Distributed Nonlinear Trajectory Optimization for Multi-Robot Motion Planning

Ferranti, Laura; Lyons, Lorenzo; Negenborn, Rudy R.; Keviczky, Tamás; Alonso–Mora, Javier

doi:10.1109/tcst.2022.3211130

Cited by 16 publications

(6 citation statements)

References 63 publications

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“…The different approaches lead to different methods for analyzing distributed computations. Both approaches may be combined, e.g., the works in [35], [36] implement cooperative DMPC using ADMM methods. Nevertheless, this paper focus on DMPC rather than distributed optimization.…”

Section: A Motivationmentioning

confidence: 99%

Coordinated Cooperative Distributed Decision-Making using Synchronization of Local Plans

Kloock¹,

Alrifaee²

2023

Preprint

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<p>Centralized decision-making for a Networked Control System (NCS) suffers from a high computational burden on the planning agent. Distributed agents, which compute cooperative decision-making, increase computational performance. In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans are inconsistent with the local plans of other agents. This inconsistency leads to the infeasibility of plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making. The algorithm enables parallel decision-making and achieves feasible decisions. The algorithm consists of two iterative steps: local planning and global synchronization. In the local planning step, the agents parallelly compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using global synchronization. The globally synchronized plans act as reference decisions for the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The parallel algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that globally feasible decisions are achieved.</p>

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Section: A Motivationmentioning

confidence: 99%

Coordinated Cooperative Distributed Decision-Making using Synchronization of Local Plans

Kloock¹,

Alrifaee²

2023

Preprint

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“…Solutions based on assigning priorities (sequential solutions) or synchronous re-planning are adopted to avoid this problem, but in the former case it introduces a hierarchy among the agents, in the latter is required a global clock synchronization. The most popular approaches rely on model predictive control (MPC) [17]- [23] elastic bands [24], buffered Voronoi cells [25]- [27], legible motion [28], [29], linear spatial separations [30]. Other interesting and recent solutions are provided by [31]- [37], all of them have to rely on a communication network.…”

Section: A Related Workmentioning

confidence: 99%

“…Variation of the maximum agents' occupancy radius allowed to avoid collisions, with the parameters c and C in (10). strategy e.g., [23], [24], [25]. This solution is able to guarantee safety (also in the case of generic κ-clustered configurations) at the price of deviating from the Lissajous path or from the desired velocity.…”

Section: A Preserving Collision Avoidancementioning

confidence: 99%

Time-Inverted Kuramoto Model Meets Lissajous Curves: Multi-Robot Persistent Monitoring and Target Detection

Boldrer

Lyons

Palopoli

et al. 2023

IEEE Robot. Autom. Lett.

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This paper presents a distributed rule-based Lloyd algorithm (RBL) for multi-robot motion planning and control. The main limitations of the basic Loyd-based algorithm (LB) concern deadlock issues and the failure to address dynamic constraints effectively. Our contribution is twofold. First, we show how RBL is able to provide safety and convergence to the goal region without relying on communication between robots, nor neighbors control inputs, nor synchronization between the robots. We considered both case of holonomic and non-holonomic robots with control inputs saturation. Second, we show that the Lloyd-based algorithm (without rules) can be successfully used as a safety layer for learning-based approaches, leading to nonnegligible benefits. We further prove the soundness, reliability, and scalability of RBL through extensive simulations, an updated comparison with the state of the art, and experimental validations on small-scale car-like robots.

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“…The vehicles are based on a platform presented to replicate the behavior of full-scale vehicles, named the Waveshare JetRacer Pro AI5 kit [150]. The results in the paper along with a video of the experiments provided in [151] show that the vehicles are able to safely accomplish the planning goals without collisions.…”

Section: B Practical Experiments Of Moo On Mrssmentioning

confidence: 99%

A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems

Naderi,

Blondin

2023

IEEE Access

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Over the last decades, researchers have studied the Multi-Objective Optimization problem (MOO) for Multi-Agent Systems (MASs). However, most of them consider the problem formulation to be an unprioritized sum of objective functions, and no work has reviewed problems with the formulation of the prioritized sum of objective functions to facilitate the study of the subject and identify the needs arising from it. In the context of Multi-Robot Systems (MRSs), most studies only focus on the mathematical development of their proposed MOO algorithm without paying attention to the application. In practice, there is not a comprehensive review to identify the reliable algorithms already applied to real platforms. Using a mapping and state-of-the-art review, this paper aims to fill these gaps by first offering a detailed overview of the discrete-time MOO methods for MASs. More specifically, we classify existing MOO methods based on the formulation of the problem into the sum of objective functions and the prioritized sum of objective functions. Secondly, we review the applications of these methods in MRSs and the practical implementation of MOO algorithms on real MRS. Finally, we suggest future research directions to extend the existing methods to more realistic approaches, including open problems in the new research area of the prioritized sum of objective functions and practical challenges for the implementation of the existing methods in robotics. This work introduces the field of MAS to researchers and enables them to position themselves in the current research trends.INDEX TERMS Multi-agent systems, multi-objective optimization, multi-robot systems, prioritized sum of objective functions.

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Distributed Nonlinear Trajectory Optimization for Multi-Robot Motion Planning

Abstract: This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

Cited by 16 publications

References 63 publications

Coordinated Cooperative Distributed Decision-Making using Synchronization of Local Plans

Coordinated Cooperative Distributed Decision-Making using Synchronization of Local Plans

Time-Inverted Kuramoto Model Meets Lissajous Curves: Multi-Robot Persistent Monitoring and Target Detection

A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems

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