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In this paper we consider the problem of controlling multiple robots manipulating and transporting a payload in three dimensions via cables. We develop robot configurations that ensure static equilibrium of the payload at a desired pose while respecting constraints on the tension and provide analysis of payload stability for these configurations. We demonstrate our methods on a team of aerial robots via simulation and experimentation.

The integration of robotic systems in industrial, logistics, and service environments has accelerated due to rising demands for automation and efficiency. While traditional robots excel in repetitive tasks within structured settings, they face limitations with irregular objects and dynamic conditions. This has led to the emergence of mobile robots as a solution for flexible and scalable operations. This paper reviews the development of robotic systems designed for co-manipulation and transportation of objects of varying shapes and sizes. Emphasis is placed on adaptive gripping technologies, modular designs, and hybrid control architectures combining centralized and distributed coordination. Equipped with advanced sensors and real-time decision-making algorithms, these robots address key challenges in unstructured environments. The study outlines their benefits for applications in logistics, manufacturing, and rescue, while bridging gaps in existing research on irregular load handling.

This paper proposes a method for autonomous cooperation among Unmanned Mobile Manipulators (UMMs) for load transportation tasks. In the proposed approach, the leader UMM utilizes force/torque measurements and carries fiducial markers arranged in a truncated rhombicuboctahedron configuration. The follower UMMs determine their pose relative to the leader using visual information while also employing their own force/torque sensors. The developed framework is fully distributed and operates without requiring any communication between the leader and followers. Safe operation is ensured through the integration of Control Barrier Functions (CBFs) in the UMM control loops, which maintain strictly bounded internal forces during collaboration. Extensive real-world experiments validate the proposed method, demonstrating its performance and generalizability for cooperative manipulation with aerial manipulators and aerial-ground mobile manipulator teams.

Flexible and safe object handling in modern industrial environments increasingly relies on mobile robotic systems capable of both dexterous manipulation and adaptive motion. However, when wheeled mobile manipulators (WMMs) operate under heavy or dynamically varying loads, challenges arise in maintaining sufficient force exertion capability and achieving stable coordination, particularly during cooperative transportation. In this paper, we present a unified framework to address these challenges with three main contributions. A quadratic-programming-based redundancy resolution scheme incorporating a load-capacity maximization metric is developed to explicitly enhance the force exertion capability of the system under heavy loads. A variable-admittance cooperative control strategy for dual-WMM transport is proposed to ensure synchronized motion and adaptive force regulation during collaborative manipulation. In addition, a unified framework that integrates configuration optimization with compliant cooperative control is established, enabling strict constraint enforcement, improved load capacity, and robust coordination between the two WMMs. Extensive simulations demonstrate the effectiveness of the proposed methods in improving load-handling performance and ensuring coordinated, compliant cooperative manipulation.

The cooperative manipulator group can accomplish complex and heavy payload tasks of object manipulation and transportation compared to a single manipulator. Effective coordination is crucial for cooperative task accomplishments. Multi-manipulator task distribution is highly complex because of the varying dynamic capabilities of the manipulators. We have introduced a novel fastest technique to quantify the dynamic task capability of the cooperative manipulator by scalar quantity and allocate the task accordingly. The scalar quantity determines the capability of applying an external wrench by end effector (EE) in line with the required wrench at the center of mass of the manipulating object. This quantity helps to diminish tracking errors in object manipulations or transportation and actuator saturation avoidance. The task distribution among the members is in proportion to their computed dynamic capability to ensure equal priority to the individual manipulators. The proposed task distribution formulation ensures the minimum magnitude of wrench interaction at the grasp point and the minimum internal wrench build-up in the object. Several physical simulation results assure trajectory tracking performance with the proposed task capability metric. The same metric aids in identifying the least capable manipulator, rearranging members for better performance, and deciding the required number of manipulators in the manipulator group.

