Mary Katherine Heinrich

Sinan Oğuz, Emanuele Garone, Marco Dorigo et al.

Intermittent faults are transient errors that sporadically appear and disappear. Although intermittent faults pose substantial challenges to reliability and coordination, existing studies of fault tolerance in robot swarms focus instead on permanent faults. One reason for this is that intermittent faults are prohibitively difficult to detect in the fully self-organized ad-hoc networks typical of robot swarms, as their network topologies are transient and often unpredictable. However, in the recently introduced self-organizing nervous systems (SoNS) approach, robot swarms are able to self-organize persistent network structures for the first time, easing the problem of detecting intermittent faults. To address intermittent faults in robot swarms that have persistent networks, we propose a novel proactive-reactive strategy to detection and mitigation, based on self-organized backup layers and distributed consensus in a multiplex network. Proactively, the robots self-organize dynamic backup paths before faults occur, adapting to changes in the primary network topology and the robots' relative positions. Reactively, robots use one-shot likelihood ratio tests to compare information received along different paths in the multiplex network, enabling early fault detection. Upon detection, communication is temporarily rerouted in a self-organized way, until the detected fault resolves. We validate the approach in representative scenarios of faulty positional data occurring during formation control, demonstrating that intermittent faults are prevented from disrupting convergence to desired formations, with high fault detection accuracy and low rates of false positives.

Weixu Zhu, Marco Dorigo, Mary Katherine Heinrich

Our recently introduced self-organizing nervous system (SoNS) provides robot swarms with 1) ease of behavior design and 2) global estimation of the swarm configuration and its collective environment, facilitating the implementation of online automatic code generation for robot swarms. In a demonstration with 6 real robots and simulation trials with >30 robots, we show that when a SoNS-enhanced robot swarm gets stuck, it can automatically solicit and run code generated by an external LLM on the fly, completing its mission with an 85% success rate.

Mostafa Wahby, Mary Katherine Heinrich, Daniel Nicolas Hofstadler et al.

Bio-hybrid systems---close couplings of natural organisms with technology---are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibilities of mixing robots with natural plants, merging useful attributes. Significant synergies arise by combining the plants' ability to efficiently produce shaped material and the robots' ability to extend sensing and decision-making behaviors. However, programming robots to control plant motion and shape requires good knowledge of complex plant behaviors. Therefore, we use machine learning to create a holistic plant model and evolve robot controllers. As a benchmark task we choose obstacle avoidance. We use computer vision to construct a model of plant stem stiffening and motion dynamics by training an LSTM network. The LSTM network acts as a forward model predicting change in the plant, driving the evolution of neural network robot controllers. The evolved controllers augment the plants' natural light-finding and tissue-stiffening behaviors to avoid obstacles and grow desired shapes. We successfully verify the robot controllers and bio-hybrid behavior in reality, with a physical setup and actual plants.

Heiko Hamann, Mohammad Divband Soorati, Mary Katherine Heinrich et al.

Key to our project flora robotica is the idea of creating a bio-hybrid system of tightly coupled natural plants and distributed robots to grow architectural artifacts and spaces. Our motivation with this ground research project is to lay a principled foundation towards the design and implementation of living architectural systems that provide functionalities beyond those of orthodox building practice, such as self-repair, material accumulation and self-organization. Plants and robots work together to create a living organism that is inhabited by human beings. User-defined design objectives help to steer the directional growth of the plants, but also the system's interactions with its inhabitants determine locations where growth is prohibited or desired (e.g., partitions, windows, occupiable space). We report our plant species selection process and aspects of living architecture. A leitmotif of our project is the rich concept of braiding: braids are produced by robots from continuous material and serve as both scaffolds and initial architectural artifacts before plants take over and grow the desired architecture. We use light and hormones as attraction stimuli and far-red light as repelling stimulus to influence the plants. Applied sensors range from simple proximity sensing to detect the presence of plants to sophisticated sensing technology, such as electrophysiology and measurements of sap flow. We conclude by discussing our anticipated final demonstrator that integrates key features of flora robotica, such as the continuous growth process of architectural artifacts and self-repair of living architecture.