Incremental evolution of autonomous controllers for unmanned aerial vehicles using multi-objective genetic programming

Barlow, Gregory J.; Oh, Chamteut; Grant, Edward

doi:10.1109/iccis.2004.1460671

Cited by 33 publications

(40 citation statements)

References 8 publications

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“…Three stages were used, from locating a large immobile target to tracking a smaller, moving one. Parker [151] followed a similar incremental strategy to evolve gaits for a hexapod robot; Barlow et al [8] did the same for controllers of simulated UAV, but using a MOEA instead of a mono-objective EA; Barate and Mazanera [7] employed two phases to evolve vision algorithms for mobile robots, where the first phase was based on behavior imitation and the second one on goal-reaching evaluations.…”

Section: Task Specificmentioning

confidence: 99%

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Doncieux

Mouret

2014

Evol. Intel.

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Evolutionary robotics is often viewed as the application of a family of black-box optimization algorithms -evolutionary algorithms -to the design of robots, or parts of robots. When considering evolutionary robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most evolutionary robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because evolutionary robotics experiments share common features, selective pressures for evolutionary robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two subcategories are further considered: task-specific approaches, which require knowledge on how to solve the task and taskagnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.

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Section: Task Specificmentioning

confidence: 99%

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Doncieux

Mouret

2014

Evol. Intel.

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“…Doncieux and Mouret [1] review recent advances in ER methods in a survey of selective pressures used in evolution. These include diversity maintenance methods [2,[26][27][28] and the use of multiobjective optimization methods [29,30]. Very large populations, situated asynchronous genetic algorithms (GAs) [31] and/or task-independent diversitymaintaining mechanisms coupled with increases in computing power could lead to an order of magnitude increase in the complexity of achievable tasks.…”

Section: Introductionmentioning

confidence: 99%

Embodied artificial life at an impasse can evolutionary robotics methods be scaled?

Nelson¹

2014

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

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Abstract-Evolutionary robotics (ER) investigates the application of artificial evolution toward the synthesis of robots capable of performing autonomous behaviors. Over the last 25 years, researchers have reported increasingly complex evolved behaviors, and have compiled a de facto set of benchmark tasks. Perhaps the best known of these is the obstacle avoidance and target homing task performed by differential drive robots. More complex tasks studied in recent ER work include augmented variants of the rodent T-maze and complex foraging tasks. But can proof-of-concept results such as these be extended to evolve complex autonomous behaviors in a general sense? In this topical analysis paper we survey relevant research and make the case that common tasks used to demonstrate the effectiveness of evolutionary robotics are not characteristic of more general cases and in fact do not fully prove the concept that artificial evolution can be used to evolve sophisticated autonomous agent behaviors. Robots capable of performing many of the tasks studied in ER have now been evolved using nearly aggregate binary success/fail fitness functions. However, arguments used to support the necessity of incremental methods for complex tasks are essentially sound. This raises the possibility that the tasks themselves allow for relatively simple solutions, or span a relatively small candidate solution set. This paper presents these arguments in detail and concludes with a discussion of current ER research.

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“…As might be expected, controllers evolved on lower assumed levels of error were not particularly fit when error increased this much. Rather than using one of the controllers evolved in previous research [11]- [13], we evolved new controllers to transfer to an EvBot. The only change in the simulation from the previous research was a change in the accuracy of the simulated sensor to ±45…”

Section: Unmanned Aerial Vehicle Controlmentioning

confidence: 99%

“…In previous work, we have developed autonomous navigation controllers for fixed wing UAVs using multiobjective genetic programming [11]- [13]. The goal is for a UAV to autonomously locate, track, and then orbit around a radar site.…”

Section: Unmanned Aerial Vehicle Controlmentioning

confidence: 99%

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Transference of Evolved Unmanned Aerial Vehicle Controllers to a Wheeled Mobile Robot

Barlow

Mattos

Grant

et al.

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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Abstract-Transference of controllers evolved in simulation to real vehicles is an important issue in evolutionary robotics (ER). We have previously evolved autonomous navigation controllers for fixed wing UAV applications using multiobjective genetic programming (GP). Controllers were evolved to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. We successfully tested an evolved UAV controller on a wheeled mobile robot. A passive sonar system on the robot was used in place of the radar sensor, and a speaker emitting a tone was used as the target in place of a radar. Using the evolved navigation controller, the mobile robot moved to the speaker and circled around it. The results from this experiment demonstrate that our evolved controllers are capable of transference to real vehicles. Future research will include testing the best evolved controllers by using them to fly real UAVs.

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Incremental evolution of autonomous controllers for unmanned aerial vehicles using multi-objective genetic programming

Cited by 33 publications

References 8 publications

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Embodied artificial life at an impasse can evolutionary robotics methods be scaled?

Transference of Evolved Unmanned Aerial Vehicle Controllers to a Wheeled Mobile Robot

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