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Wessén, J., Carlsson, M., Schulte, C., Flener, P., Pecora, F. & Matskin, M. (2023). A constraint programming model for the scheduling and workspace layout design of a dual-arm multi-tool assembly robot. Constraints, 28(2), 71-104
Open this publication in new window or tab >>A constraint programming model for the scheduling and workspace layout design of a dual-arm multi-tool assembly robot
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2023 (English)In: Constraints, ISSN 1383-7133, E-ISSN 1572-9354, Vol. 28, no 2, p. 71-104Article in journal (Refereed) Published
Abstract [en]

The generation of a robot program can be seen as a collection of sub-problems, where many combinations of some of these sub-problems are well studied. The performance of a robot program is strongly conditioned by the location of the tasks. However, the scope of previous methods does not include workspace layout design, likely missing high-quality solutions. In industrial applications, designing robot workspace layout is part of the commissioning. We broaden the scope and show how to model a dual-arm multi-tool robot assembly problem. Our model includes more robot programming sub-problems than previous methods, as well as workspace layout design. We propose a constraint programming formulation in MiniZinc that includes elements from scheduling and routing, extended with variable task locations. We evaluate the model on realistic assembly problems and workspaces, utilizing the dual-arm YuMi robot from ABB Ltd. We also evaluate redundant constraints and various formulations for avoiding arm-to-arm collisions. The best model variant quickly finds high-quality solutions for all problem instances. This demonstrates the potential of our approach as a valuable tool for a robot programmer.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Assembly manufacturing, Constraint programming, Robot planning and scheduling, Workspace layout design
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-107731 (URN)10.1007/s10601-023-09345-4 (DOI)001032500800001 ()2-s2.0-85164960102 (Scopus ID)
Available from: 2023-08-25 Created: 2023-08-25 Last updated: 2023-11-28Bibliographically approved
Gugliermo, S., Schaffernicht, E., Koniaris, C. & Pecora, F. (2023). Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization. IEEE Robotics and Automation Letters, 8(6), 3534-3541
Open this publication in new window or tab >>Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization
2023 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 6, p. 3534-3541Article in journal (Refereed) Published
Abstract [en]

The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this letter extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Behavioral sciences, Decision trees, Planning, Batteries, Task analysis, Circuit synthesis, Partitioning algorithms, Behavior-based systems, intelligent transportation systems, learning from demonstration
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-106251 (URN)10.1109/LRA.2023.3268598 (DOI)000981889200003 ()2-s2.0-85153797531 (Scopus ID)
Funder
Swedish Foundation for Strategic Research
Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2024-01-17Bibliographically approved
Zhang, S. & Pecora, F. (2023). Online and Scalable Motion Coordination for Multiple Robot Manipulators in Shared Workspaces. IEEE Transactions on Automation Science and Engineering
Open this publication in new window or tab >>Online and Scalable Motion Coordination for Multiple Robot Manipulators in Shared Workspaces
2023 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783Article in journal (Refereed) Epub ahead of print
Abstract [en]

