The suspension system, through effective damping of vibrations and shocks, can enhance the stability of wheeled robots traversing challenging terrain. Because the suspension system decouples the rigid correspondence between terrain changes and robot vibrations, considering suspension modeling in trajectory planning offers the advantage of more accurate prediction of the robot’s response to terrain. This improved predictive capability facilitates the planning of safer trajectories and may reduce tracking errors in the subsequent control process. In this work, inspired by the structure of Physics-Informed Neural Network (PINN), we propose a physics-informed planning method that considers the vibrational effects of complex nonlinear suspension systems. In addition, we design a two-stage process to accelerate training. By incorporating PINN, our method can better guarantee the physical feasibility of the planned trajectories. The proposed approach has been evaluated on a real robot platform. Compared to state-of-the-art baseline methods, our proposed approach achieves a 15.38% reduction in hazardous planning for mobile robots in wild environments.

It has been shown that learning radiance fields with depth rendering and depth supervision can effectively promote the quality and convergence of view synthesis. However, this paradigm requires input RGB-D sequences to be synchronized. In the UAV city modeling scenario, there exists asynchrony between RGB images and depth images due to the different frequencies of the solid-state LiDAR and RGB sensors. To synthesize high-quality views in such a scenario, we propose a novel time-pose function, which is an implicit network that maps timestamps to SE(3) elements. To train this function, we also design a joint optimization scheme to jointly learn the large-scale depth-regularized radiance fields and the time-pose function. Furthermore, we propose a large synthetic dataset with diverse controlled mismatches and ground truth to evaluate this new problem setting systematically. The proposed approach has been evaluated on both datasets and in a real drone. To evaluate the impact of view density, each algorithm was test on three different trajectories with different view densities. Compared to state-of-the-art baseline methods, the proposed approach reduces reconstruction error by 35.26% in city modeling scenarios. Our code is available at github.com/saythe17/AsyncNeRF.

In wild environments, motion planning for mobile robots faces the challenge of local optimal path traps due to limited sensor perception range and lack of spatial awareness. Existing approaches that avoid local optimum by designing heuristic functions or high-quality global paths in wild environments are time-consuming and unstable. This work proposes F3DMP, which consists of two parts to alleviate the local optimum solution and better utilize distant terrain information. First, the entire planning framework is adapted to the three-dimensional space so that the planning result conforms to the geometric characteristics of the terrain. Second, a time allocation function based on offline reinforcement learning is proposed. This function can anticipate potential challenges or opportunities based on semantic information for the image and proactively determine a time allocation. Our planner is integrated into a complete mobile robot system and deployed to a real robot. Experiments in simulation and the real world demonstrate that our method can improve the success rate by 28% and the trajectory smoothness by 27% compared with traditional methods.

Trajectory planning is important for ground robots to achieve safe and efficient autonomous navigation in unstructured off-road environments. Most existing methods treat each terrain as a single type. However, in the real world, a ground usually consists of hybrid terrains. In this work, we propose a novel trajectory planning network that handles hybrid terrain. To further enhance safety, we have designed a self-correcting structure based on historical planning data. This structure can correct the trajectory when an inappropriate one is planned. To train the network, we introduce a two-stage training scheme based on Offline Meta-Reinforcement Learning, which can train the network with pre-collected non-optimal datasets and reduce the occurrence of hazardous planning. The proposed approach has been evaluated on both simulated datasets and a real robot platform. Compared to state-of-the-art baseline methods, the proposed approach reduces hazardous planning by 59.3% in hybrid terrains.

We present AVS-HRL, a modular Active visual SLAM system based on hierarchical reinforcement learning. The reward function explicitly considers the efficiency of exploration and the accuracy of mapping by utilizing the internal variables of SLAM, such as feature points distribution and loop-closure signal. Compared to end-to-end active SLAM methods, we designed a map reconstruction module that can correct the cumulative error in the incremental mapping process. Furthermore, the inputs of all neural network modules use more abstract and general information, such as grid maps, rather than raw sensor observations. We conducted experiments in two different simulators and real-world environments. In the noisy setting of Habitat environments, our method improves the accuracy of the mapped areas by 68.48% as an average of Gibson and MP3D datasets. Moreover, our method's generalization performance was demonstrated through direct transfer across different simulators and real-world environments.

Model predictive control is a promising method in robot control tasks. How to design an effective model structure and efficient prediction framework for model predictive control is still an open challenge. To reduce the time consumption and avoid compounding-error of the multi-step prediction process in model predictive control, we propose a single-model simultaneous framework, which uses single dynamics model to predict the entire prediction horizon simultaneously by taking all control actions with the current state as inputs. Based on this framework, we further propose an adversarial dynamics model that contains two parts. The generator provides a dynamics model for the prediction process, while the discriminator provides constraints that are hard to describe by manually defined loss. This adversarial dynamics model can accelerate training and improve model accuracy in unstructured environments. Experiments conducted in Gazebo simulator and on a real mobile robot demonstrate the efficiency and accuracy of the single-model simultaneous framework with an adversarial dynamics model.