Modern algorithms allow robots to reach a greater level of autonomy and fulfill more challenging tasks. However, on-board limitations regarding computational and battery resources are hindering factors regarding the deployment of such algorithms particularly on mobile robots. Although offloading a majority of the algorithmic components to the edge or even cloud offers an attractive option to leverage massive computing power in robotics applications, safety and reliability remain critical issues. This paper presents a minimalistic safety fallback mechanism when offloading mobile robot navigation to the edge, that ensures safe and collision-free navigation even in the presence of failures in the connection between the on-board and edge-devices. We show the effectiveness of our approach through extensive testing in three different relevant scenarios in a simulated warehouse environment. Our experiments demonstrate the effects of different fallback strategies and show how our proposed approach is able to ensure safety while allowing the robot to continue its mission during an interrupted connection and thus avoiding unnecessary downtime.

Offloading software modules to the edge/cloud can enhance a robot’s capabilities by leveraging massive computing power. However, determining which software module should be offloaded and scheduled to which robot/edge/cloud node is a challenging task, particularly for robot fleets with diverse tasks. In this paper, we tackle the software scheduling problem and introduce a taxonomy to categorize software modules and classify their applicability and requirements for offloading. Additionally, by using prior measurements, we model the compute cluster and formalize software scheduling as a multi-objective optimization problem which we tackle with a genetic algorithm. To evaluate our approach with a challenging setup, we build a mobile manipulation task using open-source frameworks and libraries in the Robot Operating System (ROS2) community in simulation as well as a mildly simplified real-world variant. Our evaluation shows significant improvements compared to the built-in scheduler of Kubernetes (K8s) regarding robotic specific metrics such as the rate of missed cycle time in both simulated and real-world experiments.

In this paper, we present a novel approach for driving style classification based on time series data. Instead of automatically learning the embedding vector for temporal representation of the input data with Recurrent Neural Networks, we propose a combination of Hyperdimensional Computing (HDC) for data representation in high-dimensional vectors and much simpler feed-forward neural networks. This approach provides three key advantages: first, instead of having a “black box” of Recurrent Neural Networks learning the temporal representation of the data, our approach allows to encode this temporal structure in high-dimensional vectors in a human-comprehensible way using the algebraic operations of HDC while only relying on feed-forward neural networks for the classification task. Second, we show that this combination is able to achieve at least similar and even slightly superior classification accuracy compared to state-of-the-art Long Short-Term Memory (LSTM)-based networks while significantly reducing training time and the necessary amount of data for successful learning. Third, our HDC-based data representation as well as the feed-forward neural network, allow implementation in the substrate of Spiking Neural Networks (SNNs). SNNs show promise to be orders of magnitude more energy-efficient than their rate-based counterparts while maintaining comparable prediction accuracy when being deployed on dedicated neuromorphic computing hardware, which could be an energy-efficient addition in future intelligent vehicles with tight restrictions regarding on-board computing and energy resources. We present a thorough analysis of our approach on a publicly available data set including a comparison with state-of-the-art reference models.

Predicting future behavior of other traffic participants is an essential task that needs to be solved by automated vehicles and human drivers alike to achieve safe and situation-aware driving. Modern approaches to vehicles trajectory prediction typically rely on data-driven models like neural networks, in particular LSTMs (Long Short-Term Memorys), achieving promising results. However, the question of optimal composition of the underlying training data has received less attention. In this paper, we expand on previous work on vehicle trajectory prediction based on neural network models employing distributed representations to encode automotive scenes in a semantic vector substrate. We analyze the influence of variations in the training data on the performance of our prediction models. Thereby, we show that the models employing our semantic vector representation outperform the numerical model when trained on an adequate data set and thereby, that the composition of training data in vehicle trajectory prediction is crucial for successful training. We conduct our analysis on challenging real-world driving data.

Vector Symbolic Architectures belong to a family of related cognitive modeling approaches that encode symbols and structures in high-dimensional vectors. Similar to human subjects, whose capacity to process and store information or concepts in short-term memory is subject to numerical restrictions, the capacity of information that can be encoded in such vector representations is limited and one way of modeling the numerical restrictions to cognition. In this paper, we analyze these limits regarding information capacity of distributed representations. We focus our analysis on simple superposition and more complex, structured representations involving convolutive powers to encode spatial information. In two experiments, we find upper bounds for the number of concepts that can effectively be stored in a single vector only depending on the dimensionality of the underlying vector space.

Road and lane detection play an important role in autonomous driving and commercial driver-assistance systems. Vision-based road detection is an essential step towards autonomous driving, yet a challenging task due to illumination and complexity of the visual scenery. Urban scenes may present additional challenges such as intersections, multi-lane scenarios, or clutter due to heavy traffic. This paper presents an integrative approach to ego-lane detection that aims to be as simple as possible to enable real-time computation while being able to adapt to a variety of urban and rural traffic scenarios. The approach at hand combines and extends a road segmentation method in an illumination-invariant color image, lane markings detection using a ridge operator, and road geometry estimation using RANdom SAmple Consensus (RANSAC). Employing the segmented road region as a prior for lane markings extraction significantly improves the execution time and success rate of the RANSAC algorithm, and makes the detection of weakly pronounced ridge structures computationally tractable, thus enabling ego-lane detection even in the absence of lane markings. Segmentation performance is shown to increase when moving from a color-based to a histogram correlation-based model. The power and robustness of this algorithm has been demonstrated in a car simulation system as well as in the challenging KITTI data base of real-world urban traffic scenarios.