Temporal Logic Planning and Control of Robotic Swarms by Hierarchical Abstractions

Kloetzer, Marius; Belta, Călin

doi:10.1109/tro.2006.889492

Cited by 112 publications

(62 citation statements)

References 23 publications

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“…Topics covered in this paper are related mainly to control and stabilization of MAV teams. In literature, one can find papers describing control methodologies for swarms of both autonomous ground vehicles [13], [14], [15], [16] and unmanned aerial vehicles [17], [18], [19], [20]. These methods are often inspired by nature (e.g., by flocks of birds [21] or molecules forming crystals [22]), and they try to fulfil various requirements of swarm robotics.…”

Section: A Swarms Of Autonomous Vehiclesmentioning

confidence: 99%

“…Since the proposed approach follows the requirements of swarms as listed in [23]: scalability for large groups, high redundancy and fault tolerance, usability in tasks unsolvable by a single robot and locally limited sensing and communication abilities, examples of studies investigating these domains should also be mentioned. In particular, a hierarchical framework for planning and control of arbitrarily large swarms is proposed in [13]. Considerations influencing the fault tolerance of teams are discussed in [24] and various co-operation strategies for teams of MAVs solving multi-robot tasks are published in [25].…”

Section: A Swarms Of Autonomous Vehiclesmentioning

confidence: 99%

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System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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Abstract-A complex system for control of swarms of Micro Aerial Vehicles (MAV), in literature also called as Unmanned Aerial Vehicles (UAV) or Unmanned Aerial Systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visualbased stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

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Section: A Swarms Of Autonomous Vehiclesmentioning

confidence: 99%

Section: A Swarms Of Autonomous Vehiclesmentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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“…In [27,28], Kloetzer and Belta adopt a model checking approach to analysing the motion of robot swarms. A hierarchical framework is suggested to abstract away from the many details of the problem.…”

Section: Formal Verification Of Robot Swarmsmentioning

confidence: 99%

Analysing robot swarm behaviour via probabilistic model checking

Konur¹,

Dixon²,

Fisher³

2012

Robotics and Autonomous Systems

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An alternative to deploying a single robot of high complexity can be to utilize robot swarms comprising large numbers of identical, and much simpler, robots. Such swarms have been shown to be adaptable, fault-tolerant and widely applicable. However, designing individual robot algorithms to ensure effective and correct overall swarm behaviour is actually very difficult. While mechanisms for assessing the effectiveness of any swarm algorithm before deployment are essential, such mechanisms have traditionally involved either computational simulations of swarm behaviour, or experiments with robot swarms themselves. However, such simulations or experiments cannot, by their nature, analyse all possible swarm behaviours. In this paper, we will develop and apply the use of automated probabilistic formal verification techniques to robot swarms, involving an exhaustive mathematical analysis, in order to assess whether swarms will indeed behave as required. In particular we consider a foraging robot scenario to which we apply probabilistic model checking.

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“…A controller is constructed for the abstract model using the results from automata theory, which is then implemented in the concrete system. This has been successfully applied in the synthesis of switched systems [11], [12] and in the context of robot path planning [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Optimal control with regular objectives using an abstraction-refinement approach

Leong

Prabhakar

2016

2016 American Control Conference (ACC)

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Abstract-This paper presents a method to synthesize a sequence of control inputs for a discrete-time switched linear system equipped with a cost function, such that the controlled system behavior satisfies a linear-time temporal objective with minimal cost. An abstract finite state weighted transition system is constructed, such that the cost of the optimal control on the abstract system provides an upper bound on the cost of the optimal control for the original system. Specifically, the abstract system is constructed from finite partitions of the state and input spaces by solving certain optimization problems, and a sequence of controllers is obtained by considering a sequence of uniformly refined partitions. Under mild assumptions on the optimal control for the switched system, the costs of the sequence of controllers constructed converges to the cost of the optimal control for the switched system. The abstraction refinement algorithm is implemented in the tool OptCAR. The feasibility of this approach is illustrated on two different examples, by constructing automatically, sub-optimal controllers with improving optimal costs.

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Temporal Logic Planning and Control of Robotic Swarms by Hierarchical Abstractions

Cited by 112 publications

References 23 publications

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

Analysing robot swarm behaviour via probabilistic model checking

Optimal control with regular objectives using an abstraction-refinement approach

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