Dynamic multi-robot task allocation under uncertainty and temporal constraints

Choudhury, Shushman; Gupta, Jayesh K.; Kochenderfer, Mykel J.; Sadigh, Dorsa; Bohg, Jeannette

doi:10.1007/s10514-021-10022-9

Cited by 55 publications

(21 citation statements)

References 54 publications

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“…The policy search algorithm of reinforcement learning is used to study the autonomous task sequencing challenge, in which the agent swarm can sequence tasks whose execution orders are unknown [17]. Stochastic conflict-based allocation model [9] and adaptive allocation approach for heterogeneous agents [13,14] are proposed to solve the challenge of dynamic multi-agent task assignment under environment uncertainty and temporal constraints. All the above methods regard task allocation as an optimization problem that do not consider the impact of the visual perception of different agents.…”

Section: B Dynamic Task Decomposition and Task Allocationmentioning

confidence: 99%

Embodied Multi-Agent Task Planning from Ambiguous Instruction

Liu¹,

Li²,

Guo³

et al. 2022

Robotics: Science and Systems XVIII

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In human-robots collaboration scenarios, a human would give robots an instruction that is intuitive for the human himself to accomplish. However, the instruction given to robots is likely ambiguous for them to understand as some information is implicit in the instruction. Therefore, it is necessary for the robots to jointly reason the operation details and perform the embodied multi-agent task planning given the ambiguous instruction. This problem exhibits significant challenges in both language understanding and dynamic task planning with the perception information. In this work, an embodied multi-agent task planning framework is proposed to utilize external knowledge sources and dynamically perceived visual information to resolve the high-level instructions, and dynamically allocate the decomposed tasks to multiple agents. Furthermore, we utilize the semantic information to perform environment perception and generate sub-goals to achieve the navigation motion. This model effectively bridges the difference between the simulation environment and the physical environment, thus it can be simultaneously applied in both simulation and physical scenarios and avoid the notorious sim2real problem. Finally, we build a benchmark dataset to validate the embodied multi-agent task planning problem, which includes three types of high-level instructions in which some target objects are implicit in instructions. We perform the evaluation experiments on the simulation platform and in physical scenarios, demonstrating that the proposed model can achieve promising results for multi-agent collaborative tasks.

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Section: B Dynamic Task Decomposition and Task Allocationmentioning

confidence: 99%

Embodied Multi-Agent Task Planning from Ambiguous Instruction

Liu¹,

Li²,

Guo³

et al. 2022

Robotics: Science and Systems XVIII

View full text Add to dashboard Cite

show abstract

“…ere may also be dependencies between tasks that dictate that tasks must be completed in a particular order [3,22,23], known as precedence or ordering constraints. Furthermore, there may be restrictions on when a task must be completed [3,24,25], known as temporal constraints. For instance, certain tasks may need to be completed simultaneously or a task may need to be completed within a time window.…”

Section: Constraints On Tasks and Robotsmentioning

confidence: 99%

Task Allocation Using a Team of Robots

et al. 2022

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Purpose of Review Task allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. We present a survey of multi-robot task allocation covering many problem variants and solution approaches, both centralized and decentralized. Recent Findings In recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples of important research and recent progress in these directions, which are captured in this survey, along with similar examples for the various solutions that have been developed to solve such problems. Summary We first present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate, and we describe how the problem can be varied depending on the feasibility constraints, objective functions, and the level of dynamically changing information. In addition, we discuss existing solution approaches for the problem including optimization-based approaches, and market-based approaches.

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“…Moreover, these disturbances are modeled as Gaussian random variables, characterized by their mean and covariance matrices. Thus, the model of a given robot m at time k are defined as Equation (2).…”

Section: Robot Modelmentioning

confidence: 99%

“…Multi-robot systems play a vital role in next-generation factories, urban search and rescue, and package delivery, and they are anticipated to be applied in space exploration [1,2]. One of the key ingredients to a multi-robot system is the motion planning module.…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Robust Motion Planning based on Model Predictive Priority Contouring Control with Double-Layer Corridors

Wang

2022

Applied Sciences

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Disturbance poses a major challenge for the safety and real-time performance of robust robot motion planning. To address the disturbance while improving the real-time performance of multi-robot robust motion planning, a model predictive priority contouring control method is proposed. First, an improved conflict-based search (ICBS) planner is utilized to plan reference paths. The low-level planner of the conflicted-based search (CBS) planner is replaced by the hybrid A* planner and reference paths are adopted as an initial guess of model predictive priority contouring control. Second, double-layer corridors are proposed to provide safety guarantees, which include static-layer corridors and dynamic-layer corridors. The static-layer corridors are generated based on reference paths and the dynamic-layer corridors are generated based on the relative positions and velocities of robots. The double-layer corridors are applied as safety constraints of model predictive priority contouring control. Third, a prioritization mechanism is devised to improve computational efficiency. Priorities are assigned according to each robot’s task completion percentage. Based on the assigned priority, multiple robots are grouped, and each group executes the model predictive priority contouring control algorithm to acquire trajectories. Finally, our method is compared with the centralized method and the soft constraint-based DMPC. Simulations verify the effectiveness and real-time performance of our approach.

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Dynamic multi-robot task allocation under uncertainty and temporal constraints

Cited by 55 publications

References 54 publications

Embodied Multi-Agent Task Planning from Ambiguous Instruction

Embodied Multi-Agent Task Planning from Ambiguous Instruction

Task Allocation Using a Team of Robots

Multi-Robot Robust Motion Planning based on Model Predictive Priority Contouring Control with Double-Layer Corridors

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