The construction of large structures is one of the main development trends of the space exploration in the future, such as large space stations, large space solar power stations, and large space telescopes. It is one of important development tendency, which aims to make full use of space robots to assemble space structures autonomously in the aerospace industry. Considering that on-orbit assembly is an effective method to solve the problem of construction of large-scale spatial structures, it is necessary to motivate and facilitate the research of space robotics technologies for on-orbit assembly. Therefore, in this paper, the development status of space robot technology and the relevant space robot on-orbit assembly technology in recent decades are summarized. First, based on the space robot motion planning and assembly sequence planning, the development of space robot planning algorithms is introduced. For space robot assembly task, the space robot assembly method is summarized. From the control point of view, how to solve the vibration suppression and compliant assembly of on-orbit assembly is reviewed, which provides a reference for the autonomous intelligent assembly of space robots for large-scale structures in space. In order to simulate the space assembly scene on the ground, this paper introduces the development of ground verification experiments and provides ideas for the effective verification of space on-orbit assembly technology. In summary, though some of these problems have been satisfactorily solved in the past research, further research is still necessary in the future. Finally, it looks forward to the future research direction of space machine on-orbit assembly.

Closed-chain manipulation occurs when several robot arms perform tasks in cooperation. It is complex to control a dual-arm system because it requires flexible and adaptable operation ability to realize closed-chain manipulation. In this study, a deep reinforcement learning (DRL) framework based on actor-critic algorithm is proposed to drive the closed-chain manipulation of a dual-arm robotic system. The proposed framework is designed to train dual robot arms to transport a large object cooperatively. In order to sustain strict constraints of closed-chain manipulation, the actor part of the proposed framework is designed in a leader-follower mode. The leader part consists of a policy trained from the DRL algorithm and works on the leader arm. The follower part consists of an inverse kinematics solver based on Damped Least Squares (DLS) and works on the follower arm. Two experiments are designed to prove the task adaptability, one of which is manipulating an object to a random pose within a defined range, the other is manipulating a delicate structural object within a narrow space Note to Practitioners —In common industrial manipulation scenarios, there are requirements to employ robotic arms to transport a large object relative to the robotic arm, e.g., moving a payload onto a loader and assembling big craft parts. It is a cost-effective way to use a dual-arm system to extend the loading capacity of robotic arms while preserving the flexibility of manipulation. Moreover, the dual-arm system is expected to manipulate different objects without complicated reprogramming, especially in small batch production scenarios. This study proposes a task-adaptive deep reinforcement learning framework for dual-arm robot manipulation. The task adaptability includes two specific aspects, one being adaptability in targeting the pose, such as manipulating an object to a random pose within a specified range. The other is the adaptivity on the task prerequisites such as manipulating a delicate structural object within a narrow space. For future research, the dual-arm system may autonomously plan the grab positions, and additional investigations should address more common scenarios involving various object shapes.

Leveraging explicit communication and cooperation of multiple robots brings about multiple advantages in the solution of tasks with autonomous robotic agents. For this reason, to the end of transporting polygonal objects with a group of mobile robots, the aim of this article is to develop a fully distributed decision-making and control scheme that lets the robots cooperate as equals, without any kind of central control instance. Apart from coordinating the motions of the robots in a distributed manner, challenges include the self-reliant determination of configurations of the robots around the object, as well as the interrobot negotiation on which robot takes which role or position in the transportation process. Based on some assumptions on the properties of the robots and the considered types of objects, all major aspects of the control scheme are explained in detail, with the most crucial parts making use of optimization-based schemes. This enables unprecedented flexibility by letting the robots automatically adapt to a wide range of scenarios, with different numbers of robots cooperating, and accommodating intricate object shapes. A simulation-based analysis reveals some of the key properties of the proposed scheme, whereas hardware-in-the-loop experiments show the practical applicability with inexpensive hardware.

Multi-robot task allocation (MRTA) is a challenging bi-level problem in the multi-robot cooperative systems (MRCSs) and offers an effective method for addressing complex tasks. However, dynamic /uncertain environments can easily invalidate original schemes in practical MRTA decision-makings. Further, a nested structure in MRTA problems makes computational expensive. Therefore, the two main tasks are (1) finding a sufficient number of equivalent schemes for MRTA problems to adapt to task environments; and (2) improving algorithm search efficiency in bi-level optimization problems. In this study, a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning (DRL) and large neighborhood search (LNS), called MMOEA-DL, is proposed to solve MRTA problems. In the MMOEA-DL, the task allocation problem, which is considered as the upper-level optimization problem, is solved using an improved multimodal multi-objective evolutionary algorithm (MMOEA). The travelling salesman problem (TSP) regarded as the lower-level optimization problem is addressed via end-to-end method (i.e., DRL) and LNS. By leveraging the end-to-end method to obtain the results of the lower-level optimization, the bi-level optimization problem is effectively transformed into a single-level optimization problem. To demonstrate the performance of the proposed algorithm, 16 MRTA simulation scenarios and two actual MRTA scenarios with evenly and unevenly distributed task points are introduced in the present study. The simulation results verify that the MMOEA-DL not only provides decision-makers with expanded equivalent optimal schemes to address dynamic environments or unforeseen circumstances, but also offers a novel approach to solve the multimodal multi-objective bi-level optimization problem while saving computational costs.

Swarm robots search for multiple targets in collaboration in unknown environments has been addressed in this paper. An improved grouping strategy based on constriction factors Particle Swarm Optimization is proposed. Robots are grouped under this strategy after several iterations of stochastic movements, which considers the influence range of targets and environmental information they have sensed. The group structure may change dynamically and each group focuses on searching one target. All targets are supposed to be found finally. Obstacle avoidance is considered during the search process. Simulation compared with previous method demonstrates the adaptability, accuracy, and efficiency of the proposed strategy in multiple targets searching.