the zero-moment point (ZMP) serving as a heuristic balance criterion. Maintaining the ZMP location within the stability margin indicates stability, but ankle joint actuation behavior restrictions are required. This paper focuses on whole-body control of a bipedal robot, considering control input limitations. Two dynamical models of bipedal dynamics are introduced, integrating center-of-mass (CoM)-based dynamics with joint space dynamics. The controlled outputs are the CoM position and joint displacements, while the control inputs are the ZMP position and joint torques. Anti-input saturation control is considered to ensure safe values for the control inputs, especially for the ZMP and ankle joint torque signals. A decentralized adaptive approximation control (DAAC) with a saturation compensator is designed. The stability of the proposed controller is proven using Lyapunov theory. Simulation experiments are conducted on a planar 6-degrees-of-freedom (DOF) bipedal robot. The results demonstrate the robustness of the control architecture even under a disturbance torque of 10 N.m., ensuring safe stability margins for the ZMP and precise tracking for the multi-DOF bipedal system. [ABSTRACT FROM AUTHOR]

This paper presents a new control approach for a humanoid biped robot to perform highly dynamic locomotion and keep balance under large external disturbances. The proposed control approach mainly contains two parts: a simplified model-based task planner and an online whole-body controller. The periodic stability is achieved with a foot placement policy presented in this paper. The landing position is predicted and adjusted at every time step to adapt to uncertain disturbances. Then the desired task trajectories are tracked with a quadratic programming (QP) based task space whole-body controller, while satisfying some specific physical constraints. The whole-body controller outputs the optimized joint accelerations, which are then used to produce desired joint torques for the robot's actuators. The positions of the center of mass (CoM) along sagittal and lateral axes are not controlled in the whole-body controller. Meanwhile, the torques at the ankles are constrained to be small and the robot follows a passive-like behavior, which is energetically efficient and helps the feet of the robot to fit better in the environment. Simulations on several highly dynamic gaits have been conducted, two examples among which are presented in this paper. The first demonstrates a walking gait with an increasing sagittal speed and its capability to resist large external disturbances. In the second example, the robot is able to run stably on uneven terrain without knowing any information about it. [ABSTRACT FROM AUTHOR]

The dynamic motion of quadrupedal robots on challenging terrain generally requires elaborate spatial–temporal kinodynamic motion planning and accurate control at higher refresh rate in comparison with regular terrain. However, conventional quadrupedal robots usually generate relatively coarse planning and employ motion replanning or reactive strategies to handle terrain irregularities. The resultant complex and computation-intensive controller may lead to nonoptimal motions or the breaking of locomotion rhythm. In this paper, a kinodynamic optimization approach is presented. To generate long-horizon optimal predictions of the kinematic and dynamic behavior of the quadruped robot on challenging terrain, we formulate motion planning as an optimization problem; jointly treat the foot’s locations, contact forces, and torso motions as decision variables; combine smooth motion and minimal energy consumption as the objective function; and explicitly represent feasible foothold region and friction constraints based on terrain information. To track the generated motions accurately and stably, we employ a whole-body controller to compute reference position and velocity commands, which are fed forward to joint controllers of the robot’s legs. We verify the effectiveness of the developed approach through simulation and on a physical quadruped robot testbed. Results show that the quadruped robot can successfully traverse a 30° slope and 43% of nominal leg length high step while maintaining the rhythm of dynamic trot gait. [ABSTRACT FROM AUTHOR]

This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot’s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot’s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog’s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains.

When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive fuzzy pure pursuit trajectory tracking algorithm for autonomous unmanned ground vehicles (UGVs), addressing the challenges of accurate and stable navigation in complex environments. Traditional pure pursuit methods with fixed look-ahead distances struggle to maintain precision in dynamic and uneven terrains. Our approach uniquely integrates a fuzzy control algorithm that allows for real-time adjustments of the look-ahead distance based on environmental feedback, thereby enhancing tracking accuracy and smoothness. Additionally, we combine this with model predictive control (MPC) and whole-body motion control (WBC), where MPC forecasts future states and optimally adjusts control actions, while WBC ensures coordinated motion of the UGV, maintaining balance and stability, especially in rough terrains. This integration not only improves responsiveness to changing conditions but also enables dynamic balance adjustments during movement. The proposed algorithm was validated through simulations in Gazebo and real-world experiments on physical platforms. In real-world tests, our algorithm reduced the average trajectory tracking error by 45% and the standard deviation by nearly 50%, significantly improving stability and accuracy compared to traditional methods.

