In the last decade, there has been significant progress on Robotics. This development has widened the intervention fields of robots, particularly the assistance and interaction with humans. Improving the quality of this interaction requires robots to be endowed with representation and/or reasoning system for spatial knowledge. Our goal is to develop a planner allowing us to use human robot interaction to solve space sharing problems. This work is original, in that the representation of the same space for humans (symbolic, fuzzy) and robots (numeric) is not the same. Our objective is to combine human and robots representations of space in order to develop a mixed reasoning. We propose here an ontology called SpaceOntology as a spatial knowledge representation and reasoning system. In this paper, we focus on this ontology , and show how this type of knowledge can be profitably used for task planning. Our objective is to incorporate this ontology into a planner by extending the planning language PDDL.
Formation control gains significant attention in the multi-robot system field as it contributes to a vast range of applications, such as transportation. This paper presents a constrained optimization-based control law for cooperative logistics mission, which consists of rigid shape formation control, group navigation, individual and team obstacle avoidance tasks. These tasks are defined as equality and inequality constraints with different levels of priority. Hierarchical quadratic programming (HQP) approach is used to solve for the optimal solution with an inclusion of velocity limits as inequality constraints to ensure implementation feasibility. Experiment using actual industrial robots is demonstrated in order to validate the theory.
Abstract.Over the past few years, several alternative approaches have been suggested to represent the spatial knowledge that emerges from natural environments. This paper introduces a rule-based approach whose objective is to generate a spatial semantic network derived from several humans reporting a navigation process in a natural environment. Verbal descriptions are decomposed and characterized by a graph-based model where actions and landmarks are the main abstractions. A set of rules implemented as first order predicate calculus are identified and applied, and allow to merge the common knowledge inferred from route descriptions. A spatial semantic network is derived and provides a global and semantic view of the environment. The whole approach is illustrated by a case study and some preliminary experimental results.
In this paper, Cyber-Physical-Social Systems (CPSS) explicitly refers to the collaborative robots for industry. Battery autonomy is a predominant factor that limits the operating time of a cobot. As a consequence, it becomes essential for it to minimize energy consumption while satisfying all its constraints including real-time ones. This paper reviews the current state-of-the-art relating to approaches for energy efficiency specifically dedicated to cobots. The analysis indicates that energy harvesting technology combined to energy-aware real-time schedulers and energy-efficient path planners could permit to build energy-neutral CPSS.
Over the past few years, a series of computational and semantic frameworks have been developed to model and represent the spatial and temporal properties of moving entities. Despite the interest of these contributions, it is recognized that there is still a need for a qualitative reasoning support at the abstract and formal levels. The research presented in this paper introduces a qualitative approach for representing and manipulating moving entities. The model combines topological relations with qualitative distances over a spatial and temporal framework. Several basic movement configurations over dynamic entities are identified as well as movement transitions. The whole approach is illustrated in the context of the analysis of flight patterns.
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