Mostafa Wahby

Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-Organizing Systems

Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area.

Self-Organized Construction with Continuous Building Material: Higher Flexibility Based on Braided Structures

Self-organized construction with continuous, structured building material, as opposed to modular units, offers new challenges to the robot-based construction process and lends the opportunity for increased flexibility in constructed artifact properties, such as shape and deformation. As an example investigation, we look at continuous filaments organized into braided structures, within the context of bio-hybrids constructing architectural artifacts. We report the result of an early swarm robot experiment. The robots successfully constructed a braid in a self-organized process. The construction process can be extended by using different materials and by embedding sensors during the self-organized construction directly into the braided structure. In future work, we plan to apply dedicated braiding robot hardware and to construct sophisticated 3-d structures with local variability in patterns of filament interlacing.

An Evolutionary Robotics Approach to the Control of Plant Growth and Motion: Modeling Plants and Crossing the Reality Gap

The self-organizing bio-hybrid collaboration ofrobots and natural plants allows for a variety of interestingapplications. As an example we investigate how robots can beused to control the growth and motion of a natural plant, using LEDs to provide stimuli. We follow an evolutionaryrobotics approach where task performance is determined bymonitoring the plants reaction. First, we do initial plantexperiments with simple, predetermined controllers. Then weuse image sampling data as a model of the dynamics ofthe plant tip xy position. Second, we use this approach toevolve robot controllers in simulation. The task is to makethe plant approach three predetermined, distinct points in anxy-plane. Finally, we test the evolved controllers in real plantexperiments and find that we cross the reality gap successfully. We shortly describe how we have extended from plant tipto many points on the plant, for a model of the plant stemdynamics. Future work will extend to two-axes image samplingfor a 3-d approach.

Evolved Control of Natural Plants: Crossing the Reality Gap for User-Defined Steering of Growth and Motion

Mixing societies of natural and artificial systems can provide interesting and potentially fruitful research targets. Here we mix robotic setups and natural plants in order to steer the motion behavior of plants while growing. The robotic setup uses a camera to observe the plant and uses a pair of light sources to trigger phototropic response, steering the plant to user-defined targets. An evolutionary robotic approach is used to design a controller for the setup. Initially, preliminary experiments are performed with a simple predetermined controller and a growing bean plant. The plant behavior in response to the simple controller is captured by image processing, and a model of the plant tip dynamics is developed. The model is used in simulation to evolve a robot controller that steers the plant tip such that it follows a number of randomly generated target points. Finally, we test the simulation-evolved controller in the real setup controlling a natural bean plant. The results demonstrate a successful crossing of the reality gap in the setup. The success of the approach allows for future extensions to more complex tasks including control of the shape of plants and pattern formation in multiple plant setups.

On the Tradeoff Between Hardware Protection and Optimization Success: A Case Study in Onboard Evolutionary Robotics for Autonomous Parallel Parking

Making the transition from simulation to reality in evolutionary robotics is known to be challenging. What is known as the reality gap, summarizes the set of problems that arises when robot controllers have been evolved in simulation and then are transferred to the real robot. In this paper we study an additional problem that is beyond the reality gap. In simulations, the robot needs no protection against damage, while on the real robot that is essential to stay cost-effective. We investigate how the probability of collisions can be minimized by introducing appropriate penalties to the fitness function. A change to the fitness function, however, changes the evolutionary dynamics and can influence the optimization success negatively. Therefore, we detect a tradeoff between a required hardware protection and a reduced efficiency of the evolutionary optimization process. We study this tradeoff on the basis of a robotics case study in autonomous parallel parking.

Pick, Pack, & Survive: Charging Robots in a Modern Warehouse based on Online Connected Dominating Sets

The modern warehouse is partially automated by robots. Instead of letting human workers walk into shelfs and pick up the required stock, big groups of autonomous mobile robots transport the inventory to the workers. Typically, these robots have an electric drive and need to recharge frequently during the day. When we scale this approach up, it is essential to place recharging stations strategically and as soon as needed so that all robots can survive. In this work, we represent a warehouse topology by a graph and address this challenge with the Online Connected Dominating Set problem (OCDS), an online variant of the classical Connected Dominating Set problem [Guha and Khuller, 1998]. We are given an undirected connected graph G = (V, E) and a sequence of subsets of V arriving over time. The goal is to grow a connected subgraph that dominates all arriving nodes and contains as few nodes as possible. We propose an O(log^2 n)-competitive randomized algorithm for OCDS in general graphs, where n is the number of nodes in the input graph. This is the best one can achieve due to Kormans randomized lower bound of Omega(log n log m) [Korman, 2005] for the related Online Set Cover problem [Alon et al., 2003], where n is the number of elements and m is the number of subsets. We also run extensive simulations to show that our algorithm performs well in a simulated warehouse, where the topology of a warehouse is modeled as a randomly generated geometric graph.

Autonomously shaping natural climbing plants: a bio-hybrid approach

Plant growth is a self-organized process incorporating distributed sensing, internal communication and morphology dynamics. We develop a distributed mechatronic system that autonomously interacts with natural climbing plants, steering their behaviours to grow user-defined shapes and patterns. Investigating this bio-hybrid system paves the way towards the development of living adaptive structures and grown building components. In this new application domain, challenges include sensing, actuation and the combination of engineering methods and natural plants in the experimental set-up. By triggering behavioural responses in the plants through light spectra stimuli, we use static mechatronic nodes to grow climbing plants in a user-defined pattern at a two-dimensional plane. The experiments show successful growth over periods up to eight weeks. Results of the stimuli-guided experiments are substantially different from the control experiments. Key limitations are the number of repetitions performed and the scale of the systems tested. Recommended future research would investigate the use of similar bio-hybrids to connect construction elements and grow shapes of larger size.