Multi-Robot Task Allocation Games in Dynamically Changing Environments

Park, Shinkyu; Zhong, Yaofeng Desmond; Leonard, Naomi Ehrich

doi:10.1109/icra48506.2021.9561809

Cited by 19 publications

(6 citation statements)

References 14 publications

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“…The results were further developed in (Park et al, 2019b) and extended to the case in which a networked group of agents experience communication and computational delays in learning the payoffs. Park et al (2021) use a replicator model and the stabilization method to derive decision-making rules with performance guarantees for allocation of agents over tasks in real-time in a changing environment. The approach is demonstrated in a multi-robot trash collection problem, where robots make decisions for moving among patches to pick up trash that is accumulating nonuniformly over time so that trash volume is minimized.…”

Section: Recent Models For Explaining and Shaping Collective Intellig...mentioning

confidence: 99%

Collective intelligence as a public good

Leonard

Levin

2022

Collective Intelligence

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We discuss measures of collective intelligence in evolved and designed self-organizing ensembles, defining collective intelligence in terms of the benefits to be gained through the exchange of information and other resources, as well as through coordination or cooperation, in the interests of a public good. These benefits can be numerous, from estimating a hard-to-observe cue to efficiently searching for resource. The measures should also account for costs to individuals, such as in attention or energy, and trade-offs for the ensemble, such as the flexibility to respond to an important change in the environment versus stability that is robust to unimportant variability. When there is a tension between the interests of the individual and those of the group, game-theoretic considerations may affect the level of collective intelligence that can be achieved. Models of individual rules that yield collective dynamics with multi-stable solutions provide a means to examine and shape collective intelligence in evolved and designed systems.

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Section: Recent Models For Explaining and Shaping Collective Intellig...mentioning

confidence: 99%

Collective intelligence as a public good

Leonard

Levin

2022

Collective Intelligence

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“…Although previous studies on task allocation and path planning that consider environment change into account exist, the environment changes addressed by them are defined differently. Park et al [11] performed task allocation for a robot that collects garbage, treating changes in the amount of garbage as changes in the environment. They responded to this environment change by modifying the tasks already allocated to the robots.…”

Section: A Studies That Consider Environment Changesmentioning

confidence: 99%

“…Equations ( 11) and ( 12) are the constraints on facility construction tasks. Equation (11) represents the constraint that each construction task must be executed once by one of the construction robots. Equation ( 12) is a constraint that considers the execution time of the construction task.…”

Section: Formulation For Optimizationmentioning

confidence: 99%

Multi-Agent Action Graph Based Task Allocation and Path Planning Considering Changes in Environment

Okubo

Takahashi

2023

IEEE Access

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Task allocation and path planning considering changes in the mobility of robots in the environment allows the robots to efficiently execute tasks with smaller travel times. A lunar base construction is one of the situations in which robots can more efficiently accomplish its goal by taking such environment changes into account when performing tasks. For the construction, we assumed that when a robot executes a task of building a road, the environment changes such that aisles that were unusable before the task become usable post execution. If such changes in environment are considered in advance, the robot can efficiently plan to wait until the environment changes and can move before executing the task. However, previous studies have not considered such changes, resulting in inefficient planning. To solve this problem, we developed a multi-agent action graph that consists of multiple layers and expresses the environment changes associated with task execution in terms of changes in these layers. In this graph, task allocation and path planning are formulated as a combinatorial optimization problem and are optimized using mixed-integer programming. Multi-agent action graphs and the proposed formulation enable efficient planning considering changes in the robots' mobility in advance. Through simulations, we confirmed that the proposed method completed the construction of the lunar base approximately 16.4% earlier than the conventional method, while consuming approximately 16.0% less total energy of the robots.INDEX TERMS Task allocation, path planning, environment changes, multi-robot systems, robotic lunar surface operations.

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“…This paper develops a new control approach by viewing the autonomous air vehicle in a turbulent flow as an inertial point-mass particle driven by a constant horizontal thrust that can be adjusted so as to match the desired fast-tracking characteristics of the given turbulent flow. There is considerable precedence for treating vehicles as point masses for navigation and control purposes, whenever their size is small relative to the vortex length scale [ 44 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ]. Hence, our hypothesis is that by producing a controlled thrust that modifies the vehicle’s inertial response time to match the vortex time scale, the vehicle may be accelerated through the turbulent flow similarly to the fast-tracking effect ( Figure 1 a).…”

Section: Background On Transport Theory and The Fast-tracking Effectmentioning

confidence: 99%

A Fast-Tracking-Particle-Inspired Flow-Aided Control Approach for Air Vehicles in Turbulent Flow

2022

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Natural phenomena such as insect migration and the thermal soaring of birds in turbulent environments demonstrate animals’ abilities to exploit complex flow structures without knowledge of global velocity profiles. Similar energy-harvesting features can be observed in other natural phenomena such as particle transport in turbulent fluids. This paper presents a new feedback control approach inspired by experimental studies on particle transport that have recently illuminated particles’ ability to traverse homogeneous turbulence through the so-called fast-tracking effect. While in nature fast tracking is observed only in particles with inertial characteristics that match the flow parameters, the new fast-tracking feedback control approach presented in this paper employs available propulsion and actuation to allow the vehicle to respond to the surrounding flow in the same manner as ideal fast-tracking particles would. The resulting fast-tracking closed-loop controlled vehicle is then able to leverage homogeneous turbulent flow structures, such as sweeping eddies, to reduce travel time and energy consumption. The fast-tracking approach is shown to significantly outperform existing optimal control solutions, such as linear quadratic regulator and bang-bang control, and to be robust to changes in the vehicle characteristics and/or turbulent flow parameters.

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Multi-Robot Task Allocation Games in Dynamically Changing Environments

Cited by 19 publications

References 14 publications

Collective intelligence as a public good

Collective intelligence as a public good

Multi-Agent Action Graph Based Task Allocation and Path Planning Considering Changes in Environment

A Fast-Tracking-Particle-Inspired Flow-Aided Control Approach for Air Vehicles in Turbulent Flow

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