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Designing robotic systems that can change their physical form factor as well as their compliance to adapt to environmental constraints remains a major conceptual and technical challenge. To address this, we introduce the Granulobot, a modular system that blurs the distinction between soft, modular, and swarm robotics. The system consists of gear-like units that each contain a single actuator such that units can self-assemble into larger, granular aggregates using magnetic coupling. These aggregates can reconfigure dynamically and also split into subsystems that might later recombine. Aggregates can self-organize into collective states with solid- and liquid-like properties, thus displaying widely differing compliance. These states can be perturbed locally via actuators or externally via mechanical feedback from the environment to produce adaptive shape-shifting in a decentralized manner. This, in turn, can generate locomotion strategies adapted to different conditions. Aggregates can move over obstacles without using external sensors or coordinates to maintain a steady gait over different surfaces without electronic communication among units. The modular design highlights a physical, morphological form of control that advances the development of resilient robotic systems with the ability to morph and adapt to different functions and conditions. 

This research project aims to achieve a future urban environment where people and self-driving cars coexist together while guaranteeing safety. To modify the environment, our first approach is to understand the limitations of GPS/GNSS positioning in an urban area where signal blockages and reflections make positioning difficult. For the evaluation process, we assume reasonable integrity requirements and calculate navigation availability along a sample Chicago urban corridor (State Street). We reject all non-line-of-sight (NLOS) that are blocked and reflected using a 3-D map. The availability of GPS-only positioning is determined to be less than 10% at most locations. Using four full GNSS constellations, availability improves significantly but is still lower than 80 % at certain points. The results establish the need for integration with other navigation sensors, such as inertial navigation systems (INS) and Lidar, to ensure integrity. The analysis methods introduced will form the basis to determine performance requirements for these additional sensors. 

Monitoring localization safety will be necessary to certify the performance of robots that operate in life-critical applications, such as autonomous passenger vehicles or delivery drones because many current localization safety methods do not account for the risk of undetected sensor faults. One type of fault, misassociation, occurs when a feature extracted from a mapped landmark is associated to a non-corresponding landmark and is a common source of error in feature-based navigation applications. This paper accounts for the probability of misassociation when quantifying landmark-based mobile robot localization safety for fixed-lag smoothing estimators. We derive a mobile robot localization safety bound and evaluate it using simulations and experimental data in an urban environment. Results show that localization safety suffers when landmark density is relatively low such that there are not enough landmarks to adequately localize and when landmark density is relatively high because of the high risk of feature misassociation. 

This paper describes a new type of compliant and configurable soft robot, a boundary-constrained swarm. The robot consists of a sealed flexible membrane that constrains both a number of mobile robotic subunits and passive granular material. The robot can change the volume fraction of the sealed membrane by applying a vacuum, which gives the robot the ability to operate in two distinct states: compliant and jammed. The compliant state allows the robot to surround and conform to objects or pass through narrow corridors. Jamming allows the robot to form a desired shape; grasp, (a) manipulate, and exert relatively high forces on external objects; and achieve relatively higher locomotion speeds. Locomotion is achieved with a combination of whegs (wheeled legs) and vibration motors that are located on the robotic subunits. The paper describes the mechanical design of the robot, the control methodology, and its object handling capability.