An overview of collaborative robotic manipulation in multi-robot systems

Feng, Zhi; Hu, Guoqiang; Sun, Yiping; Soon, Jeffrey Bo Woon

doi:10.1016/j.arcontrol.2020.02.002

Cited by 106 publications

(41 citation statements)

References 75 publications

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“…It delivered optimal performance and proposed using "Mixed Integer Linear Programming (MILP)". A recent survey on collaborative robotic manipulation for robot manipulators, mobile robots, and mobile manipulators is presented in [240].…”

Section: Object Transport and Manipulationmentioning

confidence: 99%

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Verma

Ranga

2021

J Intell Robot Syst

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Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

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Section: Object Transport and Manipulationmentioning

confidence: 99%

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Verma

Ranga

2021

J Intell Robot Syst

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“…where T next L is the next moment pose of the main arm endeffector, T cur L is the current moment pose of the main arm end-effector, and dT L is transformed from the differential motion (dq L ) of the main arm joint. Here, dq L can be calculated from equation (5), where _ q L is the joint velocity planned by the main arm, Δt is the instantaneous time difference, and acceleration has little effect on the joint control feedback. In order to improve the reliability of T next L and ignore the influence of instantaneous joint angular acceleration, an appropriate Δt can be set to reduce the influence of acceleration.…”

Section: Speed Constraintmentioning

confidence: 99%

“…us, the coordinated control between two manipulators and adaptability of the environment is very important. e trajectory optimization and coordinated control between the dual-arm have been focused widely [4][5][6]. e coordinated operation consists of two parts: one is the coordinated trajectory planning, and the other is the coordinated motion control method.…”

Section: Introductionmentioning

confidence: 99%

Optimization Control of Cooperative Trajectory towards Dual Arms Based on Time-Varying Constrained Output State

Zhang

Zha

et al. 2021

Complexity

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Aiming at the impact and disturbance of dual-arm robots in the process of coordinated transportation, a dual-arm cooperative trajectory optimization control based on time-varying constrained output state is proposed. According to the constraint relationship of the end-effector trajectory of the dual-arm coordinated transportation, the joint space trajectory mathematical model of the dual-arm coordinated transportation was established by using the master-slave construction method. Based on the time impact optimization index of joint trajectory, a multiobjective nonlinear equation is established. Using random probability distribution to extract the interpolation features of nonuniform quintic B-spline trajectory, the feature optimization target is selected, and the Newton numerical algorithm is used for iterative optimization. At the same time, it is combined with an elite retention genetic algorithm to further optimize the target. Based on the disturbance and tracking problem, a PD control method based on time-varying constrained output state is proposed, and the control law is designed. Its convergence is verified by establishing the Lyapunov function equation and asymmetric term. The trajectory optimization results show that the proposed trajectory optimization method can increase the individual diversity and enhance the individual local optimization, thus avoiding the premature impact of the elite retention genetic algorithm. Finally, the proposed control method is simulated on the platform of Gazebo; compared with the traditional PD control method, the results show that the proposed control algorithm has high robustness, and the rationality of the coordinated trajectory control method is verified by the double-arm handling experiment.

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“…Thanks to these features, in the last decade, the MRS architecture has emerged as an effective technology in military, civil, and industrial contexts, where it is employed in a wide range of application fields from the more traditional surveillance, monitoring, and tracking tasks with both fixed and mobile robotic agents [3][4][5], to the modern physical interaction tasks involving loads cooperative grasping, transportation, and manipulation [6,7]. Nonetheless, the MRS scenario yields also new methodological challenges that need to be addressed and solved.…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, architectural issues related to distributed or hierarchical strategies in controlling the MRS have to be considered [8][9][10][11], with an impact on both the information sharing and the control interplay among robots, in particular when the physical robot-to-robot interaction is required as in transportation/manipulation tasks. In this context, current MRS research effort is focused on the development of learning and optimal decision making algorithms to guarantee general improvements on efficiency, scalability, adaptivity, and robustness in presence of partial information availability and dynamic environments [7,12,13].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control for Cooperative Transportation with Feasibility-Aware Policy

et al. 2021

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The transportation of large payloads can be made possible with Multi-Robot Systems (MRS) implementing cooperative strategies. In this work, we focus on the coordinated MRS trajectory planning task exploiting a Model Predictive Control (MPC) framework addressing both the acting robots and the transported load. In this context, the main challenge is the possible occurrence of a temporary mismatch among agents’ actions with consequent formation errors that can cause severe damage to the carried load. To mitigate this risk, the coordination scheme may leverage a leader–follower approach, in which a hierarchical strategy is in place to trade-off between the task accomplishment and the dynamics and environment constraints. Nonetheless, particularly in narrow spaces or cluttered environments, the leader’s optimal choice may lead to trajectories that are infeasible for the follower and the load. To this aim, we propose a feasibility-aware leader–follower strategy, where the leader computes a reference trajectory, and the follower accounts for its own and the load constraints; moreover, the follower is able to communicate the trajectory infeasibility to the leader, which reacts by temporarily switching to a conservative policy. The consistent MRS co-design is allowed by the MPC formulation, for both the leader and the follower: here, the prediction capability of MPC is key to guarantee a correct and efficient execution of the leader–follower coordinated action. The approach is formally stated and discussed, and a numerical campaign is conducted to validate and assess the proposed scheme, with respect to different scenarios with growing complexity.

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An overview of collaborative robotic manipulation in multi-robot systems

Cited by 106 publications

References 75 publications

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Optimization Control of Cooperative Trajectory towards Dual Arms Based on Time-Varying Constrained Output State

Model Predictive Control for Cooperative Transportation with Feasibility-Aware Policy

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