Stochastic motion planning with path constraints and application to optimal agent, resource, and route planning

Lim, Sejoon; Rus, Daniela

doi:10.1109/icra.2012.6224707

Cited by 15 publications

(11 citation statements)

References 15 publications

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“…Surveys show that, in most cases, traffic participants trust and follow the navigation recommendations [9]. Systems for traffic planning in the presence of congestion have been researched by [3,13,14,15] by controlling the information given to each participant (proposing certain routes) to achieve individual or global social optimum performance [3]. The studies done in [21] and [24] analyse the traffic performance when information about congestion, containing either local details about the neighbouring nodes or global details about the traffic networks, is disseminated according to a model of information dissemination.…”

Section: Related Workmentioning

confidence: 99%

Information Dynamics in Transportation Systems with Traffic Lights Control

Litescu

Viswanathan

Aydt³

et al. 2016

Procedia Computer Science

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Due to recent advanced communication possibilities between traffic infrastructure, vehicles and drivers, the optimization of traffic lights control can be approached in novel ways. At the same time, this may introduce new unexpected dynamics in transportation systems. Our research aims to determine how drivers and traffic lights systems interact and influence each other when they are informed one about another's behaviour. In order to study this, we developed an agent based model to simulate transportation systems with static and dynamic traffic lights and drivers using information about the traffic lights behaviour. Experiments reveal that the system's performance improves when a bigger share of drivers receive information for both static and dynamic traffic lights systems. This performance improvement is due to drivers managing to avoid stopping at red light rather them adapting their speed to different distances to the traffic lights systems. Additionally, it is demonstrated that the duration of the fixed phases also influences the performance when drivers use speed recommendations. Moreover, the results show that dynamic traffic lights can produce positive effects for roads with high speed limits and high traffic intensity, while in the rest of the cases static control is better. Our findings can be used for building more efficient traffic lights systems.

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Section: Related Workmentioning

confidence: 99%

Information Dynamics in Transportation Systems with Traffic Lights Control

Litescu

Viswanathan

Aydt³

et al. 2016

Procedia Computer Science

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show abstract

“…The environment is partitioned into logical regions, grouping taxis within those regions who then negotiate concurrent assignments of pending requests with a focus on the group average, resulting in reduced average customer wait time and reduced empty taxi cruising time. In a similar vein, Lim and Rus test their stochastic path planning solution against a Singapore road network incorporating historical traffic and travel data in [5]. Dasgupta describes a cooperative foraging scenario with shared task execution using pheromones to denote a task progress in [8].…”

Section: Related Workmentioning

confidence: 99%

“…Multi-robot task allocation (MRTA) is an important aspect of multi-robot systems, where robots have to autonomously perform tasks that are distributed spatially and temporally within an environment. MRTA is used in numerous applications of robotic systems, including reconnaissance [1], unmanned search and rescue operations [2,3], cooperative transportation [4][5][6] and autonomous exploration [7,8]. The fundamental problem addressed in MRTA is the following: Given a set of robots and a set of tasks that need to be performed by the robots, what is a suitable assignment of robots to tasks so that a global objective, such as the time to complete the tasks, or the distance traveled, or the energy expended by the robots is reduced.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Queuing-Based Algorithm for Multi-Robot Task Allocation

2015

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Multi-robot task allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to allocate a set of tasks to a set of robots so that the tasks can be completed by the robots while ensuring that a certain metric, such as the time required to complete all tasks, or the distance traveled, or the energy expended by the robots is reduced. We consider a scenario where tasks can appear dynamically and a task needs to be performed by multiple robots to be completed. We propose a new algorithm called SQ-MRTA (Spatial Queueing-MRTA) that uses a spatial queue-based model to allocate tasks between robots in a distributed manner. We have implemented the SQ-MRTA algorithm on accurately simulated models of Corobot robots within the Webots simulator for different numbers of robots and tasks and compared its performance with other state-of-the-art MRTA algorithms. Our results show that the SQ-MRTA algorithm is able to scale up with the number of tasks and robots in the environment, and it either outperforms or performs comparably with respect to other distributed MRTA algorithms.

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“…The MRTA problem can be found in several fields of MRS, such as: reconnaissance [5], [36], unmanned search and rescue missions [26], [27], logistics [23], [37], [54], autonomous exploration [12], [28], etc. The MRTA problem is known to be N P-hard [24].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

2020

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We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be N P-hard even with small numbers of robots and tasks. The field of survivors' search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks; (b) Unmanned Aerial Vehicles are the robots; and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve conflicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans; (ii) traveled distances; and (iii) exchanged messages.INDEX TERMS Ant colony system, consensus-based bundle algorithm, coordination, multi-robot systems, multi-robot task allocation problem, resolution of conflicts.

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Stochastic motion planning with path constraints and application to optimal agent, resource, and route planning

Cited by 15 publications

References 15 publications

Information Dynamics in Transportation Systems with Traffic Lights Control

Information Dynamics in Transportation Systems with Traffic Lights Control

A Spatial Queuing-Based Algorithm for Multi-Robot Task Allocation

A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

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