9,333 research outputs found
Artificial Intelligence and Systems Theory: Applied to Cooperative Robots
This paper describes an approach to the design of a population of cooperative
robots based on concepts borrowed from Systems Theory and Artificial
Intelligence. The research has been developed under the SocRob project, carried
out by the Intelligent Systems Laboratory at the Institute for Systems and
Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the
project stands both for "Society of Robots" and "Soccer Robots", the case study
where we are testing our population of robots. Designing soccer robots is a
very challenging problem, where the robots must act not only to shoot a ball
towards the goal, but also to detect and avoid static (walls, stopped robots)
and dynamic (moving robots) obstacles. Furthermore, they must cooperate to
defeat an opposing team. Our past and current research in soccer robotics
includes cooperative sensor fusion for world modeling, object recognition and
tracking, robot navigation, multi-robot distributed task planning and
coordination, including cooperative reinforcement learning in cooperative and
adversarial environments, and behavior-based architectures for real time task
execution of cooperating robot teams
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning
International audienceIn this paper we present a novel approach for multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning. Firstly, an adequate number of points should be generated in C-space on an occupancy grid map by using an adaptive cross sampling method. Secondly, a roadmap should be built while the potential targets and the milestones are extracted by second learning the result of sampling. Finally, the motion of robots should be planned by querying the constructed roadmap. In contrast to previous approaches, our ACS-PRM approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the planning efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be significantly reduced by our ACS-PRM approach compared with previous approaches
Towards a Probabilistic Roadmap for Multi-robot Coordination
International audienceIn this paper, we discuss the problem of multi-robot coordination and propose an approach for coordinated multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, called ACS-PRM, is a sampling-based method and consists of three steps including C-space sampling, roadmap building and motion planning. In contrast to previous approaches, our approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the system efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be obviously reduced by our ACS-PRM approach compared with previous approaches
Optimization Model for Planning Precision Grasps with Multi-Fingered Hands
Precision grasps with multi-fingered hands are important for precise
placement and in-hand manipulation tasks. Searching precision grasps on the
object represented by point cloud, is challenging due to the complex object
shape, high-dimensionality, collision and undesired properties of the sensing
and positioning. This paper proposes an optimization model to search for
precision grasps with multi-fingered hands. The model takes noisy point cloud
of the object as input and optimizes the grasp quality by iteratively searching
for the palm pose and finger joints positions. The collision between the hand
and the object is approximated and penalized by a series of least-squares. The
collision approximation is able to handle the point cloud representation of the
objects with complex shapes. The proposed optimization model is able to locate
collision-free optimal precision grasps efficiently. The average computation
time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise
of the point cloud. The effectiveness of the algorithm is demonstrated by
experiments.Comment: Submitted to IROS2019, experiment on BarrettHand, 8 page
A Mixed-Integer Linear Programming Formulation for Human Multi-Robot Task Allocation
In this work, we address a task allocation problem for human multi-robot
settings. Given a set of tasks to perform, we formulate a general Mixed-Integer
Linear Programming (MILP) problem aiming at minimizing the overall execution
time while optimizing the quality of the executed tasks as well as human and
robotic workload. Different skills of the agents, both human and robotic, are
taken into account and human operators are enabled to either directly execute
tasks or play supervisory roles; moreover, multiple manipulators can tightly
collaborate if required to carry out a task. Finally, as realistic in human
contexts, human parameters are assumed to vary over time, e.g., due to
increasing human level of fatigue. Therefore, online monitoring is required and
re-allocation is performed if needed. Simulations in a realistic scenario with
two manipulators and a human operator performing an assembly task validate the
effectiveness of the approach.Comment: Accepted to 2021 IEEE International Conference on Robot and Human
Interactive Communication (RO-MAN
A Survey and Analysis of Multi-Robot Coordination
International audienceIn the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper
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