Multi-robot motion coordination is essential to make robots work safely and efficiently in a shared workspace, ensuring task completion while avoiding interference between each other's motions. Achieving online replanning and scaling to large numbers of robots are particularly challenging. In this paper, we present a motion coordination method for multiple robot manipulators with given targets. The approach separates the problem into a global coordination stage and a local trajectory replanning stage. Online reactivity and scalability are achieved by means of coordination in a reduced space and efficient trajectory replanning. We first introduce the method for systems with two robots, then extend it to systems with an arbitrary number of robots. The method determines whether kinematically-feasible and non-interfering trajectories leading all the robots to their targets exist. The approach generates time-efficient trajectories if solutions exist, or provides information for switching targets if solutions cannot be found. We show formally and empirically that the method has low computational overhead and scales quadratically with the number of robots. Experiments are conducted with up to three real 7-DOF robots and up to ten simulated robots. Note to Practitioners-In underground mining, a key process is that of tunneling, i.e., drilling and blasting rock to excavate tunnels that lead to sources of ore. Drilling is carried out by rigs equipped with multiple robotic arms. A key factor affecting the efficiency of tunneling is the time to completion of drilling operations. In current industrial practice, these operations are carried out manually by an operator steering the arms on the drill rig. Time to completion can be drastically reduced if the arms could operate concurrently, intelligently optimizing and coordinating their motions. However, existing methods for multi-arm motion coordination are inadequate, as they fail to cater to one or more of the following real-world constraints: several robot arms work in close proximity and their workspaces are overlapping; task completion times are uncertain due to rock density and drill bit breakage; and contingencies such as unexpected delays in motion or stops sometimes happen. These constraints exist also in other industrial applications, like manufacturing and assembly. This paper proposes a framework which enables multiple high-DOF robot manipulators to safely and efficiently work in a shared workspace. The method allows to adjust robot trajectories while robots move, and is shown to scale well with the number of robots. Motions are generated and adjusted by time-optimal trajectory planning, whose computational overhead is small enough for online operation and benefits task completion efficiency, safety, and productivity. Experiments using three 7-DOF robots suggest that this approach is practically feasible, and simulations with up to 10 robots attest to its scalability.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Robots, Robot kinematics, Collision avoidance, Trajectory, Manipulators, Task analysis, Planning, Multi-robot systems, scheduling and coordination, real-time planning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-105936 (URN)10.1109/TASE.2023.3266889 (DOI)000976057700001 ()
Available from: 2023-05-15 Created: 2023-05-15 Last updated: 2023-05-15Bibliographically approved
Molina, S., Mannucci, A., Magnusson, M., Adolfsson, D., Andreasson, H., Hamad, M., . . . Lilienthal, A. J. (2023). The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots. IEEE robotics & automation magazine
Open this publication in new window or tab >>The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots
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2023 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Robots, Safety, Navigation, Mobile robots, Human-robot interaction, Hidden Markov models, Trajectory
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-108145 (URN)10.1109/MRA.2023.3296983 (DOI)001051249900001 ()
Funder
EU, Horizon 2020, 732737
Available from: 2023-09-14 Created: 2023-09-14 Last updated: 2024-01-02Bibliographically approved
Chimamiwa, G., Giaretta, A., Alirezaie, M., Pecora, F. & Loutfi, A. (2022). Are Smart Homes Adequate for Older Adults with Dementia?. Sensors, 22(11), Article ID 4254.
Open this publication in new window or tab >>Are Smart Homes Adequate for Older Adults with Dementia?
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2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 11, article id 4254Article, review/survey (Refereed) Published
Abstract [en]

Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users' daily activities and detect deviations. However, activity recognition and anomaly detection are not sufficient, as they lack the capacity to capture the progression of patients' habits across the different stages of dementia. To achieve this, smart homes should be enabled to recognise patients' habits and changes in habits, including the loss of some habits. In this study, we first present an overview of the stages that characterise dementia, alongside real-world personas that depict users' behaviours at each stage. Then, we survey the state of the art on activity recognition in smart homes for older adults with dementia, including the literature that combines activity recognition and anomaly detection. We categorise the literature based on goals, stages of dementia, and targeted users. Finally, we justify the necessity for habit recognition in smart homes for older adults with dementia, and we discuss the research challenges related to its implementation.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
Activity recognition, ageing, dementia, habit recognition, smart homes
National Category
Gerontology, specialising in Medical and Health Sciences Occupational Therapy
Identifiers
urn:nbn:se:oru:diva-99532 (URN)10.3390/s22114254 (DOI)000809104700001 ()35684874 (PubMedID)2-s2.0-85131268514 (Scopus ID)
Funder
EU, Horizon 2020, 754285
Available from: 2022-06-15 Created: 2022-06-15 Last updated: 2022-07-28Bibliographically approved
Swaminathan, C. S., Kucner, T. P., Magnusson, M., Palmieri, L., Molina, S., Mannucci, A., . . . Lilienthal, A. J. (2022). Benchmarking the utility of maps of dynamics for human-aware motion planning. Frontiers in Robotics and AI, 9, Article ID 916153.
Open this publication in new window or tab >>Benchmarking the utility of maps of dynamics for human-aware motion planning
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2022 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 9, article id 916153Article in journal (Refereed) Published
Abstract [en]

Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022
Keywords
ATC, benchmarking, dynamic environments, human-aware motion planning, human-populated environments, maps of dynamics
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-102370 (URN)10.3389/frobt.2022.916153 (DOI)000885477300001 ()36405073 (PubMedID)2-s2.0-85142125253 (Scopus ID)
Funder
European Commission, 101017274
Available from: 2022-11-24 Created: 2022-11-24 Last updated: 2022-12-20Bibliographically approved
Salvado, J., Mansouri, M. & Pecora, F. (2022). DiMOpt: a Distributed Multi-robot Trajectory Optimization Algorithm. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, October 23-27, 2022 (pp. 10110-10117). IEEE
Open this publication in new window or tab >>DiMOpt: a Distributed Multi-robot Trajectory Optimization Algorithm
2022 (English)In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE , 2022, p. 10110-10117Conference paper, Published paper (Refereed)
Abstract [en]

This paper deals with Multi-robot Trajectory Planning, that is, the problem of computing trajectories for multiple robots navigating in a shared space while minimizing for control energy. Approaches based on trajectory optimization can solve this problem optimally. However, such methods are hampered by complex robot dynamics and collision constraints that couple robot's decision variables. We propose a distributed multirobot optimization algorithm (DiMOpt) that addresses these issues by exploiting (1) consensus optimization strategies to tackle coupling collision constraints, and (2) a single-robot sequential convex programming method for efficiently handling non-convexities introduced by dynamics. We compare DiMOpt with a baseline centralized multi-robot sequential convex programming algorithm (SCP). We empirically demonstrate that DiMOpt scales well for large fleets of robots while computing solutions faster and with lower costs. Finally, DiMOpt is an iterative algorithm that finds feasible trajectories before converging to a locally optimal solution, and results suggest the quality of such fast initial solutions is comparable to a converged solution computed via SCP.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-104976 (URN)10.1109/IROS47612.2022.9981345 (DOI)000909405302063 ()2-s2.0-85146320107 (Scopus ID)9781665479271 (ISBN)9781665479288 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, October 23-27, 2022
Funder
Vinnova
Note

Funding agency:

KKS Synergy TeamRob

Available from: 2023-03-16 Created: 2023-03-16 Last updated: 2023-03-16Bibliographically approved
Salvado, J., Mansouri, M. & Pecora, F. (2021). A Network-Flow Reduction for the Multi-Robot Goal Allocation and Motion Planning Problem. In: IEEE International Conference on Automation Science and Engineering (CASE): . Paper presented at 17th IEEE International Conference on Automation Science and Engineering (CASE 2021), Lyon, France, August 23-27, 2021,.
Open this publication in new window or tab >>A Network-Flow Reduction for the Multi-Robot Goal Allocation and Motion Planning Problem
2021 (English)In: IEEE International Conference on Automation Science and Engineering (CASE), 2021Conference paper, Published paper (Refereed)
Abstract [en]

This paper deals with the problem of allocating goals to multiple robots with complex dynamics while computing obstacle-free motions to reach those goals. The spectrum of existing methods ranges from complete and optimal approaches with poor scalability, to highly scalable methods which make unrealistic assumptions on the robots and/or environment. We overcome these limitations by using an efficient graph-based method for decomposing the problem into sub-problems. In particular, we reduce the problem to a Minimum-Cost Max-Flow problem whose solution is used by a multi-robot motion planner that does not impose restrictive assumptions on robot kinodynamics or on the environment. We show empirically that our approach scales to tens of robots in environments composed of hundreds of polygons.

