Multiple Mobile Robot Task and Motion Planning: A Survey

Antonyshyn, Luka; Silveira, Jefferson E.; Givigi, Sidney N.; Marshall, Joshua A.

doi:10.1145/3564696

Cited by 30 publications

(8 citation statements)

References 112 publications

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“…The state-of-art in this respect still involves a great deal of manual steps at the task level needing to be specified for each individual agent system in a larger mission. Progress at the automated multi-agent path planning level is somewhat more developed [9] , although a broader solution to robust and useful in-field multi-agent systems requires development at both the multiagent task planning and path planning levels.…”

Section: Comparison With Other Approaches and Scalability Evaluationmentioning

confidence: 99%

Leveraging active queries in collaborative robotic mission planning

Berger,

Doherty,

Rudol

et al. 2024

Intelligence & Robotics

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This paper focuses on the high-level specification and generation of 3D models for operational environments using the idea of active queries as a basis for specifying and generating multi-agent plans for acquiring such models. Assuming an underlying multi-agent system, an operator can specify a request for a particular type of model from a specific region by specifying an active query. This declarative query is then interpreted and executed by collecting already existing data/information in agent systems or, in the active case, by automatically generating high-level mission plans for agents to retrieve and generate parts of the model that do not already exist. The purpose of an active query is to hide the complexity of multi-agent mission plan generation, data transformations, and distributed collection of data/information in underlying multi-agent systems. A description of an active query system, its integration with an existing multi-agent system and validation of the active query system in field robotics experimentation using Unmanned Aerial Vehicles and simulations are provided.

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Section: Comparison With Other Approaches and Scalability Evaluationmentioning

confidence: 99%

Leveraging active queries in collaborative robotic mission planning

Berger,

Doherty,

Rudol

et al. 2024

Intelligence & Robotics

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“…Task allocation, a popular task planning strategy, focuses on assigning groups of simple tasks to individual players for execution. When applied to M-RA differential games, the player configurations, availabilities and capabilities need to be considered (Smith et al, 2009;Bajaj and Bopardikar, 2019;Yan et al, 2020;Yan et al, 2022;Bajaj et al, 2021;Antonyshyn et al, 2022;Velhal et al, 2022). In this section, we first introduce an integer linear programming formulation for capturing the most number of evaders in Section 5.1 and then propose a polynomial approximation algorithm in Section 5.2.…”

Section: Task Allocationmentioning

confidence: 99%

Multiplayer reach-avoid differential games with simple motions: A review

Yan

Deng²,

Duan³

et al. 2023

Front. Control Eng.

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This paper reviews the recent works on multiplayer reach-avoid (M-RA) differential games between two adversarial teams in a game region which is split into a goal region and a play region. The pursuit team aims to protect the goal region from the evasion team by cooperatively capturing the evaders which start from the play region and strive to enter the goal region. We provide a selective overview of algorithms and theoretical results for multiplayer reach-avoid differential games. Specifically, we focus on point mass holonomic players that can move freely in the game region and have simple motions as Rufus Isaacs states. We describe how the challenges due to high-dimensional continuous joint action and state spaces, as well as complex cooperations and competitions among players, can be properly resolved by a combination of qualitative and quantitative analysis of small-scale games and optimal task allocation. We finally point out the limitations of the current works and identify fruitful future research directions on theoretical studies of multiplayer reach-avoid differential games.

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“…By recursively decomposing the plan into subgoals, HTNs can generate a plan that achieves the desired goal. For example, in [140] it is pointed out that integrating task and motion planning through the use of multiple mobile robots has become a popular approach to solve complex problems requiring cooperative efforts, as advancements in mobile robotics, autonomous systems, and artificial intelligence continue to rise.…”

Section: U N C O R R E C T E D P R O O F V E R S I O Nmentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.

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Multiple Mobile Robot Task and Motion Planning: A Survey

Cited by 30 publications

References 112 publications

Leveraging active queries in collaborative robotic mission planning

Leveraging active queries in collaborative robotic mission planning

Multiplayer reach-avoid differential games with simple motions: A review

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

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