Quadruped robots working in jungles, mountains or factories should be able to move through challenging scenarios. In this paper, we present a control framework for quadruped robots walking over rough terrain. The planner plans the trajectory of the robot's center of gravity by using the normalized energy stability criterion, which ensures that the robot is in the most stable state. A contact detection algorithm based on the probabilistic contact model is presented, which implements event‐based state switching of the quadruped robot legs. And an on‐line detection of contact force based on generalized momentum is also showed, which improves the accuracy of proprioceptive force estimation. A controller combining whole body control and virtual model control is proposed to achieve precise trajectory tracking and active compliance with environment interaction. Without any knowledge of the environment, the experiments of the quadruped robot SDUQuad‐144 climbs over significant obstacles such as 38 cm high steps and 22.5 cm high stairs are designed to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel design strategy and task‐priority‐based control methodology for a robot to successfully complete a rescue operation in an extremely unstructured environment. The mechanical structure is designed to obtain both versatile manipulability and all‐terrain mobility. The regularized hierarchical quadratic program is used for whole‐body motion and force control. The optimization strategy is reasoning about regularization and thus it ensures convergence of the solution in the face of singularities while taking into account equality and inequality constraints. We demonstrate the effectiveness of the online optimization‐based control algorithms through extensive real‐world numerical and experimental results. Finally, we highlight that the rescue robot can successfully execute missions to extract a casualty and dispose of a dangerous object both indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

Abstract Squatting is a basic movement of bipedal robots, which is essential in robotic actions like jumping or picking up objects. Due to the intrinsic complex dynamics of bipedal robots, perfect squatting motion requires high‐performance motion planning and control algorithms. The standard academic solution combines model predictive control (MPC) with whole‐body control (WBC), which is usually computationally expensive and difficult to implement on practical robots with limited computing resources. The real‐time kinematic prediction (RKP) method is proposed, which considers upcoming reference motion trajectories and combines it with quadratic programming (QP)‐based WBC. Since the WBC handles the full robot dynamics and various constraints, the RKP only needs to adopt the linear kinematics in the robot's task space and to softly constrain the desired accelerations. Then, the computational cost of derived closed‐form RKP is greatly reduced. The RKP method is verified in simulation on a heavy‐loaded bipedal robot. The robot makes rapid and large‐amplitude squatting motions, which require close‐to‐limit torque outputs. Compared with the conventional QP‐based WBC method, the proposed method exhibits high adaptability to rough planning, which implies much less user interference in the robot's motion planning. Furthermore, like the MPC, the proposed method can prepare for upcoming motions in advance but requires much less computation time.

In this paper, we introduce a new teen-sized humanoid platform dubbed DRACO 3, custom-built by Apptronik and altered for practical use by the Human Centered Robotics Laboratory at The University of Texas at Austin. The form factor of DRACO 3 is such that it can operate safely in human environments while reaching objects at human heights. To approximate the range of motion of humans, this robot features proximal actuation and mechanical artifacts to provide a high range of hip, knee, and ankle motions. In particular, rolling contact mechanisms on the lower body are incorporated using a proximal actuation principle to provide an extensive vertical pose workspace. To enable DRACO 3 to perform dexterous tasks while dealing with these complex transmissions, we introduce a novel whole-body controller (WBC) incorporating internal constraints to model the rolling motion behavior. In addition, details of our WBC for DRACO 3 are presented with an emphasis on practical points for hardware implementation. We perform a design analysis of DRACO 3, as well as empirical evaluations under the lens of the Centroidal Inertia Isotropy (CII) design metric. Lastly, we experimentally validate our design and controller by testing center of mass (CoM) balancing, one-leg balancing, and stepping-in-place behaviors.