Keywords
Motion and Path Planning, Planning, Scheduling and Coordination, Optimization and Optimal Control
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-93582 (URN)
Conference
17th IEEE International Conference on Automation Science and Engineering (CASE 2021), Lyon, France, August 23-27, 2021,
Projects
Semantic RobotsILIADAutoHauler
Funder
EU, Horizon 2020, 732737Vinnova
Available from: 2021-08-11 Created: 2021-08-11 Last updated: 2021-08-16Bibliographically approved
Salvado, J., Mansouri, M. & Pecora, F. (2021). Combining Multi-Robot Motion Planning and Goal Allocation using Roadmaps. In: 2021 IEEE International Conference on Robotics and Automation (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA 2021), Xi’an, China, May 30 - June 5, 2021 (pp. 10016-10022). IEEE
Open this publication in new window or tab >>Combining Multi-Robot Motion Planning and Goal Allocation using Roadmaps
2021 (English)In: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, p. 10016-10022Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of automating fleets of robots with non-holonomic dynamics. Previously studied methods either specialize in facets of this problem, that is, one or a combination of multi-robot goal allocation, motion planning, and coordination, and typically acrifice optimality and completeness for scalability. We propose an approach that constructs an abstract multi-robot roadmap in a reduced configuration space, where we account for environment connectivity and interference cost between robots occupying the same polygons. Querying the road-map results in a robot-goal assignment and abstract multi-robot trajectory. This is then exploited to de-compose the original problem into smaller problems, each of which is solved with a multi-robot motion planner that accounts for kinodynamic constraints. We validate the approach experimentally to demonstrate the advantage of considering task assignment and motion planning holistically, and explore some methods for balancing solution quality and computational efficiency.

Place, publisher, year, edition, pages
IEEE, 2021
Series
IEEE International Conference on Robotics and Automation (ICRA), ISSN 1050-4729, E-ISSN 2577-087X
Keywords
Path Planning for Multiple Mobile Robots or Agents, Task and Motion Planning, Multi-Robot Systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-90585 (URN)10.1109/ICRA48506.2021.9560861 (DOI)000771405403001 ()2-s2.0-85117040766 (Scopus ID)9781728190778 (ISBN)9781728190785 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA 2021), Xi’an, China, May 30 - June 5, 2021
Funder
European CommissionKnowledge FoundationVinnova
Available from: 2021-03-19 Created: 2021-03-19 Last updated: 2022-04-25Bibliographically approved
Mansouri, M., Pecora, F. & Schüller, P. (2021). Combining Task and Motion Planning: Challenges and Guidelines. Frontiers in Robotics and AI, 8, Article ID 637888.
Open this publication in new window or tab >>Combining Task and Motion Planning: Challenges and Guidelines
2021 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 8, article id 637888Article in journal (Refereed) Published
Abstract [en]

Combined Task and Motion Planning (TAMP) is an area where no one-fits-all solution can exist. Many aspects of the domain, as well as operational requirements, have an effect on how algorithms and representations are designed. Frequently, trade-offs have to be madet o build a system that is effective. We propose five research questions that we believe need to be answered to solve real-world problems that involve combined TAMP. We show which decisions and trade-offs should be made with respect to these research questions, and illustrate these on examples of existing application domains. By doing so, this article aims to provide a guideline for designing combined TAMP solutions that are adequate and effective in the target scenario.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2021
Keywords
Task and motion planning, integrative AI, knowledge representation, automated reasoning, industrial applications of robotics
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-91851 (URN)10.3389/frobt.2021.637888 (DOI)000656841700001 ()34095239 (PubMedID)2-s2.0-85107191930 (Scopus ID)
Funder
Knowledge FoundationVinnovaEU, Horizon 2020, 825619
Available from: 2021-05-19 Created: 2021-05-19 Last updated: 2021-06-21Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9652-7864

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