Decentralized Adaptive Control for Collaborative Manipulation

Culbertson, Preston; Schwager, Mac

doi:10.1109/icra.2018.8461263

Cited by 59 publications

(23 citation statements)

References 43 publications

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“…Demonstrations have typically relied on centralized approaches, based on high bandwidth connectivity, that are unsuitable for SAR. Recent work on fully decentralized methods for ground robot cooperative manipulation [82] is promising. Multi-robot motion planning algorithms which take into account laden payloads [83] will be necessary.…”

Section: Balancing Cost and Functionalitymentioning

confidence: 99%

Multi-Agent Systems for Search and Rescue Applications

Drew

2021

Curr Robot Rep

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Purpose of Review The goal of this review is to evaluate the current status of multi-robot systems in the context of search and rescue. This includes an investigation of their current use in the field, what major technical challenge areas currently preclude more widespread use, and which key topics will drive future development and adoption. Recent Findings Work blending machine learning with classical control techniques is driving progress in perception-driven autonomy, decentralized multi-robot coordination, and human-robot interaction, among others. Ad hoc mesh networking has achieved reliability suitable for safety-critical applications and may be a partial solution for communication. New modular and multimodal platforms may overcome mobility limitations without significantly increasing cost. Summary Multi-agent systems are not currently ready for deployment in search and rescue applications; however, progress is being made in a number of critical domains. As the field matures, research should focus on realistic evaluations of constituent technologies, and on confronting the challenges of simulation-to-reality transfer, algorithmic bias in autonomous agents that rely on machine learning, and novelty-versus-reliability incentive mismatch Keywords Urban search and rescue robots . Disaster robotics . Multi-robot search and rescue . Swarm search and rescue . Multi-agent systems; Field robotics This article is part of the Topical Collection on Defense, Military, and Surveillance Robotics

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Section: Balancing Cost and Functionalitymentioning

confidence: 99%

Multi-Agent Systems for Search and Rescue Applications

Drew

2021

Curr Robot Rep

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“…For example, a multi-robot team like the one shown in Fig. 1 is more dexterous and robust in heavy and large object manipulation tasks [1] than a single robot. Also, in human-robot collaboration scenarios [2], the human's input can improve the intelligence and adaptability of the team.…”

Section: Introductionmentioning

confidence: 99%

“…A distributed impedance controller has also been used for collaborative manipulation with event-triggered communication [24]. Recently, a decentralized adaptive controller [1] for multiple collaborative mobile robots was introduced. This controller can track the reference velocity trajectory without a priori knowledge of the agent's position and payload properties.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Ability-Aware Adaptive Control for Multi-Robot Collaborative Manipulation

Yan

Stouraitis

Vijayakumar

2021

IEEE Robot. Autom. Lett.

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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.

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“…There are many OOS capabilities that spacecraft swarms may enable, such as autonomous on-orbit assembly [6,7] or collaborative manipulation [8]. Yet, in this paper, we focus on the prospect of using a swarm of small-spacecraft for on-orbit inspection, an essential first step towards enabling future multi-spacecraft OOS capabilities.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Motion Planning for On-Orbit Satellite Inspection using a Swarm of Small-Spacecraft

Bernhard

Choi

Rahmani

et al. 2020

2020 IEEE Aerospace Conference

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This paper addresses the problem of how to plan optimal motion for a swarm of on-orbit servicing (OOS) smallspacecraft remotely inspecting a non-cooperative client spacecraft in Earth orbit. With the goal being to maximize the information gathered from the coordinated inspection, we present an integrated motion planning methodology that is a) fuel-efficient to ensure extended operation time and b) computationallytractable to make possible on-board re-planning for improved exploration. Our method is decoupled into first offline selection of optimal orbits, followed by online coordinated attitude planning. In the orbit selection stage, we numerically evaluate the upper and lower bounds of the information gain for a discretized set of passive relative orbits (PRO). The algorithm then sequentially assigns orbits to each spacecraft using greedy heuristics. For the attitude planning stage, we propose a dynamic programming (DP) based attitude planner capable of addressing vehicle and sensor constraints such as attitude control system specifications, sensor field of view, sensing duration, and sensing angle. Finally, we validate the performance of the proposed algorithms through simulation of a design reference mission involving 3U CubeSats inspecting a satellite in low Earth orbit.

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Decentralized Adaptive Control for Collaborative Manipulation

Cited by 59 publications

References 43 publications

Multi-Agent Systems for Search and Rescue Applications

Multi-Agent Systems for Search and Rescue Applications

Decentralized Ability-Aware Adaptive Control for Multi-Robot Collaborative Manipulation

Coordinated Motion Planning for On-Orbit Satellite Inspection using a Swarm of Small-Spacecraft

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