Legged manipulators are a prime candidate for reducing risk to human lives through completing tasks in hazardous environments. However, controlling these systems in real-world applications requires a highly functional teleoperation framework, capable of leveraging all utility of the robot to complete tasks. In this work, such a teleoperation framework is presented, where a wearable whole-body motion capture suit is integrated with a whole-body controller specialised for teleoperation and a set of teleoperation strategies that enable the control of all main frames of the robot along with additional functions. Within the whole-body controller, all tasks and constraints can be configured dynamically due to their modularity, hence enabling seamless transitions between each teleoperation strategy. As a result, this not only enables the realisation of trajectories outside the workspace without the whole-body controller but also the ability to complete tasks that would require an additional manipulator if just the gripper frames of the robot were controllable. To validate the presented framework, a set of real robot experiments have been completed to demonstrate all teleoperation strategies and analyse their proficiency. [ABSTRACT FROM AUTHOR]

Exoskeletons can help humans in a variety of ways in performing tasks. In particular, during the lifting operation, a human places a great burden on the knee or waist joint, and the exoskeleton can reduce the risks of this task. However, due to the weight of the exoskeleton itself and the movement of the overall center of gravity, balance ability and efficiency may decrease. Therefore, an appropriate assistance torque distribution strategy is required to achieve high performance with the exoskeleton. In order to solve the aforementioned problem, we propose an assistance method based on whole-body control. The proposed algorithm is meaningful because it is different from other simple model-based controllers. The controller fully utilize the dynamics to achieve a high performance. In addition, by adding a straight leg cost term, the singularity problem in the fully extended configuration was solved. This method finds the optimal solution that satisfies various constraints and minimizes the objective functions. Each objective is composed of a balancing-related term that minimizes the variation in the center of gravity, a term that supports the weight of the human and exoskeleton, a term that solves the singularity problem and a term related to efficiency. In this paper, first, a motion capture experiment is performed to analyze a human's lifting motion. Through this experiment, the trajectory of each joint angle is obtained. With PD (proportional-derivative) feedback from the joint trajectories, the exoskeleton generates human torque in the simulation and implements a lifting operation. Second, a simulation is performed with the proposed controller. As a result, it is confirmed that the proposed method reduces the amount of human joint torque and increases stability and efficiency. [ABSTRACT FROM AUTHOR]

When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms’ optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot’s movement. These constraints include whole-body dynamics, center of mass constraints, arm’s end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.

Regardless of recent advances, humanoid robots still face significant difficulties in performing locomotion tasks. Among the key challenges that must be addressed to achieve robust bipedal locomotion are dynamically consistent motion planning, feedback control, and state estimation of such complex systems. In this paper, we investigate the use of an external motion capture system to provide state feedback to an online whole-body controller. We present experimental results with the humanoid robot RH5 performing two different whole-body motions: squatting with both feet in contact with the ground and balancing on one leg. We compare the execution of these motions using state feedback from (i) an external motion tracking system and (ii) an internal state estimator based on inertial measurement unit (IMU), forward kinematics, and contact sensing. It is shown that state-of-the-art motion capture systems can be successfully used in the high-frequency feedback control loop of humanoid robots, providing an alternative in cases where state estimation is not reliable. [ABSTRACT FROM AUTHOR]

Balancing is a fundamental task in the motion control of bipedal robots. Compared to two-foot balancing, one-foot balancing introduces new challenges, such as a smaller supporting polygon and control difficulty coming from the kinematic coupling between the center of mass (CoM) and the swinging leg. Although nonlinear model predictive control (NMPC) may solve this problem, it is not feasible to implement it on the actual robot because of its large amount of calculation. This paper proposes the three-particle model predictive control (TP-MPC) approach. It combines with the hierarchical whole-body control (WBC) to solve the one-leg balancing problem in real time. The bipedal robot's torso and two legs are modeled as three separate particles without inertia. The TP-MPC generates feasible swing leg trajectories, followed by the WBC to adjust the robot's center of mass. Since the three-particle model is linear, the TP-MPC requires less computational cost, which implies real-time execution on an actual robot. The proposed method is verified in simulation. Simulation results show that our method can resist much larger external disturbance than the WBC-only control scheme. [ABSTRACT FROM AUTHOR]

