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With the continuous progress in robotics and application of such systems in evermore scenarios, safety and flexibility become increasingly important aspects and new designs should thus emphasize real-time capability and modularity. This work points out all related topics for such an endeavor and proclaims to move from conventional bottom-up design to more holistic approaches. Based on experience gained with the modular mini robot platforms BeBot and AMiRo, a novel generic modular architecture is proposed that offers high flexibility and system wide real-time capability.

Which attributes turn a robotic system from a dumb machine into a cognitive entity? This and many related questions are investigated in this work, starting with the definition of the term cognition. In order to describe and compare robots in an as general way as possible, a novel metric is introduced, defining twelve attribute classes. Furthermore, the process of creating a new robot is described from a systems engineering perspective as a seven stage procedure and difficulties that arise during practical realization of a new platform are outlined, thereby identifying the so-called dependency dilemma. The proposed solution to resolve these issues is throughout modularization of hardware and software. If done right, this approach has the potential to accelerate progress in robotics and cognitive science by making platforms much more flexible, and motivating for more cooperation regarding development and experimentation within an even broader community.

Traditional approaches to mapping of environments in robotics make use of spatially discretized representations, such as occupancy grid maps. Modern systems, e.g. in agriculture or automotive applications, are equipped with a variety of different sensors to gather diverse process-relevant modalities from the environment. The amount of data and its associated semantic information demand for broader data structures and frameworks, like semantical occupancy grid maps (SOGMs). This multi-modal representation also calls for novel methods of path planning. Due to the sequential nature of path planning as a consecutive execution of tasks and their ability to handle multi-modal data as provided by SOGMs, Markovian models, such as Hidden Markov Models (HMM) or Partially Observable Markov Decision Processes, are applicable. Furthermore, for these techniques to be applied effectively and efficiently, data from SOGMs must be extracted and refined. Superpixel algorithms, originating from computer vision, provide a method to de-noise and re-express SOGMs in an alternative representation. This publication explores and extends the use of superpixel segmentation as a post-processing step and applies Markovian models for path decoding on SOGMs., Comment: ICRA 2018 Workshop on Informative Path Planning and Adaptive Sampling, 2 pages, 4 figures