2 research outputs found
Rajaplaneerimine multi-robot süsteemile 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: Ühise lasti transportimine mitme roboti poolt on kujunenud üheks rakendusvaldkonnaks, kus
mitme roboti samaaegne kasutamine on õigustatud. Mitme roboti andureid ja ajameid on eriti
kasulik kasutada transportimaks objekte, mis on ühe roboti jaoks kas liiga suured ja/või rasked.
Käesolev lõputöö pakub välja algoritmilise ja tarkvaralise raamistiku, mis võimaldab täpselt
koordineerida mitme roboti koostööd ühise lasti liigutamisel. Välja on töötatud trajektooride
optimeerimise algoritm, mis genereerib kokkupõrkevabad ja sujuvad ühist objekti kandvate
robotite trajektoorid. Selleks on kasutatud nüüdisaegset 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 ühise jäiga tasapinnalise
kujundi, kusjuures robotite arv kujundis ei ole piiratud. Väljatöötatud lahenduse toimimist on
kontrollitud ulatuslike simulatsioonide abil aga ka viies läbi praktilisi katseid. Väljapakutud
trajektoori optimeerimise lahendus ületab olemasolevaid planeerijaidd nii trajektoori sujuvuse
kui ka kokkupõrgete vältimise võime osas
Optimal Mission Planning of Autonomous Mobile Agents for Applications in Microgrids, Sensor Networks, and Military Reconnaissance
As technology advances, the use of collaborative autonomous mobile systems for various applications will become evermore prevalent. One interesting application of these multi-agent systems is for autonomous mobile microgrids. These systems will play an increasingly important role in applications such as military special operations for mobile ad-hoc power infrastructures and for intelligence, surveillance, and reconnaissance missions. In performing these operations with these autonomous energy assets, there is a crucial need to optimize their functionality according to their specific application and mission. Challenges arise in determining mission characteristics such as how each resource should operate, when, where, and for how long.
This thesis explores solutions in determining optimal mission plans around the applications of autonomous mobile microgrids and resource scheduling with UGVs and UAVs. Optimal network connections, energy asset locations, and cabling trajectories are determined in the mobile microgrid application. The resource scheduling applications investigate the use of a UGV to recharge wireless sensors in a wireless sensor network. Optimal recharging of mobile distributed UAVs performing reconnaissance missions is also explored. With genetic algorithm solution approaches, the results show the proposed methods can provide reasonable a-priori mission plans, considering the applied constraints and objective functions in each application. The contributions of this thesis are: (1) The development and analysis of solution methodologies and mission simulators for a-priori mission plan development and testing, for applications in organizing and scheduling power delivery with mobile energy assets. Applying these methods results in (2) the development and analysis of reasonable a-priori mission plans for autonomous mobile microgrids/assets, in various scenarios. This work could be extended to include a more diverse set of heterogeneous agents and incorporate dynamic loads to provide power to