The task of performing locomotion and manipulation simultaneously poses several scientific challenges, such as how to deal with the coupling effects between them and how to cope with unknown disturbances introduced by manipulation. This paper presents an inverse dynamics-based whole-body controller for a torque-controlled quadrupedal manipulator capable of performing locomotion while executing manipulation tasks. Unlike existing methods that deal with locomotion and manipulation separately, the proposed controller can handle them uniformly, which can take into account the coupling effects between the base, limbs and manipulated object. The controller tracks the desired task–space motion references based on a hierarchical optimization algorithm, given a set of hierarchies that define strict priorities and the importance of weighting each task within a hierarchy. The simulation results show the robot is able to follow multiple task–space motion reference trajectories with reasonable deviation, which proved the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method that computes the optimal solution of the weighted hierarchical optimization problem for both equality and inequality tasks. The method is developed to resolve the redundancy of robots with a large number of Degrees of Freedom (DoFs), such as a mobile manipulator or a humanoid, so that they can execute multiple tasks with differently weighted joint motion for each priority level. The proposed method incorporates the weighting matrix into the first-order optimality condition of the optimization problem and leverages an active-set method to handle equality and inequality constraints. In addition, it is computationally efficient because the solution is calculated in a weighted joint space with symmetric null-space projection matrices for propagating recursively to a low priority task. Consequently, robots that utilize the proposed method effectively show whole-body motions handling prioritized tasks with differently weighted joint spaces. The effectiveness of the proposed method was validated through experiments with a nonholonomic mobile manipulator as well as a humanoid. [ABSTRACT FROM AUTHOR]

For humanoid robots, maintaining a dynamic balance against uncertain disturbance is crucial, and this function can be achieved by coordinating the whole body to perform multiple tasks simultaneously. Researchers generally accept hierarchical whole-body control (WBC) to address this function. Although experts can build feasible hierarchies using prior knowledge, real-time WBC is still challenging because it often requires a quadratic program with multiple inequality constraints. In addition, the torque tracking performance of the WBC algorithm will be affected by uncertain factors such as joint friction for a large transmission ratio proprioceptive-actuated robot. Therefore, the balance control of physical robots requires a systematic solution. In this study, a robot control system with high computing power and real-time communication ability, UBTMaster, is implemented to achieve a reduced WBC in real time. Based on these, a whole-body control scheme based on task priority for the dynamic balance of humanoid robots is implemented. After realizing the joint friction model identification, finally, a variety of balancing scenarios are tested on the Walker3 humanoid robot driven by the proprioceptive actuators to verify the effectiveness of the proposed scheme. The Walker3 robot exhibits excellent balance when multiple external disturbances occur simultaneously. For example, the two feet of the robot are subjected to tilt and displacement perturbations, respectively, while the torso is subjected to external shocks simultaneously. The experimental results show that the dynamic balance of the robot under multiple external disturbances can be achieved by using strictly hierarchical real-time WBC with a systematic design. [ABSTRACT FROM AUTHOR]

The studies on quadruped robots equipped with arms are still rare at the moment. The interaction between the arm and the quadrupedal platform needs to be handled by whole-body controllers. This paper presents an optimization-based dynamic whole-body controller to solve the problem of when the robot stands still for manipulation. In order to reduce the strong interaction when the robot is trotting, we keep using the whole-body controller to handle locomotion control and resort to joint PD controllers for the arm's manipulation coupled with the mobile base on the kinematic level. Simulation results validate the expected locomotion and manipulation functionalities in both manipulation mode and loco-manipulation mode. The proposed control strategies are able to use the redundancy to perform multiple tasks in a dynamic system as such with 24 degrees of freedom. [ABSTRACT FROM AUTHOR]

