An evolutionary robotics approach for the distributed control of satellite formations

Izzo, Dario; Simões, Luís; Croon, Guido C. H. E. de

doi:10.1007/s12065-014-0111-9

Cited by 20 publications

(29 citation statements)

References 18 publications

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“…al [14]. That paper described a homogeneous swarm of three SPHERES spacecraft flying in a triangular formation where each side was a different length.…”

Section: Taskmentioning

confidence: 99%

“…The control system should use this information and provide the MAV with a velocity set-point. As in [14], we utilize a single hidden layer Artificial Neural Network (ANN) with three inputs and two outputs as shown in Fig. 2.…”

Section: Taskmentioning

confidence: 99%

“…Multiple objective optimization was used to promote effective exploration of the fitness landscape. Again, to facilitate a fair comparison, the objective functions used in this paper are based on those used in [14] and are given in (3).…”

Section: Evolutionary Optimizationmentioning

confidence: 99%

“…The second promotes individuals that have a low final velocity. The final function is an augmentation to the original set from [14] and promotes behavior that results in the centroid of the formation remaining inside of a 2m radius of the origin of the axis system. This requirement was added due to the practical limitation that the vehicles must operate in a constrained 8 × 8m flight arena.…”

Section: Evolutionary Optimizationmentioning

confidence: 99%

“…al. [14] to generate an asymmetric formation of a swarm of robots using a homogeneous control system. The task will be discussed in more detail in Sect.…”

Section: Introductionmentioning

confidence: 99%

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Abstraction as a Mechanism to Cross the Reality Gap in Evolutionary Robotics

Scheper

Croon

2016

From Animals to Animats 14

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Abstract. One of the major challenges of Evolutionary Robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction on the sensory inputs and motor actions as a potential solution to this problem. Abstraction means that the robot uses preprocessed sensory inputs and closed loop low-level controllers that execute higher level motor commands. We apply abstraction to the task of forming an asymmetric triangle with a homogeneous swarm of MAVs. The results show that the evolved behavior is effective both in simulation and reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Furthermore, we study the evolved solution, showing that it exploits the environment (in this case the identical behavior of the other robots) and creates behavioral attractors resulting in the creation of the required formation. Hence, the analysis suggests that by using abstraction, sensory-motor coordination is not necessarily lost but rather shifted to a higher level of abstraction.

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“…al [14]. That paper described a homogeneous swarm of three SPHERES spacecraft flying in a triangular formation where each side was a different length.…”

Section: Taskmentioning

confidence: 99%

Section: Taskmentioning

confidence: 99%

Section: Evolutionary Optimizationmentioning

confidence: 99%

Section: Evolutionary Optimizationmentioning

confidence: 99%

“…al. [14] to generate an asymmetric formation of a swarm of robots using a homogeneous control system. The task will be discussed in more detail in Sect.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Abstraction as a Mechanism to Cross the Reality Gap in Evolutionary Robotics

Scheper

Croon

2016

From Animals to Animats 14

Self Cite

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Optimization of Swarm Behavior Assisted by an Automatic Local Proof for a Pattern Formation Task

Coppola

Croon

2018

Lecture Notes in Computer Science

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In this work, we optimize the behavior of swarm agents in a pattern formation task. We start with a local behavior, expressed as a local state-action map, that has been formally proven to lead the swarm to always eventually form the desired pattern. We seek to optimize this for performance while keeping the formal proof. First, the state-action map is pruned to remove unnecessary state-action pairs, reducing the solution space. Then, the probabilities of executing the remaining actions are tuned with a genetic algorithm. The final controllers allow the swarm to form the patterns up to orders of magnitude faster than with the original behavior. The optimization is found to suffer from scalability issues. These may be tackled in future work by automatically minimizing the size of the local state-action map with a further direct focus on performance.

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