Quadrupedal robots play a vital role in various applications, from search and rescue operations to exploration in challenging terrains. However, locomotion tasks involving unknown payload transportation on rough terrains pose significant challenges, requiring adaptive control strategies to ensure stability and performance. This dissertation contributes to the advancement of adaptive motion planning and control solutions that enable quadrupedal robots to traverse unknown rough environments while tasked with transporting unknown payloads. In the first project, a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots is developed. This framework integrates an adaptive model predictive control (AMPC) algorithm with a gradient-descent-based adaptive updating law applied to reduced-order locomotion (i.e., template) models. At the high level of the control hierarchy, an indirect adaptive law estimates unknown parameters of the reduced-order locomotion model under varying payloads, ensuring stability during trajectory planning. The optimal trajectories generated by the AMPC are then passed to a low-level and full-order nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations and hardware experiments on the A1 quadru[pedal robot validate the framework's capabilities, showcasing significant improvements in payload transportation on both flat and rough terrains compared to conventional MPC strategies. Specifically, the robot demonstrates proficiency in transporting unmodeled, unknown static payloads up to 109% of its own mass in experiments on flat terrains and 91% on rough experimental terrains. Moreover, the robot successfully manages dynamic payloads with 73% of its mass on rough terrains. Adaptive controllers must also address external disturbances inherent in real-world environments. Therefore, the second project introduces a hierarchical planning and control scheme with an adaptive L1 nonlinear model predictive control (ANMPC) at the high level, which integrates nonlinear MPC (NMPC) with an L1 adaptive controller. The prescribed optimal state and control input profiles generated by the ANMPC are then fed to the low-level nonlinear WBC. This approach aims to stabilize locomotion gaits in the presence of parametric uncertainties and external disturbances. The proposed controller is analyzed to accommodate uncertainties and external disturbances. Comprehensive numerical simulations and experimental validations on the A1 quadrupedal robot demonstrate its effectiveness on rough terrains. Numerical results suggest that ANMPC significantly improves the stability of the gaits in the presence of uncertainties and external disturbances compared to NMPC and AMPC. The robot can carry payloads up to 109% of its own mass on its trunk on flat and rough terrains. Simulation results show that the robot achieves a maximum payload capacity of 26.3 (kg), which is equivalent to 211% of its own mass on rough terrains with uncertainties and disturbances. / Doctor of Philosophy / In the rapidly advancing domain of robotics, there is a growing demand for intelligent robotic systems capable of adeptly addressing novel and unforeseen scenarios, such as uneven paths or external forces applied to the robots, like kicks and hits. This necessitates robots with the capability to handle diverse tasks with precision, particularly in the domains of object transportation and navigation through unknown terrains in applications such as search and rescue operations or cargo handling. This dissertation introduces innovative motion planning and control frameworks designed to imbue robots with adaptive capabilities, enabling them to adapt to real-world unanticipated scenarios and uncertainties during their movement, particularly when carrying unknown payloads. In the first project, a new framework is developed to enhance payload transportation by quadrupedal robots. This framework integrates an adaptive model predictive control (AMPC) algorithm with a gradient-descent-based adaptive updating law. Through extensive experiments and simulations, the framework shows remarkable improvements in payload transportation on both flat and rough terrains. The robot successfully transports payloads exceeding its own mass by up to 109% on flat terrains and 91% on rough terrains. Recognizing the need to address uncertainties in real-world environments, the second project introduces a hierarchical planning and control scheme with adaptive L1 nonlinear model predictive control (ANMPC). This approach stabilizes legged locomotion in the presence of uncertainties and disturbances. Results demonstrate that ANMPC significantly improves gait stability compared to existing methods. The robot achieves a payload capacity of up to 109% of its own mass on both experimental flat and rough terrains and reaches a maximum of 26.3 kg (around 212% of its own mass) on rough terrain simulations with uncertainties and disturbances.

Quadrupedal robots are mobile robots with four limbs. Compared with other mobile robots, quadrupedal robots are more capable of moving in complex environment. Specifically, softquadrupedal robots have the limbs that are flexible and more compliant with the environmentthan that of rigid quadrupedal robots. This project is based on a previous work at KTH where a soft quadrupedal robot prototype was built. The first part of this project is to build a test rig, analyze the dynamics of the 3D printed soft continuum actuators and choose one configuration toachieve the best dynamics. The second part of this project is to build a soft quadrupedal robotand analyze the standing and walking performance. The mechanical and electrical structure ofthe robot are re-designed to reduce the weight. Furthermore, gait analyses are conducted toenable the robot to walk. Cost of transport is calculated to compare the efficiency of differentgaits. / Mobila robotar som har fyra lemmar kallas fyrbenta robotar. Jämfört med andra mobila robotarär fyrbenta robotar mer kapabla att röra sig i komplexa miljöer. Särskild de mjuka fyrbentarobotar, vars flexibla lemmar är mer kompatibla med miljön än dem av stela fyrbenta robotar. Det här projektet är baserat på ett tidigare arbete på KTH där prototypen av en mjuk fyrbentrobot byggdes. Den första delen av detta projekt är att bygga en provrigg, analysera dynamikenav det 3D-skrivna mjuka kontinuumställdon och välja den konfigurationen som har bästadynamiken. Den andra delen av detta projekt är att bygga en mjuk fyrbent robot och analyseradess stå- och gångprestation. Den mekaniska och elektriska strukturen av roboten designades omför att minska vikten. Vidare är gångs analyser genomförda för att möjliggöra robotens gång. Cost of transport (COT) är uträknat för att jämföra olika gångs effektivitet.