This paper looks at human-robot collaboration (HRC) scenarios, in particular where the durations and costs of the actions are heterogeneous between agents, reflecting the agents’ capabilities as well as environmental constraints. We explore the use of temporal PDDL planning as a means of finding over-arching task plans for such collaborative scenarios, and apply suitable heuristics and search algorithms to improve the extent to which plans can be found that are sensitive to combined duration and cost metrics. An evaluation in a kitchen scenario shows our approach is effective, finding cost-effective task plans compared to those from existing planners, and a hand-crafted baseline.

"Hey, robot. Let’s tidy up the kitchen. By the way, I have back pain today". How can a robotic system devise a shared plan with an appropriate task allocation from this abstract goal and agent condition? Classical AI task planning has been explored for this purpose, but it involves a tedious definition of an inflexible planning problem. Large Language Models (LLMs) have shown promising generalisation capabilities in robotics decision-making through knowledge extraction from Natural Language (NL). However, the translation of NL information into constrained robotics domains remains a challenge. In this paper, we use LLMs as translators between NL information and a structured AI task planning problem, targeting human-robot collaborative plans. The LLM generates information that is encoded in the planning problem, including specific subgoals derived from an NL abstract goal, as well as recommendations for subgoal allocation based on NL agent conditions. The framework, PlanCollabNL, is evaluated for a number of goals and agent conditions, and the results show that correct and executable plans are found in most cases. With this framework, we intend to add flexibility and generalisation to HRC plan generation, eliminating the need for a manual and laborious definition of restricted planning problems and agent models.

One of the main challenges for successful human-robot collaborative applications lies in adapting the plan to the human agent’s changing state and preferences. A promising solution is to bridge the gap between agent modelling and AI task planning, which can be done by integrating the agent state as action costs in the task planning domain. This allows for the plan to be adapted to different partners, by influencing the action allocation. The difficulty then lies in setting appropriate action costs. This paper presents a novel framework to learn a set of planning action costs considering the preferred actions for an agent based on their state. An evolutionary optimisation algorithm is used for this purpose, and an action outcome simulator is developed to act as the black-box function, based on both an agent model and an action type model. This addresses the challenge of collecting data in HRC real-world scenarios, accelerating the learning for posterior fine-tuning in real applications. The coherence of the models and the simulator is proven through a conducted survey, and the learning algorithm is shown to learn appropriate action costs, producing plans that satisfy both the agents’ preferences and the prioritised plan requisites. The resulting system is a generic learning framework integrating components that can be easily extended to a wide range of applications, models and planning formalisms.

Human-Robot Collaboration (HRC) has become a major trend in robotics in recent years with the idea of combining the strengths from both humans and robots. In order to share the work to be done, many task planning approaches have been implemented. However, they don't fully satisfy the required adaptability in human-robot collaborative tasks, with most approaches not considering neither the state of the human partner nor the possibility of adapting the collaborative plan during execution or even anticipating failures. In this paper, we present a planning system for human-robot collaborative plans that takes into account the agents' states and deals with unforeseen human behaviour, by replanning in anticipation when the human state changes to prevent action failure. The human state is defined in terms of capacity, knowledge and motivation. The system has been implemented in a standardised environment using the Planning Domain Definition Language (PDDL) and the modular ROSPlan framework, and we have validated the approach in multiple simulation settings. Our results show that using the human model fosters an appropriate task allocation while allowing failure anticipation, replanning in time to prevent it.

Global Navigation Satellite Systems (GNSS) have been commonly used as a source for global localization in autonomous systems, including autonomous vehicles and robots. Different GNSS-based techniques exist to compute the position, velocity and time (PVT) of a moving rover, differing in the level of precision and complexity. The expected performance for each technique is well-studied but it is highly-dependant on the surrounding conditions and local effects such as occlusion or multipath. This paper presents the results after evaluating multiconstellation single-frequency data obtained from an extensive field campaign carried out in the port of Thessaloniki over several months, as part of the European GSA project LOGIMATIC. The paper, furthermore, focuses on the GNSS performance in container terminals, considered different from common urban and suburban scenarios and a potentially challenging environment due to the presence of containers, cranes, hangars and proximity to water. Analysis results show that the accuracy of the standalone PVT estimation methods for GPS and Galileo separately matched the expected performance numbers, with Galileo slightly outperforming GPS. The analysis also illustrated the effects of multipath and signal denial when operating in the proximity of cranes and hangars. Furthermore, the application of EGNOS/SBAS introduced a significant improvement, demonstrating the added value of EGNOS and Galileo over a traditional GPS only solution in these scenarios.

Accurate localization is a problem in environments such as tunnels or pipes due to the hostile conditions, dimensions and the general lack of distinctive visual and/or structural features. Standard indoor localization techniques (e.g. visual SLAM) do not work well in pipelines given the lack of exploitable visual features, while outdoor techniques (GPS in particular) do not work inside metal pipes. In this paper, we present a RF odometry-like method to localize a robot along a pipe. Using a radio-frequency signal transmitter and a receiver, we propose and implement the complete setup in order to obtain periodic received-signal fadings and base a localization system on the periodicity of these fadings. There are two main advantages of the proposed system. First, the sensors are easy to install and can be used with any (ground, aerial) robot. Second, the fadings obtained are periodic, avoiding cumulative errors in localization.

The propagation of electromagnetic waves in tunnel- like environments differs from other scenarios, basically because tunnels can act as a waveguide, allowing the communication range to be extended. Nevertheless, phenomena such as strong fadings appear. In this context, our research group has been performing multi-robot navigation under communication constraints in this kind of environments. It is clear that fadings greatly affect communication among robots, and an in-depth study of these phenomena is required. In this paper, we present the results of a measuring campaign carried out in the Somport tunnel (in Spain) at 433 MHz, 868 MHz, 2.4 GHz and 5.2 GHz. Slow and fast fadings, as well as near and far sectors are analyzed using wavelets, a tool never used before to study propagation in tunnels. Results obtained from the wavelets and from classic filtering techniques are compared, showing that the former have the advantage of condensing most of the useful and valid information in a single graph.