2,321 research outputs found
Rajaplaneerimine multi-robot sĂŒsteemile jagatud lasti transportimisel
Shared payload transportation has emerged as one of the key real-world applications that
warrants the deployment of multiple robots. The key motivation stems from the fact that
actuation and sensing abilities of multiple robots can be pooled together to transport objects
that are either too big or heavy to be handled by a single robot. This thesis proposes
algorithmic and software frameworks to achieve precise multi-robot coordination for object
transportation. On the algorithmic side, a trajectory optimization formulation is developed
which generates collision-free and smooth trajectories for the robots transporting the object.
State-of-the art Gradient Descent variants are utilized for obtaining the solution. On the
software side, a trajectory planner (local planner) is developed and integrated to Robot
Operating System (ROS). The local planner is responsible for calculating individual velocities
for any number of robots forming a rigid geometric in-plane constellation. Extensive simulation
as well as real-world experiments are performed to demonstrate the validity of the developed
solutions. It is demonstrated that how the proposed trajectory optimization approach
outperforms off-the-shelf planners with respect to metrics like smoothness and collision
avoidance.
In estonian: Ăhise lasti transportimine mitme roboti poolt on kujunenud ĂŒheks rakendusvaldkonnaks, kus
mitme roboti samaaegne kasutamine on Ôigustatud. Mitme roboti andureid ja ajameid on eriti
kasulik kasutada transportimaks objekte, mis on ĂŒhe roboti jaoks kas liiga suured ja/vĂ”i rasked.
KÀesolev lÔputöö pakub vÀlja algoritmilise ja tarkvaralise raamistiku, mis vÔimaldab tÀpselt
koordineerida mitme roboti koostööd ĂŒhise lasti liigutamisel. VĂ€lja on töötatud trajektooride
optimeerimise algoritm, mis genereerib kokkupĂ”rkevabad ja sujuvad ĂŒhist objekti kandvate
robotite trajektoorid. Selleks on kasutatud nĂŒĂŒdisaegset gradientlaskumise (ingl Gradient
Descent) meetodit. Tarkvara poolelt on loodud trajektoori planeerija (lokaalne planeerija) ja
see on integreeritud arendusplatvormil ROS (Robot Operating System). Lokaalne planeerija
arvutab individuaalsed kiirused igale robotile, mis moodustavad ĂŒhise jĂ€iga tasapinnalise
kujundi, kusjuures robotite arv kujundis ei ole piiratud. VÀljatöötatud lahenduse toimimist on
kontrollitud ulatuslike simulatsioonide abil aga ka viies lÀbi praktilisi katseid. VÀljapakutud
trajektoori optimeerimise lahendus ĂŒletab olemasolevaid planeerijaidd nii trajektoori sujuvuse
kui ka kokkupÔrgete vÀltimise vÔime osas
An enhanced classifier system for autonomous robot navigation in dynamic environments
In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad
Attractor dynamics approach to joint transportation by autonomous robots: theory, implementation and validation on the factory floor
This paper shows how non-linear attractor dynamics can be used to control teams of two autonomous mobile robots that coordinate their motion in order to transport large payloads in unknown environments, which might change over time and may include narrow passages, corners and sharp U-turns. Each robot generates its collision-free motion online as the sensed information changes. The control architecture for each robot is formalized as a non-linear dynamical system, where by design attractor states, i.e. asymptotically stable states, dominate and evolve over time. Implementation details are provided, and it is further shown that odometry or calibration errors are of no significance. Results demonstrate flexible and stable behavior in different circumstances: when the payload is of different sizes; when the layout of the environment changes from one run to another; when the environment is dynamice.g. following moving targets and avoiding moving obstacles; and when abrupt disturbances challenge team behavior during the execution of the joint transportation task.- This work was supported by FCT-Fundacao para a Ciencia e Tecnologia within the scope of the Project PEst-UID/CEC/00319/2013 and by the Ph.D. Grants SFRH/BD/38885/2007 and SFRH/BPD/71874/2010, as well as funding from FP6-IST2 EU-IP Project JAST (Proj. Nr. 003747). We would like to thank the anonymous reviewers, whose comments have contributed to improve the paper
Cooperative transport in swarm robotics. Multi object transportation
Swarm robotics is a research field inspired from the natural behavior of ants, bees or fish in their natural habitat. Each group display swarm behavior in different ways. For example, ants use pheromones to trace one another in order to find a nest, reach a food source or do any operation, while bees use dance moves to attract one another to the desired place. In swarm robotics, small robots attempt to mimic insect behavior. The robotic swarm group collaborate to perform a task and collectively solve a given problem. In the process, the robots use the sensors they are equipped with to move, communicate or avoid obstacles until they collectively do the desired functionality. In this thesis, we propose a modification to the Robotic Darwinian Particle Swarm Optimization (RDPSO) algorithm. In the RDPSO, robots deployed in a rescue operation, transport one object at a time to a desired safe place. In our algorithm, we simultaneously transport multiple objects to safety. We call our algorithm Multi Robotics Darwinian Particle Swarm Optimization (MRDPSO). Our algorithm is developed and implemented on a VREP simulator using ePuck robots as swarm members. We test our algorithm using two different environment sizes complete with obstacles. First implementation is for two simultaneous object transported but can be extended to more than two. We compare our new algorithm to the results of single RDPSO and found our algorithm to be 35 to 41 % faster. We also compared our results to those obtained from three selected papers that are Ghosh, Konar, and Janarthanan [1], TORABI [2], and Kube and Bonabeau [3]. The performance measures we compare to are the accuracy of transporting all objects to desired location, and the time efficiency of transporting all the objects in our new system
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