The tracking problem (that is, how to follow a previously memorized path) is one of the most important problems in mobile robots. Several methods can be formulated depending on the way the robot state is related to the path. “Trajectory tracking” is the most common method, with the controller aiming to move the robot toward a moving target point, like in a real-time servosystem. In the case of complex systems or systems under perturbations or unmodeled effects, such as UAVs (Unmanned Aerial Vehicles), other tracking methods can offer additional benefits. In this paper, methods that consider the dynamics of the path’s descriptor parameter (which can be called “error adaptive tracking”) are contrasted with trajectory tracking. A formal description of tracking methods is first presented, showing that two types of error adaptive tracking can be used with the same controller in any system. Then, it is shown that the selection of an appropriate tracking rate improves error convergence and robustness for a UAV system, which is illustrated by simulation experiments. It is concluded that error adaptive tracking methods outperform trajectory tracking ones, producing a faster and more robust convergence tracking, while preserving, if required, the same tracking rate when convergence is achieved.

Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) is $O\left(log\right(M\times N\left)\right)$ , M and N being the height and width of the image. Efficient implementations of this parallel algorithm would allow images to be processed in real time, as well as permit us to uncover fast algorithms for contour detection and segmentation, opening new perspectives within the image processing field.

The Memory Wall remains a critical bottleneck in modern computing, as the performance gap between processors and memory continues to widen. Neuromorphic computing offers a promising non-von Neumann alternative by emulating the brain's efficiency. This work proposes a Neuromorphic Dual-Match Content-Addressable Memory model bio-inspired by the hippocampus implemented using Spiking Neural Networks on the SpiNNaker platform. The architecture introduces two major innovations: a synergy between soft and hard matching capabilities for exact-match pattern retrieval and the integration of a dopamine-inspired gating mechanism. This neuromodulatory signal effectively decouples learning from recall, eliminating synaptic toxicity and ensuring data integrity during intensive operations. To overcome hardware-specific constraints, such as synaptic weight saturation and core capacity, a distributed bank-based structure with a specialized padding codification is presented. The model's performance was validated through a novel quantitative methodology, simulating a fully-associative L1 cache lookup using real-world memory access traces from the SPEC17 benchmark obtained via the ChampSim simulator. Results demonstrate that the dopamine-gated mechanism elevates the retrieval hit rate from a baseline of 55.81% to an absolute 100% success rate, maintaining perfect integrity even under high-traffic scenarios. This scalable and deterministic framework transitions spiking associative memories from theoretical bio-models to robust computational components, providing a high-precision spiking RAM solution for next-generation neuromorphic systems and autonomous agents.