This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. Furthermore, we account for the uncertainty of the robot's partial state by utilizing the Unscented Transform and the uncertainty of the neural network model by using Monte Carlo dropout. The uncertainty-aware collision cost is then combined with the goal direction given by a global planner in order to determine the best action sequence to execute in a receding horizon manner. To demonstrate the method, we develop a resilient small flying robot integrating lightweight sensing and computing resources. A set of simulation and experimental studies, including a field deployment, in both cluttered and perceptually-challenging environments is conducted to evaluate the quality of the prediction network and the performance of the proposed planner.

This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying robots and enables navigation in environments with steep slopes, and diverse geometries. When a communication link is available, each robot of the team shares submaps to a centralized location where a multi-robot coordination framework identifies global frontiers of the exploration space to inform each system about where it should re-position to best continue its mission. The strategy is verified through a field deployment inside an underground mine in Switzerland using a legged and a flying robot collectively exploring for 45 min, as well as a longer simulation study with three systems.

This paper contributes a novel strategy towards risk-aware motion planning for collision-tolerant aerial robots subject to localization uncertainty. Attuned to the fact that micro aerial vehicles are often tasked to navigate within GPS-denied, possibly unknown, confined and obstacle-filled environments the proposed method exploits collision-tolerance at the robot design level to mitigate the risks of collisions especially as their likelihood increases with growing uncertainty. Accounting for the maximum kinetic energy with which an impact is considered safe, alongside the robot dynamics, the planner builds a set of admissible uncertainty-aware and collision-inclusive paths over a horizon involving multiple motion steps. The first step of the best path is executed by the robot, while the procedure is then repeated in a receding horizon manner. Evaluated in extensive simulation studies and experimental results with a collision-tolerant flying robot, the planner successfully considers the interplay between uncertainty and the likelihood of a collision, balances the risks of possible impacts and enables to navigate safely within highly cluttered environments.

This paper contributes a method designed to enable autonomous distributed 3D nuclear radiation field mapping. The algorithm uses a single radiation sensor and a sequence of spatially distributed and robotically acquired radiation measurements across a discretized 3D grid to derive a radiation gradient. The derived gradient is probabilistically propagated to unknown components of the map to further guide a curiosity-driven path planner by identifying the next most radiologically informative point given available information. To demonstrate the method, we develop a resilient micro flying robot capable of autonomous GPS-denied navigation that integrates a Thallium–doped Cesium Iodide (CsI(Tl)) scintillator and Silicon Photomultiplier (SiPm) combined with custom–built pulse counting circuitry. A set of experimental studies is presented inside an indoor facility within which actual radioactive uranium ore sources have been distributed.

This work presents the design and autonomous navigation policy of the Resilient Micro Flyer, a new type of collision-tolerant robot tailored to fly through extremely confined environments and manhole-sized tubes. The robot maintains a low weight (<500g) and implements a combined rigid-compliant design through the integration of elastic flaps around its stiff collision-tolerant frame. These passive flaps ensure compliant collisions, contact sensing and smooth navigation in contact with the environment. Focusing on resilient autonomy, capable of running on resource-constrained hardware, we demonstrate the beneficial role of compliant collisions for the reliability of the onboard visual-inertial odometry and propose a safe navigation policy that exploits both collision-avoidance using lightweight time-of-flight sensing and adaptive control in response to collisions. The robot further realizes an explicit manhole navigation mode that exploits the direct mechanical feedback provided by the flaps and a special navigation strategy to self-align inside manholes with non-straight geometry. Comprehensive experimental studies are presented to evaluate, both individually and as a whole, how resilience is achieved based on the robot design and its navigation scheme.