Multi-robot teams can achieve more dexterous, complex and heavier payload tasks than a single robot, yet effective collaboration is required. Multi-robot collaboration is extremely challenging due to the different kinematic and dynamics capabilities of the robots, the limited communication between them, and the uncertainty of the system parameters. In this letter, a Decentralized Ability-Aware Adaptive Control (DA ³ C) is proposed to address these challenges based on two key features. Firstly, the common manipulation task is represented by the proposed nominal task ellipsoid, which is used to maximize each robot's force capability online via optimizing its configuration. Secondly, a decentralized adaptive controller is designed to be Lyapunov stable in spite of heterogeneous actuation constraints of the robots and uncertain physical parameters of the object and environment. In the proposed framework, decentralized coordination and load distribution between the robots is achieved without communication, while only the control deficiency is broadcast if any of the robots reaches its force limits. In this case, the object's reference trajectory is modified in a decentralized manner to guarantee stable interaction. Finally, we perform several numerical and physical simulations to analyse and verify the proposed method with heterogeneous multi-robot teams in collaborative manipulation tasks.

The cable-driven redundant manipulator (CDRM) has a light slender and highly dexterous body, which is especially suitable for fine manipulation in confined spaces or unstructured environments. However, for dexterous manipulation and physical interaction with its surrounding environment, the force/torque sensor is indispensable, which will significantly increase the mass, dimension, and cost of the whole CDRM system. In this article, a force-sensing algorithm and compliance control framework for CDRM without a six-axis force/torque sensor are proposed. First, we design a modular cable-driven manipulator with two-level internal sensors, i.e., joint encoders and cable tension sensors. The multispace kinetic model is derived to establish the mapping between motor, cable, joint, and end-effector states. At the same time, we build a recursive dynamics model that takes the cables’ tension, cable–hole friction, links’ gravity, and end-effector forces into account. Then, the indirect force-sensing algorithm of the end-effector is proposed by combining the kinematic and dynamic equations and internal sensor information. Furthermore, a compliance controller based on indirect force sensing is designed. Finally, typical experiments are carried out based on the CDRM. Experiment results indicate that the force-sensing accuracy exceeds 95%, whereas the compliance controller demonstrates outstanding compliant behavior in human–robot interaction tasks.

In this paper, we review the state of the art in hyper-redundant manipulators for applications in confined space such as on-orbit services. With their multiple degrees of freedom (DOF) and slender links, hyper-redundant manipulators can offer superior dexterity and excellent operability. They can traverse freely, manipulate objects flexibly, and conform to curvilinear paths accurately in confined spaces. The by-design separation of the mechanical and electrical parts in these manipulators also offers inherent structural compliance and miniaturization. Due to the elastic characteristics of driving cables, hyper-redundant manipulators have both stiffness and flexibility. In this paper, the overviews of the current state of the art in this field are provided from the perspectives of both typical applications and key technologies. We detailed the relevant studies on the configuration, obstacle avoidance, path planning, and control technologies for hyper-redundant manipulators and highlight the use of these studies in the development of practical applications. Furthermore, we propose several aspects that need to be further studied, namely efficient inverse kinematics solution, strong coupled dynamics modeling, variable stiffness control, and multi-objective trajectory optimization. Breakthroughs in these areas will provide valuable solutions for complex path planning and control of hyper-redundant manipulators.

This paper investigates one of the most challenging tasks in dynamic manipulation-catching large-momentum moving objects. Beyond the realm of quasi-static manipulation, dealing with highly dynamic objects can significantly improve the robot's capability of interacting with its surrounding environment. Yet, the inevitable motion mismatch between the fast moving object and the approaching robot will result in large impulsive forces, which lead to the unstable contacts and irreversible damage to both the object and the robot. To address the above problems, we propose an online optimization framework to: 1) estimate and predict the linear and angular motion of the object; 2) search and select the optimal contact locations across every surface of the object to mitigate impact through sequential quadratic programming (SQP); 3) simultaneously optimize the end-effector motion, stiffness, and contact force for both robots using multi-mode trajectory optimization (MMTO); and 4) realise the impact-aware catching motion on the compliant robotic system based on indirect force controller. We validate the impulse distribution, contact selection, and impactaware MMTO algorithms in simulation and demonstrate the benefits of the proposed framework in real-world experiments including catching large-momentum moving objects with welldefined motion, constrained motion and free-flying motion.