Experimental Evaluation of Hybrid Conditional Planning for Service Robotics

Nouman, Ahmed; Yalciner, Ibrahim Faruk; Erdem, Esra; Patoğlu, Volkan

doi:10.1007/978-3-319-50115-4_60

Cited by 11 publications

(16 citation statements)

References 10 publications

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“…For instance, the Planning with Knowledge and Sensing (PKS) planner considers incomplete information and performs contingent planning [25] in two main scenarios, using force sensing and visual sensing. In a similar direction, offline-based hybrid conditional task and motion planning has been proposed [26], i.e., task planning is foremost performed, and then geometric evaluation is considered by incorporating low-level feasibility checks inside conditional planning (assuming that actuation actions are deterministic). On the contrary, the approach proposed here interweaves simultaneously efficient geometric reasoning inside the task planning process to provide geometrically feasible plans.…”

Section: Task and Motion Planning Under Uncertaintymentioning

confidence: 99%

Contingent Task and Motion Planning under Uncertainty for Human–Robot Interactions

2020

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Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human-robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.

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Section: Task and Motion Planning Under Uncertaintymentioning

confidence: 99%

Contingent Task and Motion Planning under Uncertainty for Human–Robot Interactions

2020

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“…The processing of multiple tasks in state-of-the-art robot systems is realised via high-level deliberation. Reasoning algorithms compute a multi-objective plan that satisfies temporal constraints [52] and uncertainties in conditional planning [53]. This approach utilises a semantic planner, which requires a complex knowledgebase that integrates predicates of all possible requests from the system user and requires that the predicates be compatible among the system tasks.…”

Section: ) Multi-task Robot Systemsmentioning

confidence: 99%

Scheduling of a Robot’s Tasks With the TaskER Framework

Dudek

Winiarski

2020

IEEE Access

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Robots, in contrast to typical computational systems, affect the physical environment directly. Therefore, other assumptions must be considered for task management procedures in these system types. Robots coexist with humans in the environment and act upon potentially dangerous objects (e.g., a cooker); hence, extra safety procedures in robot task harmonisation must be ensured. Additionally, an algorithm that schedules tasks for a robot and optimises the robot's operation needs to consider the robot motion time, dynamics of the physical processes, changes in the robot user preferences and changes in the environment made by other habitants. In this article, we investigate the problem of switching between various independent tasks safely and state requirements for a control system resolving it. The tasks are uploaded to a store, launched on a robot at the user's request (similar to smartphone applications) and scheduled following a configurable algorithm. Furthermore, we design a model of systems satisfying the requirements. The systems are structured with agents of different classes. We propose a task-switching procedure and dedicated states of the finite-state machines describing the operation of the agents. Finally, we present a TaskER framework implementing the model, and we verify the model through the execution of an exemplary system in scenarios showing the benefits of the model implementation. As a result of our approach application, the robot tasks can be safely interrupted, postponed, resumed, and potential danger (e.g., leaving a cooker on for a long time) can be minimised.

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“…Because of its rich representation capabilities, ASP is applied in many applications of planning in robotics (e.g. [63]) and the combination of monitoring, diagnosis, and replanning (e.g. [28]).…”

Section: General Overview Of Asp Applicationsmentioning

confidence: 99%

Industrial Applications of Answer Set Programming

et al. 2018

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Automated problem solving in combination with declarative specifications of search-problems have shown to substantially improve the implementation and maintenance costs as well as the man-machine interaction of deployed industrial applications. The knowledge representation and reasoning (KRR) framework of answer set programming (ASP) offers a rich representation language and high performance solvers. Therefore, ASP has become very attractive for the representation and solving of search-problems both for academia and industry. This article focuses on the latest industrial applications of ASP. We do not only present successful applications of ASP but also describe the development process and the design of ASP programs in an industrial context. Finally, we discuss current approaches to tackle the most significant application challenges such as grounding and runtime improvements by heuristics.

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Experimental Evaluation of Hybrid Conditional Planning for Service Robotics

Cited by 11 publications

References 10 publications

Contingent Task and Motion Planning under Uncertainty for Human–Robot Interactions

Contingent Task and Motion Planning under Uncertainty for Human–Robot Interactions

Scheduling of a Robot’s Tasks With the TaskER Framework

Industrial Applications of Answer Set Programming

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