A Constraint-based Mission Planning Approach for Reconfigurable Multi-Robot Systems

The application of reconfigurable multi-robot systems introduces additional degrees of freedom to design robotic missions compared to classical multi-robot systems. To allow for autonomous operation of such systems, planning approaches have to be investigated that cannot only cope with the combinatorial challenge arising from the increased flexibility of modular systems, but also exploit this flexibility to improve for example the safety of operation. While the problem originates from the domain of robotics it is of general nature and significantly intersects with operations research. This paper suggests a constraint-based mission planning approach, and presents a set of revised definitions for reconfigurable multi-robot systems including the representation of the planning problem using spatially and temporally qualified resource constraints. Planning is performed using a multi-stage approach and a combined use of knowledge-based reasoning, constraint-based programming and integer linear programming. The paper concludes with the illustration of the solution of a planned example mission.

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Simultaneous task allocation and planning for temporal logic goals in heterogeneous multi-robot systems

The International Journal of Robotics Research ◽

10.1177/0278364918774135 ◽

2018 ◽

Vol 37 (7) ◽

pp. 818-838 ◽

Cited By ~ 21

Author(s):

Philipp Schillinger ◽

Mathias Bürger ◽

Dimos V. Dimarogonas

Keyword(s):

Temporal Logic ◽

Resource Constraints ◽

A Priori ◽

Planning Problem ◽

Execution Costs ◽

Robot Systems ◽

Optimal Action ◽

Mission Specification ◽

Complex Planning

This paper describes a framework for automatically generating optimal action-level behavior for a team of robots based on temporal logic mission specifications under resource constraints. The proposed approach optimally allocates separable tasks to available robots, without requiring a priori an explicit representation of the tasks or the computation of all task execution costs. Instead, we propose an approach for identifying sub-tasks in an automaton representation of the mission specification and for simultaneously allocating the tasks and planning their execution. The proposed framework avoids the need to compute a combinatorial number of possible assignment costs, where each computation itself requires solving a complex planning problem. This can improve computational efficiency compared with classical assignment solutions, in particular for on-demand missions where task costs are unknown in advance. We demonstrate the applicability of the approach with multiple robots in an existing office environment and evaluate its performance in several case study scenarios.

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IMPERA: Integrated Mission Planning for Multi-Robot Systems

Robotics ◽

10.3390/robotics4040435 ◽

2015 ◽

Vol 4 (4) ◽

pp. 435-463 ◽

Cited By ~ 2

Author(s):

Daniel Saur ◽

Kurt Geihs

Keyword(s):

Mission Planning ◽

Robot Systems ◽

Multi Robot

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GPS-INS state estimation for multi-robot systems with computational resource constraints

2009 American Control Conference ◽

10.1109/acc.2009.5159809 ◽

2009 ◽

Cited By ~ 1

Author(s):

Luke M. Wachter ◽

Laura E. Ray

Keyword(s):

State Estimation ◽

Resource Constraints ◽

Computational Resource ◽

Robot Systems ◽

Multi Robot

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REAL-TIME SCHEDULING OF DISTRIBUTED MULTI-ROBOT MANIPULATOR SYSTEMS

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2005-0012 ◽

2005 ◽

Vol 29 (2) ◽

pp. 179-194

Author(s):

P. Yuan ◽

M. Moallem ◽

R.V. Patel

Keyword(s):

Execution Time ◽

Cooperative Control ◽

Resource Constraints ◽

Robot Manipulator ◽

Scheduling Algorithm ◽

Worst Case ◽

Real Time Scheduling ◽

Robot Systems ◽

Time Scheduling ◽

Multi Robot

This paper presents an online task-oriented scheduling method and an off-line scheduling algorithm that can be used for cooperative control of a distributed multi-robot manipulator system. Satisfaction of temporal deadlines and tasks-relative constraints are considered in this work. With the proposed algorithms, both the timing constraints and relative task dependencies can be satisfied when the worst-case execution time is unknown. The total execution time of the assembly tasks can be significantly improved compared with other known scheduling algorithms such as the First-In-First-Out and Round Robin scheduling methods. Experimental results are presented indicating that the proposed algorithm can be used for improving the performance of multi-robot systems in terms of timing and resource constraints.

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Multi-Robot Planning Under Uncertain Travel Times and Safety Constraints

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/68 ◽

2019 ◽

Author(s):

Masoumeh Mansouri ◽

Bruno Lacerda ◽

Nick Hawes ◽

Federico Pecora

Keyword(s):

Team Performance ◽

Current Practice ◽

Stochastic Petri Nets ◽

Travel Times ◽

Planning Approach ◽

Robot Systems ◽

Markov Decision ◽

Safety Constraints ◽

Multi Agent ◽

Multi Robot

We present a novel modelling and planning approach for multi-robot systems under uncertain travel times. The approach uses generalised stochastic Petri nets (GSPNs) to model desired team behaviour, and allows to specify safety constraints and rewards. The GSPN is interpreted as a Markov decision process (MDP) for which we can generate policies that optimise the requirements. This representation is more compact than the equivalent multi-agent MDP, allowing us to scale better. Furthermore, it naturally allows for asynchronous execution of the generated policies across the robots, yielding smoother team behaviour. We also describe how the integration of the GSPN with a lower-level team controller allows for accurate expectations on team performance. We evaluate our approach on an industrial scenario, showing that it outperforms hand-crafted policies used in current practice.

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A distributed and integrated method for cooperative mission planning of multiple heterogeneous UAVs

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-05-2017-0124 ◽

2018 ◽

Vol 90 (9) ◽

pp. 1403-1412

Author(s):

Weinan WU ◽

Naigang Cui

Keyword(s):

Degrees Of Freedom ◽

Planning Process ◽

Feasible Solution ◽

Optimal Solution ◽

Path Following ◽

Mission Planning ◽

Planning Problem ◽

Content Type ◽

Integrated Method ◽

Operating Rate

Purpose The purpose of this paper is to develop a distributed and integrated method to get a fast and feasible solution for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs). Design/methodology/approach In this study, the planning process is conducted in a distributed framework; the cooperative mission planning problem is reformulated with some specific constraints in the real mission; a distributed genetic algorithm is the algorithm proposed for searching for the optimal solution; genes of the chromosome are modified to adapt to the heterogeneous characteristic of UAVs; a fixed-wing UAV’s six degrees-of-freedom (DOF) model with a path following method is used to test the proposed mission planning method. Findings This method not only has the ability to obtain good feasible solutions but also improves the operating rate vastly. Research limitations/implications This study is only applied to the case where the communication among UAVs is linked during the mission. Practical implications This study is expected to be practical for a real mission because of its fast operating rate and good feasible solution. Originality/value This solution is tested on a fixed-wing UAV’s 6-DOF model by a path following method, so it is believable from the perspective of an autonomous UAV guidance and control system.

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