8 accepted papers
This letter introduces the Cable Robot Simulation and Control (CaRoSaC) Framework, which integratesa realistic simulation environment with a model-free reinforcement learning control methodology for suspended Cable-Driven Parallel Robots (CDPRs), accounting for the effects of cable sag. Our approach
This letter re-visits the problem of visual-inertial navigation system (VINS) and presents a novel filter design we dub the multi state constraint <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">equivariant</i> filter (MSC <italic xmlns:mml="http://w
The capability to extract task specific, semantic information from raw sensory data is a crucial requirement for many applications of mobile robotics. Autonomous inspection of critical infrastructure with Unmanned Aerial Vehicles (UAVs), for example, requires precise navigation relative to the struc…
Collaboratively estimating the state of two robots under communication constraints is challenging regarding computational complexity and statistical optimality. Previous work only achieves practical solutions by either disregarding parts of the measurements or imposing a communication overhead, bein…
Suspended Cable-Driven Parallel Robots (SCDPR) have intriguing capabilities on large scales but still have open challenges in precisely estimating the end-effector pose. The cables exhibit a downward curved shape, also known as cable sag which needs to be accounted for in the pose estimation. The ca…
Sensor fusion systems merging (multiple) delayed sensor signals through a statistical approach are challenging setups, particularly for resource constrained platforms. For statistical consistency, one would be required to keep an appropriate history, apply the correcting signal at the given time sta…
Delay compensation in filter based sensor fusion frameworks for multiple sensors with varying delays and different rates quickly results in large computational overhead should the delayed measurements be incorporated in a statistically meaningful way. Even more so if high rate propagation sensors (e…
Many of today's Visual-Inertial Odometry (VIO)frameworks work well under regular motion but have issues and need special treatment under special motion. Here, special does not imply bad or corrupted data but stands for increased difficulty to treat clean data. Common special motion for VIO are large…