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An Integrating Comprehensive Trajectory Prediction with Risk Potential Field Method for Autonomous Driving
Apr 01, 2024
Due to the uncertainty of traffic participants' intentions, generating safe but not overly cautious behavior in interactive driving scenarios remains a formidable challenge for autonomous driving. In this paper, we address this issue by combining a deep learning-based trajectory prediction model with risk potential field-based motion planning. In order to comprehensively predict the possible future trajectories of other vehicles, we propose a target-region based trajectory prediction model(TRTP) which considers every region a vehicle may arrive in the future. After that, we construct a risk potential field at each future time step based on the prediction results of TRTP, and integrate risk value to the objective function of Model Predictive Contouring Control(MPCC). This enables the uncertainty of other vehicles to be taken into account during the planning process. Balancing between risk and progress along the reference path can achieve both driving safety and efficiency at the same time. We also demonstrate the security and effectiveness performance of our method in the CARLA simulator.
Efficient Continuous-Time Ego-Motion Estimation for Asynchronous Event-based Data Associations
Feb 26, 2024Event cameras are bio-inspired vision sensors that asynchronously measure per-pixel brightness changes. The high temporal resolution and asynchronicity of event cameras offer great potential for estimating the robot motion state. Recent works have adopted the continuous-time ego-motion estimation methods to exploit the inherent nature of event cameras. However, most of the adopted methods have poor real-time performance. To alleviate it, a lightweight Gaussian Process (GP)-based estimation framework is proposed to efficiently estimate motion trajectory from asynchronous event-driven data associations. Concretely, an asynchronous front-end pipeline is designed to adapt event-driven feature trackers and generate feature trajectories from event streams; a parallel dynamic sliding-window back-end is presented within the framework of sparse GP regression on SE(3). Notably, a specially designed state marginalization strategy is employed to ensure the consistency and sparsity of this GP regression. Experiments conducted on synthetic and real-world datasets demonstrate that the proposed method achieves competitive precision and superior robustness compared to the state-of-the-art. Furthermore, the evaluations on three 60 s trajectories show that the proposal outperforms the ISAM2-based method in terms of computational efficiency by 2.64, 4.22, and 11.70 times, respectively.
Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition
Nov 19, 2023Robotic manipulation holds the potential to replace humans in the execution of tedious or dangerous tasks. However, control-based approaches are not suitable due to the difficulty of formally describing open-world manipulation in reality, and the inefficiency of existing learning methods. Thus, applying manipulation in a wide range of scenarios presents significant challenges. In this study, we propose a novel method for skill learning in robotic manipulation called Tactile Active Inference Reinforcement Learning (Tactile-AIRL), aimed at achieving efficient training. To enhance the performance of reinforcement learning (RL), we introduce active inference, which integrates model-based techniques and intrinsic curiosity into the RL process. This integration improves the algorithm's training efficiency and adaptability to sparse rewards. Additionally, we utilize a vision-based tactile sensor to provide detailed perception for manipulation tasks. Finally, we employ a model-based approach to imagine and plan appropriate actions through free energy minimization. Simulation results demonstrate that our method achieves significantly high training efficiency in non-prehensile objects pushing tasks. It enables agents to excel in both dense and sparse reward tasks with just a few interaction episodes, surpassing the SAC baseline. Furthermore, we conduct physical experiments on a gripper screwing task using our method, which showcases the algorithm's rapid learning capability and its potential for practical applications.