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Due to the lack of experienced labor and high‐quality requirements, interior painting task calls for robotic solutions in the construction field. This paper presents a robotic system aiming for the automatic interior wall painting task. The new painting robot comprises an omnidirectional mobile base and a seven degrees‐of‐freedom (7‐DOF) redundant manipulator consisting of a 6‐DOF robot arm and a 1‐DOF lifting mechanism to make the painting task more flexible. Further, a building information modeling‐based three‐dimensional (3D) reconstruction approach is used to obtain the complete 3D model of all walls in the interior environment for automatic painting. Moreover, we propose a two‐stage coverage planning framework to automatically generate optimal mobile base paths and manipulator trajectories to paint the walls. In the proposed framework, the global planner plans the painting waypoints sequence optimally. The local planner generates the mobile base poses by a new evaluation function which both ensures coverage of all painting waypoints and optimizes robot paths length. The results of field tests showed that the whole painting robot system has high environment adaptability to paint interior walls automatically. Furthermore, the planning results can significantly reduce total robot paths length compared with the previous studies. The painting robot can automatically finish a painting of 46.3 m2 ${{\rm{m}}}^{2}$ in 59.3 min, and this performance verifies the proposed design and algorithms.

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This paper presents an optimal motion planning framework to generate versatile energy-optimal quadrupedal jumping motions automatically (e.g., flips, spin). The jumping motions via the centroidal dynamics are formulated as a 12dimensional black-box optimization problem subject to the robot kino-dynamic constraints. Gradient-based approaches offer great success in addressing trajectory optimization (TO), yet, prior knowledge (e.g., reference motion, contact schedule) is required and results in sub-optimal solutions. The new proposed framework first employed a heuristics-based optimization method to avoid these problems. Moreover, a prioritization fitness function is created for heuristics-based algorithms in robot ground reaction force (GRF) planning, enhancing convergence and searching performance considerably. Since heuristicsbased algorithms often require significant time, motions are planned offline and stored as a pre-motion library. A selector is designed to automatically choose motions with user-specified or perception information as input. The proposed framework has been successfully validated only with a simple continuously tracking PD controller in an open-source Mini-Cheetah by several challenging jumping motions, including jumping over a window-shaped obstacle with 30 cm height and left-flipping over a rectangle obstacle with 27 cm height. (Video ⋆

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