Department Publications

Large debris created after natural disasters restrict access to inner parts of affected regions and slows down disaster relief operations. Power shovels are often used to clear wreckage but the process can take a lot of time. Moreover, it is dangerous to involve human workers in such conditions due to loose debris collapsing and causing severe damage. To speedup access to inner areas, instead of removing obstacles, they can be surmounted with the assistance of a carefully maneuvered power shovel arm. In this paper, an autonomous obstacle surmounting technique for an unmanned power shovel is proposed. Out of different sequences, the one that optimizes the total energy consumption is selected as the best candidate for surmounting a given obstacle by dynamic simulations. The experiments conducted using a miniaturized power shovel show the effectiveness of the proposed method.

Large debris created after natural disasters restrict access to inner parts of affected regions, and slows down disaster relief operations. Power shovels are often used to clear wreckage but the process can take a lot of time. Moreover, it is dangerous to involve human workers operating heavy machinery in such unstable conditions. To speedup access to inner areas, obstacles can be surmounted with the assistance of carefully maneuvered power shovel arm, instead of removing them. In this work, an autonomous obstacle surmounting technique for an unmanned power shovel is proposed. Out of different sequences, the one that optimizes the total energy consumption is chosen as the best candidate for surmounting a given step-like obstacle. Dynamic simulation results show the effectiveness of the proposed method.

Pitch and airspeed responses of a medium scale UAV were identified using the prediction error method. Data collection was conducted with an active closed loop autopilot control system. A naive SISO open loop transfer function was estimated using the parametric direct approach. Since its predictability was not significant, better estimators were developed using MISO parametric direct, and SISO parametric indirect method. The model validations were done to quantify their predictability.

Power shovels are used to clear wreckage after natural disasters but the process can consume a lot of time. Furthermore, operating such heavy machines in those circumstances is very dangerous for the involved human workers. To speed up access to inner parts of disaster stricken areas, obstacles can be surmounted with the assistance of carefully maneuvered arm, instead of removing them. In this paper, an autonomous obstacle surmounting technique for an unmanned power shovel is proposed. Out of different sequences, the one that optimizes the total energy consumption is chosen as the best candidate for surmounting a given step-like obstacle. Simulation and experiment results show the effectiveness of the proposed method.