Firm foot contact is the top priority of climbing robots to avoid catastrophic events, especially when working at height. This study proposes a robust planning and control framework for climbing robots that provides robustness to slippage in unknown environments. The framework includes 1) a center of mass (CoM) trajectory optimization under the estimated contact condition, 2) Kalman filter–like approach for uncertain environment parameter estimation and subsequent CoM trajectory re-planing, and 3) an online weight adaptation approach for whole-body control (WBC) framework that can adjust the ground reaction force (GRF) distribution in real time. Though the friction and adhesion characteristics are often assumed to be known, the presence of several factors that lead to a reduction in adhesion may cause critical problems for climbing robots. To address this issue safely and effectively, this study suggests estimating unknown contact parameters in real time and using the evaluated contact information to optimize climbing motion. Since slippage is a crucial behavior and requires instant recovery, the computation time for motion re-planning is also critical. The proposed CoM trajectory optimization algorithm achieved state-of-art fast computation via trajectory parameterization with several reasonable assumptions and linear algebra tricks. Last, an online weight adaptation approach is presented in the study to stabilize slippery motions within the WBC framework. This can help a robot to manage the slippage at the very last control step by redistributing the desired GRF. In order to verify the effectiveness of our method, we have tested our algorithm and provided benchmarks in simulation using a magnetic-legged climbing robot Manegto.

To enable the application of humanoid robots outside of laboratory environments, the biped must meet certain requirements. These include, in particular, coping with dynamic motions such as climbing stairs or ramps or walking over irregular terrain. Sit-to-stand transitions also belong to this category. In addition to their actual application such as getting out of vehicles or standing up after sitting, for example, at a table, these motions also provide benefits in terms of performance assessment. Therefore, they have long been used as a sports medical and geriatric assessment for humans. Here, we develop optimized sit-to-stand trajectories using optimal control, which are characterized by their dynamic and humanlike nature. We implement these motions on the humanoid robot REEM-C. Based on the obtained sensor data, we present a unified benchmarking procedure based on two different experimental protocols. These protocols are characterized by their increasing level of difficulty for quantifying different aspects of lower limb performance. We report performance results obtained by REEM-C using two categories of indicators: primary, scenario-specific indicators that assess overall performance (chair height and ankle-to-chair distance) and subsidiary, general indicators that further describe performance. The latter provide a more detailed analysis of the applied motion and are based on metrics such as the angular momentum, zero moment point, capture point, or foot placement estimator. In the process, we identify performance deficiencies of the robot based on the collected data. Thus, this work is an important step toward a unified quantification of bipedal performance in the execution of humanlike and dynamically demanding motions.

This paper presents practical enhancements of the operational space formulation (OSF) to exploit inequality constraints for whole-body control of a high degree of freedom robot with a floating base and multiple contacts, such as humanoids. A task-oriented optimisation method is developed to obtain a feasible torque resolution solely for task variables based on the OSF, which effectively reduces the number of optimisation variables. Interestingly, the proposed scheme amends assigned tasks on demand of satisfying inequality conditions, while dynamic consistency among contact-constrained tasks is preserved. In addition, we propose an efficient algorithm structure ameliorating real-time control capability which has been a major hurdle to transplant optimisation methods into the OSF-based whole-body control framework. Control performance, the feasibility of the optimised solution, and the computation time of the proposed control framework are verified through realistic dynamic simulations of a humanoid. We also clarify the pros and cons of the proposed method compared with existing optimisation-based ones, which may offer an insight for practical control engineers to select whole-body controllers necessitated from the desired application.

