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Model-based strategies are a promising solution to the grand challenge of equipping continuum soft robots with motor intelligence. However, finite-dimensional models of these systems are inherently inaccurate, thus posing pressing robustness concerns. Moreover, the actuation space of soft robots is usually limited. This article aims at solving both these challenges by proposing a robust model-based strategy for the shape control of soft robots with system uncertainty and input saturation. The proposed architecture is composed of two key components. First, we propose an observer that estimates deviations between the theoretical model and the soft robot, ensuring that the estimation error converges to zero within finite time. Second, we introduce a sliding mode controller to regulate the soft robot shape while fulfilling saturation constraints. This controller uses the observer's output to compensate for the deviations between the real system and the established model. We prove the convergence of the closed-loop with theoretical analysis and the method's effectiveness with simulations and experiments., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

This letter investigates the direct trajectory optimization of the free-floating space manipulator (FFSM). The main purpose is to plan the joint space trajectories to reduce the spacecraft motion due to the joint rotation during the FFSM performing tasks. To improve the calculation efficiency, the adaptive Radau pseudospectral method (A-RPM) is applied to discretize the system dynamics and transform the formulated optimal problem into a nonlinear programming problem (NLP). By adaptively subdividing the current segment and assigning collocation points according to the solution error, high-degree interpolation polynomials are avoided. To verify the effectiveness of the proposed method, a ground micro-gravity platform of the FFSM system is designed by using the air-bearing technique, on which experiments are carried out. The results show that the variation of the base spacecraft is dramatically reduced if the joints rotate along the optimized trajectories., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

This paper proposes a method to detect dynamic objects in the images obtained by a small UAV. Two geometric constraints in multi-view images are used to classify each of the extracted featurepoints as static or dynamic. The first constraint is the epipolar constraint which requires static points to lie on the corresponding epipolar lines in the subsequent image. The second constraint, named as flow-vector bound constraint here, restricts the motion of static points along the epipolar lines. In addition, the pose of the UAV-borne camera, which is required when applying these constraints, is estimated by using a vision-based SLAM method, PTAM. The proposed method fully exploits the characteristics of UAV-borne images and achieves satisfactory results. The algorithms were tested with a small quadrotor platform in a real-world scene and successfully detected features extracted from multiple pedestrians.

This paper proposes a method to detect dynamic objects in the images obtained by a small UAV. Two geometric constraints in multi-view images are used to classify each of the extracted featurepoints as static or dynamic. The first constraint is the epipolar constraint which requires static points to lie on the corresponding epipolar lines in the subsequent image. The second constraint, named as flow-vector bound constraint here, restricts the motion of static points along the epipolar lines. In addition, the pose of the UAV-borne camera, which is required when applying these constraints, is estimated by using a vision-based SLAM method, PTAM. The proposed method fully exploits the characteristics of UAV-borne images and achieves satisfactory results. The algorithms were tested with a small quadrotor platform in a real-world scene and successfully detected features extracted from multiple pedestrians.