This review paper focuses on quadrotor- and multirotor-based cooperative aerial manipulation. Emphasis is first given to comparing and evaluating prototype systems that have been implemented and tested in real-time in diverse application environments. The underlying modeling and control approaches are also discussed and compared. The outcome of this review allows for understanding the motivation and rationale to develop such systems, their applicability and implementability in diverse applications and also challenges that need to be addressed and overcome. Moreover, this paper provides a guide to develop the next generation of prototype systems based on preferred characteristics, functionality, operability, and application domain.

Human teams are able to easily perform collaborative manipulation tasks. However, simultaneously manipulating a large extended object for a robot and human is a difficult task due to the inherent ambiguity in the desired motion. Our approach in this paper is to leverage data from human-human dyad experiments to determine motion intent for a physical human-robot co-manipulation task. We do this by showing that the human-human dyad data exhibits distinct torque triggers for a lateral movement. As an alternative intent estimation method, we also develop a deep neural network based on motion data from human-human trials to predict future trajectories based on past object motion. We then show how force and motion data can be used to determine robot control in a human-robot dyad. Finally, we compare human-human dyad performance to the performance of two controllers that we developed for human-robot co-manipulation. We evaluate these controllers in three-degree-of-freedom planar motion where determining if the task involves rotation or translation is ambiguous.

The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and creating computational bottlenecks. This paper proposes a novel Federated Learning (FL) framework for distributed multi-robotic arm trajectory optimization. Our method enables collaborative learning where robots train a shared model locally and only exchange gradient updates, preserving data privacy. The framework integrates an adaptive Rapidly exploring Random Tree (RRT) algorithm enhanced with a dynamic pruning strategy to reduce computational overhead and ensure collision-free paths. Real-time synchronization is achieved via EtherCAT, ensuring precise coordination. Experimental results demonstrate that our approach achieves a 17% reduction in average path length, a 22% decrease in collision rate, and a 31% improvement in planning speed compared to a centralized RRT baseline, while reducing inter-robot communication overhead by 45%. This work provides a scalable and efficient solution for collaborative manipulation in applications ranging from assembly lines to warehouse automation.

This paper addresses the problem of deriving well-defined timed abstractions for the decentralized cooperative manipulation of a single object by N robotic agents. In particular, we propose a distributed model-free control protocol for the trajectory tracking of the cooperatively manipulated object without necessitating feedback of the contact forces/torques or inter-agent communication. Certain prespecified performance functions determine the transient and steady state of the coupled object-agents system. The latter, along with a region partition of the workspace that depends on the physical volume of the object and the agents, allows us to define timed transitions for the coupled system among the derived workspace regions. Therefore, we abstract its motion as a finite transition system and, by employing standard automata-based methodologies, we define high level complex tasks for the object that can be encoded by timed temporal logics. In addition, we use load sharing coefficients to represent potential differences in power capabilities among the agents. Finally, realistic simulation studies verify the validity of the proposed scheme.

Dual-arm robot manipulation is applicable to many domains, such as industrial, medical, and home service scenes. Learning from demonstrations is a highly effective paradigm for robotic learning, where a robot learns from human actions directly and can be used autonomously for new tasks, avoiding the complicated analytical calculation for motion programming. However, the learned skills are not easy to generalize to new cases where special constraints such as varying relative distance limitation of robotic end effectors for human-like cooperative manipulations exist. In this paper, we propose a dynamic movement primitives (DMPs) based skills learning framework for redundant dual-arm robots. The method, with a coupling acceleration term to the DMPs function, is inspired by the transient performance control of Barrier Lyapunov Functions. The additional coupling acceleration term is calculated based on the constant joint distance and varying relative distance limitations of end effectors for object-approaching actions. In addition, we integrate the generated actions in joint space and the solution for a redundant dual-arm robot to complete a human-like manipulation. Simulations undertaken in Matlab and Gazebo environments certify the effectiveness of the proposed method.