In this paper, we propose a framework for designing sliding mode controllers for a class of mechanical systems with symmetry, both unconstrained and constrained, that evolve on principal fiber bundles. Control laws are developed based on the reduced motion equations by exploring symmetries, leading to a sliding mode control strategy where the reaching stage is executed on the base space, and the sliding stage is performed on the structure group. Thus, design complexity is reduced, and difficult choices for coordinate representations when working with a particular Lie group are avoided. For this purpose, a sliding subgroup is constructed on the structure group based on a kinematic controller, and the sliding variable will converge to the identity of the state manifold upon reaching the sliding subgroup. A reaching law based on a general sliding vector field is then designed on the base space using the local form of the mechanical connection to drive the sliding variable to the sliding subgroup, and its time evolution is given according to the appropriate covariant derivative. Almost global asymptotic stability and local exponential stability are demonstrated using a Lyapunov analysis. We apply the results to a fully actuated system (a rigid spacecraft actuated by reaction wheels) and a subactuated nonholonomic system (unicycle mobile robot actuated by wheels), which is also simulated for illustration.

The nonlinearity and hysteresis of soft robot motions have posed challenges in accurate soft robot control. Neural networks, especially recurrent neural networks (RNNs), have been widely leveraged for this issue due to their nonlinear activation functions and recurrent structures. Although they have shown satisfying accuracy in most tasks, these black-box approaches are not explainable, and hence, they are unsuitable for areas with high safety requirements, like robot-assisted surgery. Based on the RNN controllers, we propose a data-driven explainable controller (DDEC) whose parameters can be updated online. We discuss the Jacobian controller and kinematics controller in theory and demonstrate that they are only special cases of DDEC. Moreover, we utilize RNN, the Jacobian controller, the kinematics controller, and DDECs for trajectory following tasks. Experimental results have shown that our approach outperforms the other controllers considering trajectory following errors while being explainable. We also conduct a study to explore and explain the functions of each DDEC component. This is the first interpretable soft robot controller that overcomes the shortcomings of both NN controllers and interpretable controllers. Future work may involve proposing different DDECs based on different RNN controllers and exploiting them for high-safety-required applications.

While Visual Servoing is deeply studied to perform simple maneuvers, the complex cases where the target is far out of the camera field of view during the maneuver are not common in the literature. For this reason, in this paper, we present ViKi-HyCo (Visual Servoing and Kinematic Hybrid-Controller). This approach generates the necessary maneuvers for the complex positioning of a non-holonomic mobile robot in outdoor environments. In this method, we use camera-LiDAR fusion for automatic target calculation. The multi-modal nature of our target representation allows us to hybridize the visual servoing with a kinematic controller. In this way, we can perform complex maneuvers even when the target is far away from the camera's field of view. The automatic target calculation is performed through object localization for outdoor environments that estimate the spatial location of a target point for the kinematic controller and allow the dynamic calculation of a desired bounding box of the detected object for the visual servoing controller. The presented approach does not require an object-tracking algorithm and applies to any visually tracking robotic task where its kinematic model is known. The ViKi-HyCo gives an error of 0.0428 \pm 0.0467 m in the X-axis and 0.0515 \pm 0.0323 m in the Y-axis at the end of a complete positioning task.

Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to replace inner components without highly affecting system performance, which is interchangeability. Due to the necessity of this property, a hybrid adaptive controller is introduced to achieve interchangeability from the perspective of control approaches. This method utilizes an offline-trained recurrent neural network controller to cope with the nonlinear and delayed response from soft robots. Furthermore, an online optimizing kinematics controller is applied to decrease the error caused by the above neural network controller. Soft pneumatic robots with different deformation properties but the same mold have been included for validation experiments. In the experiments, the systems with different actuation configurations and the different robots follow the desired trajectory with errors of 3.3 +- 2.9% and 4.3 +- 4.1% compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller is also compared with a model-based controller in simulation. This controller endows soft robots with the potential for wide application, and future work may include different offline and online controllers. A weight parameter adjusting strategy may also be proposed in the future.

This paper summarizes the progress in developing a rugged, low-cost, automated ground cone robot network capable of traffic delineation at lane-level precision. A holonomic omnidirectional base with a traffic delineator was developed to allow flexibility in initialization. RTK GPS was utilized to reduce minimum position error to 2 centimeters. Due to recent developments, the cost of the platform is now less than $1,600. To minimize the effects of GPS-denied environments, wheel encoders and an Extended Kalman Filter were implemented to maintain lane-level accuracy during operation and a maximum error of 1.97 meters through 50 meters with little to no GPS signal. Future work includes increasing the operational speed of the platforms, incorporating lanelet information for path planning, and cross-platform estimation.