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            <title xml:lang="en">Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming</title>
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                <title xml:lang="en">Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming</title>
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                  <date type="start">2010-10-06</date>
                  <date type="end">2010-10-07</date>
                  <settlement>Larnaca</settlement>
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                <editor>Harris Papadopoulos; Andreas S. Andreou; Max Bramer</editor>
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                <term xml:lang="en">Sensor Morphology</term>
                <term xml:lang="en">Corridor Following</term>
                <term xml:lang="en">Evolutionary Robotics</term>
                <term xml:lang="en">Genetic Programming</term>
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              <p>Genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. In this contribution we use genetic programming to automatically evolve efficient robot controllers for a corridor following task. Based on tests executed in a simulation environment we show that very robust and efficient controllers can be obtained. Also, we stress that it is important to provide sufficiently diverse fitness cases, offering a sound basis for learning more complex behaviour. The evolved controller is successfully applied to real environments as well. Finally, controller and sensor morphology are co-evolved, clearly resulting in an improved sensor configuration.</p>
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