Challenge 3: Sustainability through automation

Contributors: A. Censi, F. Corman, E. Frazzoli, N. Geroliminis, J. Lygeros

We are developing mathematical models, methods and simulations for ride-sharing systems that allow riders to switch between modes or drivers at a designated transfer hub – addressing the problem of the need for similar itineraries between matched drivers and riders. We are also looking at developing policies to manage rapidly changing demands in transport networks (i.e., bridging the gap between reality and the current state of the theory, which is based on static or equilibrium conditions).

Contributors: A. Censi, F. Corman, E. Frazzoli, N. Geroliminis, G. Hug

The communications capabilities baked into future mobility, combined with new control solutions, will open up new possibilities for infrastructure design. Meanwhile, the accelerating electrification of transport puts new pressure on the power grid. We tackle the problem of how to design vehicle charging infrastructure, taking into account a wide range of operational and planning factors.

Contributors: A. Censi, E. Frazzoli, N. Geroliminis, D. Kuhn

Traditional last-mile delivery using large vans comes with congestion, parking and access problems. The proposed alternative of crowd-shipping relies on individuals (typically cyclists) making slight adjustments to their itineraries to collect and deliver packages. We are investigating transfer points (to reduce the match needed between short planned bike trips and long package journeys across a city), dynamic pricing strategies, and integration with wider transport systems (including ride-sharing as well as mass transit), and working toward a pilot study to test real-world applicability.

Contributors: E. Balta, A. Karimi, J. Lygeros, A. Rupenyan

The complex physical and/or chemical phenomena inherent in many manufacturing processes pose an insurmountable challenge for pure model-based approaches, which may be too complex or inaccurate to be useful. We are using data-driven methods to fill the gap.

Contributors: E. Balta, J. Lygeros, A. Rupenyan

Beyond process-level control, we aim to develop design principles to, on one hand, abstract information from the process level to inform system-level decisions (demand forecasting, supply-chain management and so on), and on the other, to pass high-level control objectives to the process level. This involves game theory challenges such as competitive behaviour or information sharing. Our long-term goal is to achieve a holistic optimisation approach across levels within the manufacturing ecosystem, supporting improvements in sustainability along with other optimisation goals such as manufacturing cost and resilience.

Contributors: E. Balta, P. Heer, A. Rupenyan

Digital twins (representations of a system that change along with the real system, through a stream of data updates and adaptations) hold great potential for operational aspects such as predictive maintenance, planning and more. As the digital twin is adjusted using process data, the underlying control algorithm needs to be adapted. This provides an ideal testing ground for the research into lifelong learning and control described under Theme 1.4.

Contributors: P. Heer, S. Mastellone, A. Rupenyan

The large-scale introduction of power electronics converters in energy and industrial processes has transformed these applications into highly dynamic, interactive systems. Since these technologies can serve diverse uses, with different temporal and spatial scales, it is useful to develop adaptive frameworks (with various levels of abstraction) that can be employed in various use cases. We aim to define a unified framework to address the complexity of regulating the power flow across the mechanical and electrical parts, to ensure the overall security of supply. On larger scales, we are looking to achieve better understanding of the exchange of relevant information from and to the lower levels, in order to abstract from relevant data with negligible information loss. This involves modular thinking that unifies multiple disciplines, from engineering to economics and policy.

Contributors: P. Heer, G. Hug, C. Jones, J. Lygeros, S. Mastellone, V. Medici, D. Shaw

Switzerland already has the regulatory framework to support local coordination of energy prosumers, though this is not yet widely implemented. Through a so-called ZEV (Zusammenschluss zum Eigenverbrauch), neighbours can act (and be billed by the utility) as a single entity. Within this framework, we aim to (1) derive an approach to form building clusters that can minimise central balancing needs; (2) automate the coordination between houses, for efficiency and privacy; (3) define a mechanism to incentivise effective coordination between the clusters. We will also consider the arising ethical questions, such as the risk that incentives may favour certain already privileged groups, and whether markets are the fairest and most ethical mechanism for coordinating energy distribution.

Contributors: A. Censi, E. Frazzoli, P. Heer, G. Hug

In planning a building’s energy systems, decisions must unavoidably be based on many assumptions and simplifications. At this early stage, of course no data has yet been collected on actual use; nor is it possible to foresee how needs may evolve over the 50-plus years of a building’s lifespan. We aim to develop efficient computational tools to improve decision making (taking into account flexibility and redundancy measures to ensure reliability in a highly automated system), and crucially, to provide the transparency (now lacking in data-driven control techniques) that will enable operators to comprehend the reasoning behind automated processes, improving trust as well as troubleshooting.

Contributors: M. Hutter, C. Jones, M. Zeilinger

Legged locomotion control over rough terrain presents major challenges, owing to the complexity and uncertainty of the system dynamics as well as limited computational power. Combining the complementary approaches of MPC and reinforcement learning, we aim to develop perception-aware controllers (with a vision-based MPC helping to compensate for sensor limitations), test them using the quadrupedal robot ANYmal available at ETH Zurich, and benchmark the work in simulation and real-world experiments against existing approaches.

Contributors: A. Censi, E. Frazzoli, P. Heer, D. Shaw, M. Zeilinger

As machine learning proliferates, we continue to wrestle with the dilemma of how to evaluate machine-made decisions, which lack transparency and explainability. For instance, we urgently need to be able to understand decisions made by autonomous vehicles, for legal and liability reasons. Building on previous work at the NCCR Automation, we will investigate the contrasting approaches of a “rulebook” orientation (with decision making constrained by clearly defined hierarchical rules) or data-driven learning approach (which may better accommodate future uncertainties), with the goal of informing future developments in autonomous decision-making systems. We are also researching the ethics of interaction between humans and humanoid robots, specifically in the context of elderly care.

Contributors: A. Censi, E. Frazzoli, M. Hutter

The third robotic revolution will see the integration of mobile robotic systems in unstructured everyday environments, ultimately replacing humans in a range of dangerous and tedious jobs. We are investigating legged mobile manipulator systems for inspection and maintenance tasks: starting with end-to-end systems for navigation and locomotion, we will expand the agent’s skillset to combined locomotion and manipulation, using reinforcement learning to achieve unprecedented versatility and agility. The ultimate goal is to develop a multi-agent solution for coordinating a fleet of robots.