Periodic Reporting for period 2 - SMART (Soft, Self-responsive, Smart MAterials for RoboTs)
Reporting period: 2022-03-01 to 2024-08-31
The overall objective of the SMART project is to train 15 Early Stage Researchers (ESRs) with a training through research in the fields of soft robotics with innovative smart materials having self-healing, sensing and actuation properties.
In this field of soft robotics, the following problems have been identified:
1: Robotic systems are typically dimensioned to be able to withstand occasional extreme loads, instead of being designed based on their performance tasks. This over-dimensioning, resulting in heavy and oversized robotic systems, is a first problem that is being addressed. A lighter and more compact system is needed.
2: Robotic designs become more complex as their tasks and performance become ever more complicated and demanding. The composing parts and components in such complex robotic systems become less accessible for maintenance. When a component fails, large parts of the robotic system have to be disassembled in order to replace it, which is a very costly and time-consuming intervention done by specialists.
3: Animals exploit soft structures to move in complex environments. These capabilities have inspired robotic engineers to incorporate soft technologies into actuator designs, and led to the development of smart actuators. Standard control schemes are not suitable for soft robots due to the inherent deformability of the material. This makes the derivation of accurate and tractable dynamic models and their control principles challenging and innovative solutions have to be developed.
4: Smart materials with sensing, actuation and self-healing capabilities provide the necessary tools to overcome or mitigate the previous problems. However, these smart materials are still in full development. Preliminary experiments proved that producing robotic parts with smart materials is feasible, but dedicated materials are missing.
5: Robotics can provide an innovative breakthrough in materials science by lifting smart materials into fully functional smart systems. This full integration is still not a reality and further breakthroughs are needed.
The overall long-term research objective of the SMART project is the ambitious breakthrough to develop a material-oriented solution for smart soft structures. By integrating engineered functional materials, we are developing soft robotic systems to sense, actuate and heal damages so the soft robot can interact with a dynamic and unknown environment while being able to self-heal incurred damage due to fatigue, overloading, and sharp objects present in the environment or by human contact.
This aim can be divided into 3 specific research objectives (RO):
RO.1: Development, characterization and tuning of stimuli-responsive materials with smart, adaptive and self-healing properties for industrial and commercial applications, designed using greener chemistries and for dedicated, user-defined properties and functionalities.
RO.2: Development and optimization of manufacturing processes for complex geometries and intelligent design. Development of smart actuator/sensor systems with dedicated smart control and response system through artificial intelligence and machine learning techniques.
RO.3: Development of fully autonomous smart soft robotic demonstrators and derived applications.
In this field of soft robotics, the following problems have been identified:
1: Robotic systems are typically dimensioned to be able to withstand occasional extreme loads, instead of being designed based on their performance tasks. This over-dimensioning, resulting in heavy and oversized robotic systems, is a first problem that is being addressed. A lighter and more compact system is needed.
2: Robotic designs become more complex as their tasks and performance become ever more complicated and demanding. The composing parts and components in such complex robotic systems become less accessible for maintenance. When a component fails, large parts of the robotic system have to be disassembled in order to replace it, which is a very costly and time-consuming intervention done by specialists.
3: Animals exploit soft structures to move in complex environments. These capabilities have inspired robotic engineers to incorporate soft technologies into actuator designs, and led to the development of smart actuators. Standard control schemes are not suitable for soft robots due to the inherent deformability of the material. This makes the derivation of accurate and tractable dynamic models and their control principles challenging and innovative solutions have to be developed.
4: Smart materials with sensing, actuation and self-healing capabilities provide the necessary tools to overcome or mitigate the previous problems. However, these smart materials are still in full development. Preliminary experiments proved that producing robotic parts with smart materials is feasible, but dedicated materials are missing.
5: Robotics can provide an innovative breakthrough in materials science by lifting smart materials into fully functional smart systems. This full integration is still not a reality and further breakthroughs are needed.
The overall long-term research objective of the SMART project is the ambitious breakthrough to develop a material-oriented solution for smart soft structures. By integrating engineered functional materials, we are developing soft robotic systems to sense, actuate and heal damages so the soft robot can interact with a dynamic and unknown environment while being able to self-heal incurred damage due to fatigue, overloading, and sharp objects present in the environment or by human contact.
This aim can be divided into 3 specific research objectives (RO):
RO.1: Development, characterization and tuning of stimuli-responsive materials with smart, adaptive and self-healing properties for industrial and commercial applications, designed using greener chemistries and for dedicated, user-defined properties and functionalities.
RO.2: Development and optimization of manufacturing processes for complex geometries and intelligent design. Development of smart actuator/sensor systems with dedicated smart control and response system through artificial intelligence and machine learning techniques.
RO.3: Development of fully autonomous smart soft robotic demonstrators and derived applications.
