Scalable Cooperative Transport of Cable-Suspended Loads With UAVs Using Distributed Trajectory Optimization

Jackson, Brian E.; Howell, Taylor A.; Shah, Kunal; Schwager, Mac; Manchester, Zachary

doi:10.1109/lra.2020.2975956

Cited by 61 publications

(29 citation statements)

References 27 publications

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“…Many works have tackled the 3D motion coordination and collision avoidance problem for UAV swarms through a hierarchical approach with trajectory planning formulated as an optimization problem, similar to what was discussed earlier for single-UAV systems. Different centralized, decentralized and distributed approaches can be seen in the literature considering homogeneous and heterogeneous multi-UAV systems in addition to considering multi-vehicle systems working with aerial and ground vehicles; for example, see [ 79 , 317 , 318 , 319 , 320 , 321 , 322 , 323 , 324 , 325 , 326 , 327 ].…”

Section: Research Challengesmentioning

confidence: 99%

Towards Fully Autonomous UAVs: A Survey

Elmokadem

Savkin

2021

Sensors

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Unmanned Aerial Vehicles have undergone rapid developments in recent decades. This has made them very popular for various military and civilian applications allowing us to reach places that were previously hard to reach in addition to saving time and lives. A highly desirable direction when developing unmanned aerial vehicles is towards achieving fully autonomous missions and performing their dedicated tasks with minimum human interaction. Thus, this paper provides a survey of some of the recent developments in the field of unmanned aerial vehicles related to safe autonomous navigation, which is a very critical component in the whole system. A great part of this paper focus on advanced methods capable of producing three-dimensional avoidance maneuvers and safe trajectories. Research challenges related to unmanned aerial vehicle development are also highlighted.

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Section: Research Challengesmentioning

confidence: 99%

Towards Fully Autonomous UAVs: A Survey

Elmokadem

Savkin

2021

Sensors

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“…Other authors have studied aerial towing (manipulation via cables) in works such as [22]- [24]. The authors in [25] use a distributed optimization algorithm to perform collaborative manipulation of a cable-suspended load.…”

Section: Prior Workmentioning

confidence: 99%

Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies

2021

Self Cite

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In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in SE(3). The robots do not know the mass or friction properties of the object, or where they are attached to the object. They can, however, access a common state measurement, either from one robot broadcasting its measurements to the team, or by all robots communicating and averaging their state measurements to estimate the state of their centroid. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller's behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller's performance using numerical simulations of a manipulation task in 3D, as well as hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodeled effects, such as discretization errors and complex frictional interactions.

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“…The nonlinear MPC-based approach in [10] reduces the need for linearized constraints. The distributed MPC approach in [11] incorporates the residual balancing method from [12], while [13] utilizes distributed trajectory optimization based on a decomposition technique that avoids communication between agents until convergence.…”

Section: Introductionmentioning

confidence: 99%

“…The distributed MPC approach from [11] does not utilize the adaptive penalty parameter to improve the system safety. The distributed trajectory optimization technique from [13] requires performing the optimization steps until convergence and the decomposition method has no convergence proofs, making the method unsuitable for autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM

An,

Giordano,

Liu

2021

Preprint

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Decentralized conflict resolution for autonomous vehicles is needed in many places where a centralized method is not feasible, e.g., parking lots, rural roads, merge lanes, etc. However, existing methods generally do not fully utilize optimization in decentralized conflict resolution. We propose a decentralized conflict resolution method for autonomous vehicles based on a novel extension to the Alternating Directions Method of Multipliers (ADMM), called Online Adaptive ADMM (OA-ADMM), and on Model Predictive Control (MPC). OA-ADMM is tailored to online systems, where fast and adaptive real-time optimization is crucial, and allows the use of safety information about the physical system to improve safety in real-time control. We prove convergence in the static case and give requirements for online convergence. Combining OA-ADMM and MPC allows for robust decentralized motion planning and control that seamlessly integrates decentralized conflict resolution. The effectiveness of our proposed method is shown through simulations in CARLA, an open-source vehicle simulator, resulting in a reduction of 47.93% in mean added delay compared with the next best method.

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Scalable Cooperative Transport of Cable-Suspended Loads With UAVs Using Distributed Trajectory Optimization

Cited by 61 publications

References 27 publications

Towards Fully Autonomous UAVs: A Survey

Towards Fully Autonomous UAVs: A Survey

Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies

Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM

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