{"id":{"repo_id":"greenwich","oai_identifier":"oai:gala.gre.ac.uk:44075"},"canonical_url":"https://search.dev.ndltd.org/etd/greenwich/oai:gala.gre.ac.uk:44075","repository":{"repo_id":"greenwich","name":"University of Greenwich","base_url":"https://gala.gre.ac.uk/cgi/oai2"},"display":{"title":"Towards realising multimodal robots","abstract":"Evolutionary Algorithms (EAs) have been applied in co-evolutionary robotics over the last quarter century. They simultaneously generate the robot body morphology and controller algorithm through artificial evolution. However, the literature shows that in this area, it is still not possible to generate robots that perform better than conventional manual designs, even for simple tasks. This thesis describes steps undertaken to improve the co-evolution process. The investigation concerns two key areas of co-evolutionary robotics. The first is in fitness function development, which plays an integral part in selecting parents for mating during evolution. The effect of an incremental fitness function based on established algorithmic techniques from specific task domains of robotics is studied. An A-star algorithm-based fitness function for path planning is designed and implemented for co- evolution of robots for navigation and obstacle avoidance. Results show that the trajectories of robots that reach a goal using the A-star algorithm-based fitness function are shorter than the robots evolved with a basic distance-based fitness function. The second area of study is the role of the controller evolution in the co-evolution process. Inspired by natural paradigms of evolution coupled with learning in biological organisms, a Reinforced Co-evolutionary Algorithm (ReCoAl) is proposed. ReCoAl works by allowing a direct policy gradient based Reinforcement Learning algorithm to improve the controller of every evolved robot to better utilise the available morphological resources before the fitness evaluation. The findings indicate that the learning process has both positive and negative effects on the progress of evolution, similar to observations in evolutionary biology.","abstract_html":"Evolutionary Algorithms (EAs) have been applied in co-evolutionary robotics over the last quarter century. They simultaneously generate the robot body morphology and controller algorithm through artificial evolution. However, the literature shows that in this area, it is still not possible to generate robots that perform better than conventional manual designs, even for simple tasks. This thesis describes steps undertaken to improve the co-evolution process. The investigation concerns two key areas of co-evolutionary robotics. The first is in fitness function development, which plays an integral part in selecting parents for mating during evolution. The effect of an incremental fitness function based on established algorithmic techniques from specific task domains of robotics is studied. An A-star algorithm-based fitness function for path planning is designed and implemented for co- evolution of robots for navigation and obstacle avoidance. Results show that the trajectories of robots that reach a goal using the A-star algorithm-based fitness function are shorter than the robots evolved with a basic distance-based fitness function. The second area of study is the role of the controller evolution in the co-evolution process. Inspired by natural paradigms of evolution coupled with learning in biological organisms, a Reinforced Co-evolutionary Algorithm (ReCoAl) is proposed. ReCoAl works by allowing a direct policy gradient based Reinforcement Learning algorithm to improve the controller of every evolved robot to better utilise the available morphological resources before the fitness evaluation. The findings indicate that the learning process has both positive and negative effects on the progress of evolution, similar to observations in evolutionary biology.","abstract_has_math":false,"creators":["Radhakrishna Prabhu, Shanker Ganesh"],"institution":"University of Greenwich","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Kyberd, J","Seals, Richard"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-02","date_published":"2019-02","updated_at":"2026-07-24T02:25:58Z","subjects":["Q Science (General)"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kyberd, J","Seals, Richard"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Vice Chancellor's PhD Scholarship"]},{"key":"dc:creator","label":"Author","values":["Radhakrishna Prabhu, Shanker Ganesh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02"]},{"key":"dc:date.issued","label":"Date","values":["2019-02"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Faculty of Engineering and Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Greenwich"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://gala.gre.ac.uk/id/eprint/44075/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Q Science (General)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://gala.gre.ac.uk/id/eprint/44075/1/Shanker%20Ganesh%20Radhakrishna%20Prabhu%202019.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Evolutionary Algorithms (EAs) have been applied in co-evolutionary robotics over the last quarter century. They simultaneously generate the robot body morphology and controller algorithm through artificial evolution. However, the literature shows that in this area, it is still not possible to generate robots that perform better than conventional manual designs, even for simple tasks. This thesis describes steps undertaken to improve the co-evolution process. The investigation concerns two key areas of co-evolutionary robotics. The first is in fitness function development, which plays an integral part in selecting parents for mating during evolution. The effect of an incremental fitness function based on established algorithmic techniques from specific task domains of robotics is studied. An A-star algorithm-based fitness function for path planning is designed and implemented for co- evolution of robots for navigation and obstacle avoidance. Results show that the trajectories of robots that reach a goal using the A-star algorithm-based fitness function are shorter than the robots evolved with a basic distance-based fitness function. The second area of study is the role of the controller evolution in the co-evolution process. Inspired by natural paradigms of evolution coupled with learning in biological organisms, a Reinforced Co-evolutionary Algorithm (ReCoAl) is proposed. ReCoAl works by allowing a direct policy gradient based Reinforcement Learning algorithm to improve the controller of every evolved robot to better utilise the available morphological resources before the fitness evaluation. The findings indicate that the learning process has both positive and negative effects on the progress of evolution, similar to observations in evolutionary biology."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards realising multimodal robots"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kyberd, J","Seals, Richard"],"dc:contributor.sponsor":["Vice Chancellor's PhD Scholarship"],"dc:creator":["Radhakrishna Prabhu, Shanker Ganesh"],"dc:date":["2019-02"],"dc:date.issued":["2019-02"],"dc:description.abstract":["Evolutionary Algorithms (EAs) have been applied in co-evolutionary robotics over the last quarter century. They simultaneously generate the robot body morphology and controller algorithm through artificial evolution. However, the literature shows that in this area, it is still not possible to generate robots that perform better than conventional manual designs, even for simple tasks. This thesis describes steps undertaken to improve the co-evolution process. The investigation concerns two key areas of co-evolutionary robotics. The first is in fitness function development, which plays an integral part in selecting parents for mating during evolution. The effect of an incremental fitness function based on established algorithmic techniques from specific task domains of robotics is studied. An A-star algorithm-based fitness function for path planning is designed and implemented for co- evolution of robots for navigation and obstacle avoidance. Results show that the trajectories of robots that reach a goal using the A-star algorithm-based fitness function are shorter than the robots evolved with a basic distance-based fitness function. The second area of study is the role of the controller evolution in the co-evolution process. Inspired by natural paradigms of evolution coupled with learning in biological organisms, a Reinforced Co-evolutionary Algorithm (ReCoAl) is proposed. ReCoAl works by allowing a direct policy gradient based Reinforcement Learning algorithm to improve the controller of every evolved robot to better utilise the available morphological resources before the fitness evaluation. The findings indicate that the learning process has both positive and negative effects on the progress of evolution, similar to observations in evolutionary biology."],"dc:format":["application/pdf"],"dc:identifier.uri":["https://gala.gre.ac.uk/id/eprint/44075/1/Shanker%20Ganesh%20Radhakrishna%20Prabhu%202019.pdf"],"dc:language":["en"],"dc:publisher.department":["Faculty of Engineering and Science"],"dc:publisher.institution":["University of Greenwich"],"dc:relation.isreferencedby":["https://gala.gre.ac.uk/id/eprint/44075/"],"dc:subject":["Q Science (General)"],"dc:title":["Towards realising multimodal robots"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T02:25:58Z"}