Ana Cavalcanti

Hassan Sartaj, Shaukat Ali, Ana Cavalcanti et al.

Self-adaptive robotic systems operate autonomously in dynamic and uncertain environments, requiring robust real-time monitoring and adaptive behaviour. Unlike traditional robotic software with predefined logic, self-adaptive robots exploit artificial intelligence (AI), machine learning, and model-driven engineering to adapt continuously to changing conditions, thereby ensuring reliability, safety, and optimal performance. This paper presents a research agenda for software engineering in self-adaptive robotics, structured along two dimensions. The first concerns the software engineering lifecycle, requirements, design, development, testing, and operations, tailored to the challenges of self-adaptive robotics. The second focuses on enabling technologies such as digital twins and AI-driven adaptation, which support runtime monitoring, fault detection, and automated decision-making. We identify open challenges, including verifying adaptive behaviours under uncertainty, balancing trade-offs between adaptability, performance, and safety, and integrating self-adaptation frameworks like MAPE K/MAPLE-K. By consolidating these challenges into a roadmap toward 2030, this work contributes to the foundations of trustworthy and efficient self-adaptive robotic systems capable of meeting the complexities of real-world deployment.

Abdulrazaq Abba, Ana Cavalcanti, Jeremy Jacob

In this work, we consider translating tock-CSP into Timed Automata for UPPAAL to facilitate using UPPAAL in reasoning about temporal specifications of tock-CSP models. The process algebra tock-CSP provides textual notations for modelling discrete-time behaviours, with the support of tools for automatic verification. Similarly, automatic verification of Timed Automata (TA) with a graphical notation is supported by the UPPAAL real-time verification toolbox \uppaal. The two modelling approaches, TA and tock-CSP, differ in both modelling and verification approaches, temporal logic and refinement, respectively, as well as their provided facilities for automatic verification. For instance, liveness requirements are difficult to specify with the constructs of tock-CSP, but they are easy to specify and verify in UPPAAL. To take advantage of temporal logic, we translate tock-CSP into TA for \uppaal; we have developed a translation technique and its supporting tool. We provide rules for translating tock-CSP into a network of small TAs for capturing the compositional structure of tock-CSP that is not available in TA. For validation, we start with an experimental approach based on finite approximations to trace sets. Then, we explore mathematical proof to establish the correctness of the rules for covering infinite traces.

Wei Li, Alvaro Miyazawa, Pedro Ribeiro et al.

Controllers for autonomous robotic systems can be specified using state machines. However, these are typically developed in an ad hoc manner without formal semantics, which makes it difficult to analyse the controller. Simulations are often used during the development, but a rigorous connection between the designed controller and the implementation is often overlooked. This paper presents a state-machine based notation, RoboChart, together with a tool to automatically create code from the state machines, establishing a rigorous connection between specification and implementation. In RoboChart, a robot's controller is specified either graphically or using a textual description language. The controller code for simulation is automatically generated through a direct mapping from the specification. We demonstrate our approach using two case studies (self-organized aggregation and swarm taxis) in swarm robotics. The simulations are presented using two different simulators showing the general applicability of our approach.