Famously, the Battle of the Bulge in the winter of 1944/45 was one of the largest and bloodiest armed conflict of the Second World War. Taking place in densely forested Ardennes region between Belgium and Luxembourg, it was the last major German offensive campaign on the Western Front during World War II. Researchers have used drone-mounted LiDAR – which emits pulses of light to create 3D models and maps – to'see through' the thick forest canopy. They found nearly 1,000 features within the landscape, including dugouts, bomb craters and even artillery emplacements where troops positioned their guns. Pictured are LiDAR images from the study.

To achieve high-dexterity motion planning on floating-base systems, the base dynamics induced by arm motions must be treated carefully. In general, it is a significant challenge to establish a fixed-base frame during tasking due to forces and torques on the base that arise directly from arm motions (e.g. arm drag in low Reynolds environments and arm momentum in high Reynolds environments). While thrusters can in theory be used to regulate the vehicle pose, it is often insufficient to establish a stable pose for precise tasking, whether the cause be due to underactuation, modeling inaccuracy, suboptimal control parameters, or insufficient power. We propose a solution that asks the thrusters to do less high bandwidth perturbation correction by planning arm motions that induce zero perturbation on the base. We are able to cast our motion planner as a nonholonomic rapidly-exploring random tree (RRT) by representing the floating-base dynamics as pfaffian constraints on joint velocity. These constraints guide the manipulators to move on zero-perturbation manifolds (which inhabit a subspace of the tangent space of the internal configuration space). To invoke this representation (termed a \textit{perturbation map}) we assume the body velocity (perturbation) of the base to be a joint-defined linear mapping of joint velocity and describe situations where this assumption is realistic (including underwater, aerial, and orbital environments). The core insight of this work is that when perturbation of the floating-base has affine structure with respect to joint velocity, it provides the system a class of kinematic reduction that permits the use of sample-based motion planners (specifically a nonholonomic RRT). We show that this allows rapid, exploration-geared motion planning for high degree of freedom systems in obstacle rich environments, even on floating-base systems with nontrivial dynamics.

The NIMBUS Lab at the University of Nebraska has been developing drones that have the unique ability to dig holes in the ground and then fill those holes with sensors. If this sounds like a complicated task, that's because it is: The drone needs to be able to carry a portable digging system a useful distance, locate a diggable spot, land, verify that the spot it thought was diggable is in fact diggable, dig a hole and install the sensor, and then fly off again. At IROS late last year, folks from the NIMBUS Lab presented a paper detailing a rather burly quadcopter that could carry an auger with an embedded sensor and use it to place the sensor in the ground (you can see a video of this in action here). And at ISER a few weeks later, they presented another paper on how the drone can autonomously figure out whether it's digging in a good spot or not. One of the biggest challenges to a system like this is that by the time you pack in the drilling rig and all the sensors and computers that the drone needs to operate autonomously, you'll be lucky if the thing will manage to keep itself aloft for more than just a few minutes.