This paper proposes an online gain adaptation approach to enhance the robustness of whole-body control (WBC) framework for legged robots under unknown external force disturbances. Without properly accounting for external forces, the closed-loop control system incorporating WBC may become unstable, and therefore the desired task goals may not be achievable. To study the effects of external disturbances, we analyze the behavior of our current WBC framework via the use of both full-body and centroidal dynamics. In turn, we propose a way to adapt feedback gains for stabilizing the controlled system automatically. Based on model approximations and stability theory, we propose three conditions to ensure that the adjusted gains are suitable for stabilizing a robot under WBC. The proposed approach has four contributions. We make it possible to estimate the unknown disturbances without force/torque sensors. We then compute adaptive gains based on theoretic stability analysis incorporating the unknown forces at the joint actuation level. We demonstrate that the proposed method reduces task tracking errors under the effect of external forces on the robot. In addition, the proposed method is easy-to-use without further modifications of the controllers and task specifications. The resulting gain adaptation process is able to run in real-time. Finally, we verify the effectiveness of our method both in simulations and experiments using the bipedal robot Draco2 and the humanoid robot Valkyrie.

We propose a bipedal walking and impact reduction algorithm for a bipedal robot with pneumatically driven knees. The proposed algorithm is meaningful in that unlike in the existing studies on fully pneumatically driven robots and their control, it can overcome the low control performance of pneumatic actuators while utilizing the high compliance of pneumatic actuators through the high control performance of other joints in robots that apply pneumatic actuators only to the knee joint. Our algorithm takes advantage of whole-body control to overcome the low control performance of pneumatic systems and utilizes pneumatic compliance by simultaneously controlling the force and stiffness of the pneumatic actuators. Since a pneumatic actuator outputs a force, a force and torque converter is added to the general whole-body control framework, and a torque limit is considered as a function of the joint angle. In addition, a mechanism for selecting the stiffness of the knee is added. In particular, a force control method based on maintaining the minimum stiffness is applied to reduce the impact force when the ground conditions suddenly change. The pneumatics and the robot system were accurately modeled in a simulation, and the proposed algorithm was applied in the simulation to realize bipedal walking and to confirm the impact reduction effect in the event of a sudden ground condition change. [ABSTRACT FROM AUTHOR]

Balancing is a fundamental task in the motion control of bipedal robots. Compared to two-foot balancing, one-foot balancing introduces new challenges, such as a smaller supporting polygon and control difficulty coming from the kinematic coupling between the center of mass (CoM) and the swinging leg. Although nonlinear model predictive control (NMPC) may solve this problem, it is not feasible to implement it on the actual robot because of its large amount of calculation. This paper proposes the three-particle model predictive control (TP-MPC) approach. It combines with the hierarchical whole-body control (WBC) to solve the one-leg balancing problem in real time. The bipedal robot’s torso and two legs are modeled as three separate particles without inertia. The TP-MPC generates feasible swing leg trajectories, followed by the WBC to adjust the robot’s center of mass. Since the three-particle model is linear, the TP-MPC requires less computational cost, which implies real-time execution on an actual robot. The proposed method is verified in simulation. Simulation results show that our method can resist much larger external disturbance than the WBC-only control scheme.

Regardless of recent advances, humanoid robots still face significant difficulties in performing locomotion tasks. Among the key challenges that must be addressed to achieve robust bipedal locomotion are dynamically consistent motion planning, feedback control, and state estimation of such complex systems. In this paper, we investigate the use of an external motion capture system to provide state feedback to an online whole-body controller. We present experimental results with the humanoid robot RH5 performing two different whole-body motions: squatting with both feet in contact with the ground and balancing on one leg. We compare the execution of these motions using state feedback from (i) an external motion tracking system and (ii) an internal state estimator based on inertial measurement unit (IMU), forward kinematics, and contact sensing. It is shown that state-of-the-art motion capture systems can be successfully used in the high-frequency feedback control loop of humanoid robots, providing an alternative in cases where state estimation is not reliable.

In this paper, a new hybrid kinematic/dynamic control scheme for humanoid robots is proposed. Its basic idea lies in the tracking of several values in both operational and joint spaces. These values include (i) the relative pose of the robot's feet, (ii) the position of the center of mass, (iii) the body's orientation, and (iv) the admissible range of variation of the joints. A zero-moment point (ZMP)-based dynamic feedback is included in the proposed scheme to improve the stability of dynamic motions. The proposed stabilizer is based on a spherical projection of a nonlinear PID regulation control law. Through the proposed study, it is shown that these objectives allow us to produce smooth dynamically stable whole-body motions. The effectiveness and robustness of the proposed control scheme are demonstrated through four real-time experimental scenarios conducted on the HOAP-3 humanoid robot. [ABSTRACT FROM AUTHOR]