From the beginning of the SMART-ITN project to the end of the reporting period, 15 ESRs have been recruited and based on their individual background, individualized Personal Career Development Plans (PCDP) for each of them have been prepared. Each PCDP contains a detailed workplan both in their projected scientific research and also on the individual training. Each ESR has begun working on their individual scientific projects along with a highly multidisciplinary training program provided by the ITN.
ESRs are being trained as a new generation of young European scientists and engineers at the EU level strengthening EU innovation capacity with industrially hands-on expertise and experience. In addition to their individual training programs in their host institutions, SMART training activities have already offered:
-All proposed training events were completed and successfully finalized. Based on their PCDPs, ESRs have attended enough training so far to collect over 180 ECTS credits, as proposed.
-All planned industrial and academic secondments were finalized by eligible ESRs.
-SMART consortium has published a total of 56 publications (43 articles in academic journals, 13 publications in conference proceeding/workshops) all international peer-reviewed and open access (33 gold and 23 green access).
-In addition to the achieved 9 milestones, a total of 30 deliverables (11 deliverables for scientific workpackages, 6 deliverables for the training activities, 2 deliverables for the dissemination and communication activities, 1 for ethics and 10 deliverables for the successful management of the action) were submitted via the EU portal.
-As promised in DMP, all meeting minutes and presentations are gathered in Sharepoint and will be stored 5 more years.
-Communication, dissemination and exploitation activities of the project have extensive impact on the scientific community, on the relevant industry and on the general public.
In terms of the scientific achievements, each ESR continues making progress on their individual projects in collaboration with each other. Below image (Figure 2) shows an outline of the scientific progress of SMART ESRs on the 3 Workpackages (WP).
ESRs are being trained as a new generation of young European scientists and engineers at the EU level strengthening EU innovation capacity with industrially hands-on expertise and experience. In addition to their individual training programs in their host institutions, SMART training activities have already offered:
-All proposed training events were completed and successfully finalized. Based on their PCDPs, ESRs have attended enough training so far to collect over 180 ECTS credits, as proposed.
-All planned industrial and academic secondments were finalized by eligible ESRs.
-SMART consortium has published a total of 56 publications (43 articles in academic journals, 13 publications in conference proceeding/workshops) all international peer-reviewed and open access (33 gold and 23 green access).
-In addition to the achieved 9 milestones, a total of 30 deliverables (11 deliverables for scientific workpackages, 6 deliverables for the training activities, 2 deliverables for the dissemination and communication activities, 1 for ethics and 10 deliverables for the successful management of the action) were submitted via the EU portal.
-As promised in DMP, all meeting minutes and presentations are gathered in Sharepoint and will be stored 5 more years.
-Communication, dissemination and exploitation activities of the project have extensive impact on the scientific community, on the relevant industry and on the general public.
In terms of the scientific achievements, each ESR continues making progress on their individual projects in collaboration with each other. Below image (Figure 2) shows an outline of the scientific progress of SMART ESRs on the 3 Workpackages (WP).
As a training network, SMART will continue providing multidisciplinary training events in the field of Soft Robotics with innovative smart materials having self-responsive properties. 4 more training events will be organized by the consortium (14 days in total and 21 ECTS credits). Next training event will be based on transferrable skills, project management, career exploitation and entrepreneurship and a business event. In the following reporting period, each ESR will continue making progress to ensure completing a minimum 180 ECTS credits while adhering to local requirements to obtain a PhD degree.
In the upcoming reporting period, industrial partners will be more involved with the researchers during their industrial secondments of 48 months.
Expected scientific progress is the development of complete robotic systems using smart materials that are able to:
* Exploit smart softness/actuation to interact with the environment, especially in manipulation and locomotion
* “Feel pain” through identification of damage by direct sensing or evaluation of a loss of functionality
* “Relieve pain” by moving towards a position that avoids further damage using machine learning and Artificial Intelligence (AI) concepts
* “Recover from pain” by taking measures on restoring all functions through:
- Autonomous healing or the application of a controlled stimulus to activate the healing action,
- Rehabilitation of the damage to verify and evaluate if the damage and lost functionalities are effectively restored,
- Calibration to guarantee the system can go back to work in a reliable way.
In the upcoming reporting period, industrial partners will be more involved with the researchers during their industrial secondments of 48 months.
Expected scientific progress is the development of complete robotic systems using smart materials that are able to:
* Exploit smart softness/actuation to interact with the environment, especially in manipulation and locomotion
* “Feel pain” through identification of damage by direct sensing or evaluation of a loss of functionality
* “Relieve pain” by moving towards a position that avoids further damage using machine learning and Artificial Intelligence (AI) concepts
* “Recover from pain” by taking measures on restoring all functions through:
- Autonomous healing or the application of a controlled stimulus to activate the healing action,
- Rehabilitation of the damage to verify and evaluate if the damage and lost functionalities are effectively restored,
- Calibration to guarantee the system can go back to work in a reliable way.