Predicting Energy Consumption and Traversal Time of Ground Robots for Outdoor Navigation on Multiple Types of Terrain

The outdoor navigation capabilities of ground robots have improved significantly in recent years, opening up new potential applications in a variety of settings. Cost-based representations of the environment are frequently used in the path planning domain to obtain an optimized path based on various objectives, such as traversal time or energy consumption. However, obtaining such cost representations is still cumbersome, particularly in outdoor settings with diverse terrain types and slope angles. In this paper, we address this problem by using a data-driven approach to develop a cost representation for various outdoor terrain types that supports two optimization objectives, namely energy consumption and traversal time. We train a supervised machine learning model whose inputs consists of extracted environment data along a path and whose outputs are the predicted energy consumption and traversal time. The model is based on a ResNet neural network architecture and trained using field-recorded data. The error of the proposed method on different types of terrain is within 11\% of the ground truth data. To show that it performs and generalizes better than currently existing approaches on various types of terrain, a comparison to a baseline method is made.

Enhancing Temporal Planning Domains by Sequential Macro-actions (Extended Version)

Temporal planning is an extension of classical planning involving concurrent execution of actions and alignment with temporal constraints. Durative actions along with invariants allow for modeling domains in which multiple agents operate in parallel on shared resources. Hence, it is often important to avoid resource conflicts, where temporal constraints establish the consistency of concurrent actions and events. Unfortunately, the performance of temporal planning engines tends to sharply deteriorate when the number of agents and objects in a domain gets large. A possible remedy is to use macro-actions that are well-studied in the context of classical planning. In temporal planning settings, however, introducing macro-actions is significantly more challenging when the concurrent execution of actions and shared use of resources, provided the compliance to temporal constraints, should not be suppressed entirely. Our work contributes a general concept of sequential temporal macro-actions that guarantees the applicability of obtained plans, i.e., the sequence of original actions encapsulated by a macro-action is always executable. We apply our approach to several temporal planners and domains, stemming from the International Planning Competition and RoboCup Logistics League. Our experiments yield improvements in terms of obtained satisficing plans as well as plan quality for the majority of tested planners and domains.

The Need for a Meta-Architecture for Robot Autonomy

Long-term autonomy of robotic systems implicitly requires dependable platforms that are able to naturally handle hardware and software faults, problems in behaviors, or lack of knowledge. Model-based dependable platforms additionally require the application of rigorous methodologies during the system development, including the use of correct-by-construction techniques to implement robot behaviors. As the level of autonomy in robots increases, so do the cost of offering guarantees about the dependability of the system. Certifiable dependability of autonomous robots, we argue, can benefit from formal models of the integration of several cognitive functions, knowledge processing, reasoning, and meta-reasoning. Here we put forward the case for a generative model of cognitive architectures for autonomous robotic agents that subscribes to the principles of model-based engineering and certifiable dependability, autonomic computing, and knowledge-enabled robotics.

Enabling and Assessing Trust when Cooperating with Robots in Disaster Response (EASIER)

This paper presents a conceptual overview of the EASIER project and its scope. EASIER focuses on supporting emergency forces in disaster response scenarios with a semi-autonomous mobile manipulator. Specifically, we examine the operator's trust in the system and his/her cognitive load generated by its use. We plan to address different research topics, exploring how shared autonomy, interaction design, and transparency relate to trust and cognitive load. Another goal is to develop non-invasive methods to continuously measure trust and cognitive load in the context of disaster response using a multilevel approach. This project is conducted by multiple academic partners specializing in artificial intelligence, interaction design, and psychology, as well as an industrial partner for disaster response equipment and end-users for framing the project and the experiments in real use-cases.