Industrial manipulators are increasingly used in manufacturing and packaging systems where the exact tasks are not known in advance. In such settings, task and motion planning (TAMP) must be performed online, while the resulting plans should maximize throughput and satisfy velocity, acceleration, and higher-order derivative constraints. This is especially challenging when multiple fast manipulators operate close together in a shared workspace, resulting in additional constraints on trajectories, task allocations, and orderings to avoid collisions.
Existing multi-agent TAMP methods often rely on discretized space and time, resulting in trajectories that cannot be executed directly by manipulators. On the other hand, continuous trajectory optimization methods can generate smooth trajectories, but they do not address task allocations and orderings, and either consider a single manipulator and/or are too slow for online planning.
We present a soft real-time TAMP framework for multiple fast manipulators. Tasks are generated from product and place positions, assigned to robots, and converted into dynamically feasible trajectories. Experiments compare polygonal bang-bang, polygonal smoothstep, and smooth optimized Bézier trajectories with continuity up to acceleration and jerk. The results show that the polygonal trajectories are the fastest to compute, while Bézier optimization reduces the makespan at the cost of increased planning time. Additionally, bang-bang initialization gives faster convergence than smoothstep and is thus a good starting point for fast Bézier optimization. The makespan improvement reduces per iteration, allowing for a relatively large improvement in minimal time. The proposed assignment methods are shown to be feasible for online planning resulting in few potential collisions, and a custom convex-hull-based collision checker is compared to a sample-based collision checker on the tradeoff between conservativeness and runtime. ... Read more

The severity of hip osteoarthritis is measured a.o. by the minimal distance between the femoral head and the acetabular roof in an X-ray image. However, the whole joint space profile might be a more accurate estimator, since it would include irregularities in the bone surface. These irregular bulges (osteophytes) on the bone surface are one of the signals that a person might have OA. Thus the stage of OA might be better estimated automatically by having this data in the joint space profile instead of just using the minimal joint space.

For this joint space profile, the distance between the femoral head and the acetabular roof needs to be calculated. Therefore, the positions of these parts in the hip joint are required to be know. These can be retrieved from e.g. a segmentation mask.

One way of calculating the distance in a joint is to use a radial projection. A radial projection is a way of projecting points from a curved space to a plane by projecting lines from a central point along increasing angles.

In this paper, we investigate how the joint space profile can be segmented most accurately from a radial projection originating from the center of the femoral head by several comparing noise filtering and edge-finding algorithms. After which is shown that a custom algorithm based on the theory behind edge detection in noisy images works most reliably and accurately.

There are still multiple points of improvement for this algorithm. The femoral head can be segmented more accurately than the acetabular roof, the segmentation of the latter could be optimized by detecting the brightest line (peaks) instead of the most sudden change (steepest gradient) in the X-ray image as the edge for the femoral head. The algorithm could be further improved by taking care of local outliers off those edges.

In conclusion, this paper compares multiple ways of segmenting the joint space of the hip joint. The best-performing algorithm could in the future be used in an assisting tool for doctors to highlight important irregularities and measurements in the hip joint space. ... Read more