Anders Lager

Task Planning of Industrial Mobile Robots in Collaborative Dynamic Environments

Student   Anders Lager
Advisors   Alessandro V. Papadopoulos
Thomas Nolte
Branko Miloradovic
Giacomo Spampinato(ABB Robotics)
Faculty Reviewer   Petter Ögren, Royal Institute of Technology (KTH), Stockholm, Sweden
Grading Committee   Sven Koenig, University of California, Irvine (UCI), USA
Milica Petrovic, University of Belgrade, Belgrade, Serbia
Luca Bascetta, Politecnico di Milano, Milan, Italy
Ning Xiong, Mälardalen University, Västerås, Sweden (reserve)
Defence   Mälardalen University, Västerås, Sweden
Room My and Teams Meeting
October 24th, 2025 09:15
Abstract   Over the past decades, industrial robotics has transitioned from fixed, single-purpose machines to flexible, collaborative mobile systems capable of navigating complex factory environments. Today’s manufacturing demands, driven by labor scarcity, the need for rapid reconfiguration, and advances in AI and sensing, require robots to perform increasingly sophisticated, non-repetitive tasks alongside human workers. Designing and executing efficient multi-robot missions in such dynamic, human-in-the-loop settings presents multiple challenges: expressing high-level production requirements in a planner-friendly way, handling unexpected execution errors, scaling to large task allocations, and accounting for uncertainties in task durations and human behavior.
This thesis introduces an intuitive task modeling formalism and a suite of algorithmic methods that address these challenges end-to-end. First, we propose a domain-expert-friendly syntax for defining single-robot production missions, automatically generating problem definitions compatible with diverse off-the-shelf planners. To support rapid recovery from errors, we present task roadmaps, a novel planning algorithm that reuses the original search tree to accelerate replanning when execution deviates. We extend the formalism to a multi-robot kitting use case with alternative task locations and introduce a scalable, clustering-based approach to maintain computational tractability.
Recognizing the inherent uncertainties of human-robot collaboration, we further develop a collaborative stochastic task planning framework that integrates human risk preferences and models variability in task and routing durations. Finally, we tackle a collaborative production scenario with complex cross-schedule dependencies, proposing a stochastic scheduling method that generates optimized, deadlock-free plans while balancing efficiency with human well-being.
Extensive simulations and experiments grounded in real-world applications demonstrate that our methods significantly improve planning efficiency, robustness, and adaptability in dynamic industrial settings, paving the way toward more resilient, human-centric robotic automation.
Rules and Guidelines   The PhD procedure summary
Guidelines for Third-Cycle Studies at MDU
Thesis   Thesis
Included Papers   Paper A: A Task Modelling Formalism for Industrial Mobile Robot Applications .
Paper B: Task Roadmaps - Speeding up Task Replanning .
Paper C: A Scalable Heuristic for Mission Planning of Mobile Robot Teams.
Paper D: Risk Aware Planning of Collaborative Mobile Robot Applications with Uncertain Task Durations
Paper E: Stochastic Scheduling for Human-Robot Collaboration in Dynamic Manufacturing Environments
Publications   Complete list of publications

Back to Research

Last modified: 2025-09-01 10:59:32 +0200