Towards reliable real-time trajectory optimization
Date
2024-06-03
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Abstract
Liikumise planeerimine on robootika põhiaspekt, mis võimaldab robotitel liikuda läbi keeruliste ja muutuvate keskkondade. Levinud lähenemisviis liikumise planeerimise probleemide lahendamiseks on trajektoori optimeerimine. Trajektoori optimeerimine võib matemaatiliste aparatuuride kaudu esindada robotite kõrgtasemelist käitumist. Siiski praegustel trajektoori optimeerimise lähenemisviisidel on kaks peamist väljakutset. Esiteks, sõltub nende lahendus suuresti esialgsest oletusest ja nad kipuvad takerduma kohalike miinimumidesse. Teiseks seisavad nad silmitsi mastaapsuse piirangutega, kuna kitsenduste arv suureneb.
Antud doktoritöö püüab nende väljakutsetega toime tulla, tutvustades nelja uuenduslikku trajektoori optimeerimise algoritmi, et parandada usaldusväärsust, mastaapsust ja arvutusliku efektiivsust.
Pakutud algoritmidel on kaks uudset aspekti. Esimene oluline uuendus on kinemaatiliste kitsenduste ja kokkupõrke vältimise kitsenduste ümberkujundamine. Teine oluline uuendus seisneb algoritmide väljatöötamises, mis kasutavad tõhusalt graafikaprotsessori kiirendite paralleelset arvutust. Kasutades ümbersõnastatud kitsendusi ja võimendades graafikaprotsessoritede arvutusvõimsust, näitavad selle lõputöö pakutud algoritmid oluliselt tõhususe ja mastaapsuse paranemist võrreldes olemasolevate meetoditega. Paralleelarvutus võimaldab kiiremat arvutusaega, võimaldades dünaamilistes keskkondades reaalajas otsuseid langetada. Lisaks on algoritmid loodud kohanema keskkonnamuutustega, tagades tugeva jõudluse isegi tundmatu- tes ja segastes tingimustes.
Iga pakutud optimeerija põhjalik võrdlusanalüüs kinnitab nende tõhusust. Tänu põhjalikule hindamisele ületavad pakutud algoritmid pidevalt tipptasemel meetodeid erinevate mõõdustike kaudu, näiteks sujuvuse kulude ja arvutusaja osas. Need tulemused rõhutavad pakutud trajektoori optimeerimise algoritmide potentsiaali robootikarakenduste liikumise planeerimise tipptasemel märkimisväärselt edendada.
Kokkuvõttes annab antud doktoritöö olulise panuse trajektoori optimeerimise algoritmide valdkonnale. See tutvustab uuenduslikke lahendusi, mis käsitlevad konkreetselt olemasolevate meetodite ees seisvaid väljakutseid. Kavandatud algoritmid sillutavad teed tõhusamatele ja jõulisematele liikumisplaneerimise lahendustele robootikas, võimendades paralleelset arvutust ja spetsiifilisi matemaatilisi struktuure.
Motion planning is a key aspect of robotics, allowing robots to move through complex and changing environments. A common approach to address motion planning problems is trajectory optimization. Trajectory optimization can represent the high-level behaviors of robots through mathematical formulations. However, current trajectory optimization approaches have two main challenges. Firstly, their solution heavily depends on the initial guess, and they are prone to get stuck in local minima. Secondly, they face scalability limitations by increasing the number of constraints. This thesis endeavors to tackle these challenges by introducing four innovative trajectory optimization algorithms to improve reliability, scalability, and computational efficiency. There are two novel aspects of the proposed algorithms. The first key innovation is remodeling the kinematic constraints and collision avoidance constraints. Another key innovation lies in the design of algorithms that effectively utilize parallel computation on GPU accelerators. By using reformulated constraints and leveraging the computational power of GPUs, the proposed algorithms of this thesis demonstrate significant improvements in efficiency and scalability compared to the existing methods. Parallelization enables faster computation times, allowing for real-time decision-making in dynamic environments. Moreover, the algorithms are designed to adapt to changes in the environment, ensuring robust performance even in unknown and cluttered conditions. Extensive benchmarking for each proposed optimizer validates their efficacy. Through comprehensive evaluation, the proposed algorithms consistently outperform state-of-the-art methods across various metrics, such as smoothness costs and computation time. These results highlight the potential of the proposed trajectory optimization algorithms to significantly advance the state-of-the-art in motion planning for robotics applications. Overall, this thesis makes a significant contribution to the field of trajectory optimization algorithms. It introduces innovative solutions that specifically address the challenges faced by existing methods. The proposed algorithms pave the way for more efficient and robust motion planning solutions in robotics by leveraging parallel computation and specific mathematical structures.
Motion planning is a key aspect of robotics, allowing robots to move through complex and changing environments. A common approach to address motion planning problems is trajectory optimization. Trajectory optimization can represent the high-level behaviors of robots through mathematical formulations. However, current trajectory optimization approaches have two main challenges. Firstly, their solution heavily depends on the initial guess, and they are prone to get stuck in local minima. Secondly, they face scalability limitations by increasing the number of constraints. This thesis endeavors to tackle these challenges by introducing four innovative trajectory optimization algorithms to improve reliability, scalability, and computational efficiency. There are two novel aspects of the proposed algorithms. The first key innovation is remodeling the kinematic constraints and collision avoidance constraints. Another key innovation lies in the design of algorithms that effectively utilize parallel computation on GPU accelerators. By using reformulated constraints and leveraging the computational power of GPUs, the proposed algorithms of this thesis demonstrate significant improvements in efficiency and scalability compared to the existing methods. Parallelization enables faster computation times, allowing for real-time decision-making in dynamic environments. Moreover, the algorithms are designed to adapt to changes in the environment, ensuring robust performance even in unknown and cluttered conditions. Extensive benchmarking for each proposed optimizer validates their efficacy. Through comprehensive evaluation, the proposed algorithms consistently outperform state-of-the-art methods across various metrics, such as smoothness costs and computation time. These results highlight the potential of the proposed trajectory optimization algorithms to significantly advance the state-of-the-art in motion planning for robotics applications. Overall, this thesis makes a significant contribution to the field of trajectory optimization algorithms. It introduces innovative solutions that specifically address the challenges faced by existing methods. The proposed algorithms pave the way for more efficient and robust motion planning solutions in robotics by leveraging parallel computation and specific mathematical structures.
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