The task of performing locomotion and manipulation simultaneously poses several scientific challenges, such as how to deal with the coupling effects between them and how to cope with unknown disturbances introduced by manipulation. This paper presents an inverse dynamics-based whole-body controller for a torque-controlled quadrupedal manipulator capable of performing locomotion while executing manipulation tasks. Unlike existing methods that deal with locomotion and manipulation separately, the proposed controller can handle them uniformly, which can take into account the coupling effects between the base, limbs and manipulated object. The controller tracks the desired task–space motion references based on a hierarchical optimization algorithm, given a set of hierarchies that define strict priorities and the importance of weighting each task within a hierarchy. The simulation results show the robot is able to follow multiple task–space motion reference trajectories with reasonable deviation, which proved the effectiveness of the proposed controller.

For humanoid robots, maintaining a dynamic balance against uncertain disturbance is crucial, and this function can be achieved by coordinating the whole body to perform multiple tasks simultaneously. Researchers generally accept hierarchical whole-body control (WBC) to address this function. Although experts can build feasible hierarchies using prior knowledge, real-time WBC is still challenging because it often requires a quadratic program with multiple inequality constraints. In addition, the torque tracking performance of the WBC algorithm will be affected by uncertain factors such as joint friction for a large transmission ratio proprioceptive-actuated robot. Therefore, the balance control of physical robots requires a systematic solution. In this study, a robot control system with high computing power and real-time communication ability, UBTMaster, is implemented to achieve a reduced WBC in real time. Based on these, a whole-body control scheme based on task priority for the dynamic balance of humanoid robots is implemented. After realizing the joint friction model identification, finally, a variety of balancing scenarios are tested on the Walker3 humanoid robot driven by the proprioceptive actuators to verify the effectiveness of the proposed scheme. The Walker3 robot exhibits excellent balance when multiple external disturbances occur simultaneously. For example, the two feet of the robot are subjected to tilt and displacement perturbations, respectively, while the torso is subjected to external shocks simultaneously. The experimental results show that the dynamic balance of the robot under multiple external disturbances can be achieved by using strictly hierarchical real-time WBC with a systematic design.

The studies on quadruped robots equipped with arms are still rare at the moment. The interaction between the arm and the quadrupedal platform needs to be handled by whole-body controllers. This paper presents an optimization-based dynamic whole-body controller to solve the problem of when the robot stands still for manipulation. In order to reduce the strong interaction when the robot is trotting, we keep using the whole-body controller to handle locomotion control and resort to joint PD controllers for the arm’s manipulation coupled with the mobile base on the kinematic level. Simulation results validate the expected locomotion and manipulation functionalities in both manipulation mode and loco-manipulation mode. The proposed control strategies are able to use the redundancy to perform multiple tasks in a dynamic system as such with 24 degrees of freedom.

This paper presents a novel omnidirectional walking pattern generator for bipedal locomotion combining two structurally different approaches based on the virtual constraints and the preview control theories to generate a flexible gait that can be modified on-line. The proposed strategy synchronizes the displacement of the robot along the two planes of walking: the zero moment point based preview control is responsible for the lateral component of the gait, while the sagittal motion is generated by a more dynamical approach based on virtual constraints. The resulting algorithm is characterized by a low computational complexity and high flexibility, requisite for a successful deployment to humanoid robots operating in real world scenarios. This solution is motivated by observations in biomechanics showing how during a nominal gait the dynamic motion of the human walk is mainly generated along the sagittal plane. We describe the implementation of the algorithm and we detail the strategy chosen to enable omnidirectionality and on-line gait tuning. Finally, we validate our strategy through simulation experiments using the COMAN + platform, an adult size humanoid robot developed at Istituto Italiano di Tecnologia. Finally, the hybrid walking pattern generator is implemented on real hardware, demonstrating promising results: the WPG trajectories results in open-loop stable walking in the absence of external disturbances.