A model of the movement of a robotic platform adapted to the conditions of an industrial orchard is proposed. (Research purpose) Development of a motion control system for an autonomous robotic wheeled platform based on inertial and satellite navigation and traversed path calculation, which will allow it to move in an apple orchard and automatically perform various technological operations, such as fertilization, growth diseases control of, fruit harvesting. (Materials and methods) A mathematical model was developed to control the movement of a robotic platform, taking into account the turning radii of three types, the length of the arc of the performed circle, the speed of movement in the garden plantation rows using a garden electronic map. The method used allows implementing a program for the robotic platform automatic movement around a typical orchard using a minimum set of sensors, significantly reducing the load on the onboard computer processor and memory. Software, developed in the Python programming language, enables plotting the robotic platform route, displaying the movement trajectory, and indicating the positioning accuracy at each point in relation to the trees in the garden plantation rows, the movement speed and the wheel rotation angle. (Results and discussion) The robotic platform managed to autonomously pass the preset routes, while the interaction of the software and the robotic platform hardware was provided. A field testing of the developed software was performed. (Conclusions) The specified accuracy of the robotic platform positioning was confirmed for the 3.5-meter aisles of intensive orchards. The maximum deviation from the task map using satellite and inertial navigation system was 164 millimeters, which complies with the agrotechnical requirements for mechanized fruit harvesting.
When a quadruped robot is climbing stairs, due to unexpected factors, such as the size of the differing from the international standard or the stairs being wet and slippery, it may suddenly fall down. Therefore, solving the self-recovery problem of the quadruped robot after falling is of great significance in practical engineering. This is inspired by the self-recovery of crustaceans when they fall; the swinging of their legs will produce a resonance effect of a specific body shape, and then the shell will swing under the action of external force, and self-recovery will be achieved by moving the center of gravity. Based on the bionic mechanism, the kinematics model of a one-leg swing and the self-recovery motion model of a falling quadruped robot are established in this paper. According to the established mathematical model, the algorithm training environment is constructed, and a control strategy based on the reinforcement learning algorithm is proposed as a controller to be applied to the fall self-recovery of quadruped robots. The simulation results show that the quadruped robot only takes 2.25 s to achieve self-recovery through DDPG on flat terrain. In addition, we compare the proposed algorithm with PID and LQR algorithms, and the simulation experiments verify the superiority of the proposed algorithm.
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