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.

The trend towards Industry 4.0 demands an increasing flexibility of system configurations including more agile collaborative human-robot systems that can quickly adapt to new tasks and missions. At the same time, state-of-the-art safety solution for many industrial robots is the use of barriers around the robot to limit the exposure to human co-workers. As these barriers cannot be reconfigured easily, it considerably limits the flexibility of many robotic solutions. Thus, new more agile safety solutions are required, and these require new, robust perception systems that can ensure detection of all safety relevant objects. In this paper, we propose a novel depth perception approach called HistoDepth, which addresses this need. It uses depth sensors that are mounted at fixed, static positions outside of the workspace and tracks each pixel measurement statistically over time. This enables our solution to differentiate static scene elements from dynamic (potentially hazardous) elements and to enforce a robot safety maneuver upon detection of a critical object. Moreover, it can adapt automatically to changing noise or scene configurations without user input or the need for setup changes. Using an UR5 robot arm, we demonstrate that this solution can enable new agile and flexible safety concepts for collaborative robots.

As automated vehicles are expected to significantly reduce the number of fatalities in road traffic, ensuring their safety is one of the most critical challenges in the industry today. The Responsibility-Sensitive Safety (RSS) concept is a step towards this goal. RSS formalizes reasonable boundaries for the foreseeable worst-case behavior of traffic participants by defining clear mathematically proven rules. All parameters used in RSS have a physical meaning and are thus well understandable, but the values of these parameters have a crucial effect on the applicability of this approach. Choosing too conservative parameter values may impact traffic flow, while the opposite could lead to uncomfortable or potentially unsafe driving behavior. While a majority of work concentrated on the longitudinal use case, in this work we focus on finding reasonable parameter values for lateral safety. We propose scopes and parameter sets for the RSS minimum lateral distance extracted from human driving behavior that allow for a comfortable driving behavior while not hindering traffic flow.

The need for safety in automated driving is undisputed. Since automated vehicles are expected to reduce the number of fatalities in road traffic significantly, hundreds of millions of test kilometers would be required for statistical safety validation [1]. Physics-based safety verification approaches are promising in order to reduce this validation effort. Towards this goal, Mobileye introduced the concept of Responsibility-Sensitive Safety (RSS). In RSS, bounds for the reasonable worst-case behavior of traffic participants are assumed to be given, such as the reaction time or the maximum deceleration. These parameters have a crucial effect on the applicability of the approach: choosing conservative parameters likely hinders traffic flow, while the opposite could lead to collisions, as the assumptions are violated. Thus, in this work, we focus on finding reasonable parameters of RSS. Based on the physical limits, legal requirements and human driving behavior, we propose scopes and parameter sets that allow for a sound safety verification while not hindering traffic flow. Furthermore, we present an approach that explains seemingly frequent human drivers' RSS violations on highways and may lead to a useful extension of RSS.

Addressing safety for future autonomous vehicles (AVs) while maintaining a high practicability, i.e. not using excessive safety margins, getting stuck or not able to drive at all, is still an open research question. In this regard, the Responsibility Sensitive Safety (RSS) approach, which is a formal parametric safety model, is a promising concept and is currently gaining lots of attraction. With RSS it is possible to combine safety and practicability, if the model parameters are chosen in a reasonable manner, i.e. good enough to achieve a desirable level of safety, but not covering the physical worst case. However, as we will show in this work, this can lead to a not optimal behavior of the AV in extreme situations, when other traffic participants violate RSS assumptions. To overcome this, we propose an extension to RSS, called RSS +, which can mitigate many of these situations in a pro-active manner, without sacrificing practicability.

Occupancy grids are widely used in robotics and autonomous systems to create a representation of the environment. Originally designed to map a static environment, recently also dynamic occupancy grids are emerging for the handling of dynamic scenes. However, a major drawback of (dynamic) occupancy grids is the high computational cost (memory and compute) to store and process the information. This is due to the fact, that occupancy grids usually divide the environment in cells of the same size (i.e. uniform grids). As a result, the computational cost increases quadratically with decreasing cell size. Therefore, for many use cases, a trade-off between accuracy (high resolution grid) and distance covered by the grid is required to keep the computational cost in an acceptable range. To overcome this issue we propose in this paper a novel approach for dynamic occupancy grids using non-uniform cell sizes. Our results show, that these non-uniform occupancy grids reduce the numbers of required cells and therefore the computational cost significantly without compromising on the quality of the results.

On the path towards mass deployment of automated vehicles (AVs) several challenges still have to be resolved. One of these is the development of approaches that allow the safe operation of an AV within uncertain environments, while not imposing excessive safety margins. Existing proposals, such as the Responsibility Sensitive Safety (RSS) approach from Intel/Mobileye, are a good first step towards this goal. These approaches are based on parameterizable models, where the parameter choice is often a balancing process of safety and usefulness (e.g. traffic density). Hence, a proper selection of the parameters is crucial, which requires a proper situation understanding within the models. Therefore, we propose to consider the risk of a driving situation inside RSS. The result is a novel risk-aware RSS approach, which allows for significant reductions in safety margins (i.e. increased traffic density) in a situation-dependent manner, while risk limits are maintained, thus achieving the desired balance between safety and usefulness.

A typical Surround View System consists of several cameras on the vehicle perimeter. This document proposes three novel methods for the extrinsic calibration of Surround View Systems (SVS). I - The first approach uses a single calibration pattern placed step-by-step on the vehicle perimeter. II - The second approach uses several calibration patterns placed on the ground plane. The vehicle drives between the calibration setup. No knowledge of the vehicle's location relative to the calibration patterns is necessary; only distances between the calibration patterns are required. III - The last approach gives the most flexibility in calibration. The features are distributed on the ground plane arbitrarily. The vehicle drives forward and backward and all extrinsic calibration parameters are automatically estimated. Each of these approaches provide very good calibration results, sufficient for the correct operation of SVS.

To meet the demand for robot to perform complex tasks, it is desirable to develop a methodology for intelligent behavior evolution in which a robot learns behaviors just as a human acquires dexterity, by repeated practice and use. Presented is a method for learning complex and dexterous behaviors through a knowledge array network, i.e., a network of knowledge arrays that play most important role as behavioral building blocks for robot behavior learning and evolution based on the intelligent composite motion control (ICMC). The process to realize a behavior from component element motions is presented. It is shown how a ball shooting behavior by a legged robot in robot soccer is realized according to the proposed method. Component element motions, are optimized. The optimal parameters obtained are then stored as a knowledge array, with which the robot can adaptively execute sub-optimal motions even for inexperienced situations. With the element motions optimized beforehand for a wide range of situations, the desirable shooting is obtained by combining them with additional optimization. The numerical result is given to demonstrate the presented method.

In this paper a multi-joint robot for sewer inspection tasks is presented. In order to increase the operating scope the robot has been designed to run round or over obstacles, to follow sewage branches and is operated with no wire attached to it. As a result of the wireless approach the robot has to carry an energy resource and must be abbe to act autonomously. In this paper we give a short description of the mechanical design and the electronic components used. Then we describe the control system and show sequences and results of in-pipe experiments.

In this paper a multijoint robot for sewer inspection tasks is presented. In order to increase the operating scope the robot has been made able to run round or over obstacles, to follow sewage branches and is aimed to work wirelessly unlike most other sewer inspection robots. As a result of the wireless approach the robot has to carry an energy resource and must be able to act autonomously. This article is focused on the mechanical design and the control hardware of the system. Additionally, we describe a first approach of a control strategy and some results of first tests.