Efficient motion planning with the error tolerance is crucial for dynamic robotic tasks, particularly robotic table tennis. This task demands simultaneous high efficiency and error tolerance. First, the incoming ball’s high speed allows only tens of milliseconds for motion planning. Second, two types of errors, namely, ball-paddle motion uncertainty and joint limit violation, must be tolerated to ensure a high success rate of planning (SRP) and striking. This article proposes an advanced joint planning framework designed for high efficiency and error tolerance. To tolerate the error of ball-paddle motion uncertainty, this work introduces a joint classification criterion according to the joint motion characteristics. To tolerate the error of joint limit violation, based on the classification criterion, this study also develops a robust reference trajectory generation scheme, named error-tolerant-variable-sigmoid-based motion template (ETVSMT), to fully consider the motion capabilities of different joints. The ETVSMT approach utilizes the variable-sigmoid-based motion template (VSMT) as the backbone and designs its basic and remedial portions to tolerate the error of hard joint limit violation. The implementation of the ETVSMT scheme results in an average SRP of 98% and a ball-striking success rate of up to 90%, with execution time on an Intel Xeon CPU as low as 15 ms. Furthermore, the proposed method ensures the outgoing ball lands on the opposite side of the table with an average landing error of 19.62 cm and a net-passing height error of 12.68 cm. The proposed method can also benefit other dynamic tasks like human–robot interaction to improve the planning efficiency.

AFM based nanomanipulations have been successfully applied in various areas such as physics, biology and so forth in nano scale. Traditional nanomanipulations always have to approach the problems such as hysteresis, nonlinearity and thermal drift of the scanner, and the noise brought by the position sensor. In this research, a compressive feedbacks based non-vector space control approach is proposed for improving the accuracy of AFM based nanomanipulations. Instead of sensors, the local image was used as the feedback to a non-vector space controller to generate a closed-loop control for manipulation. In this paper, there are four research topics: First, local scan strategy was used to get a local image. Second, since the feedback is an image, a non-vector space controller was designed to deal with the difficulty in vector space such as calibration and coordinate transformation. Third, in order to further decrease the time spent on local scan, compressive sensing was introduced to this system. Finally, to overcome the disadvantage that compressive sensing costs time on reconstructing the original signal, we directly use the compressive data as the feedback. Both theoretical analysis and experimental results have shown that the system has a good performance on AFM tip motion control. Therefore, the non-vector space control method can make visual servoing easier, and the compressive feedback could make a high speed real-time control of nanomanipulation possible. In addition, this new method can be applied to nano-assembly, nano-imaging and nanomanipulation.