Autonomous Robots最新文献

Localization measurements for an autonomous underwater vehicle (AUV) are often difficult to obtain. In many cases, localization measurements are only available sporadically after the AUV comes to the sea surface. Since the motion of AUVs is often affected by unknown underwater flow fields, the sporadic localization measurements carry information of the underwater flow field. Motion tomography (MT) algorithms have been developed to compute a underwater flow map based on the sporadic localization measurements. This paper extends MT by introducing Laplacian regularization in to the problem formulation and the MT algorithm. Laplacian regularization enforces smoothness in the spatial distribution of the underwater flow field. The resulted Laplacian regularized motion tomography (RMT) algorithm converges to achieve a finite error bounded. The performance of the RMT and other variants of MT are compared through the method of data resolution analysis. The improved performance of RMT is confirmed by experimental data collected from underwater glider ocean sensing experiments.

Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated primarily in terms of human safety. Consequently, human-robot interaction (HRI) in scenarios with no intervention of technical personnel is under-explored. However, in the future, robots will be deployed in unstructured and unsupervised environments where they will be expected to work unsupervised on tasks which require direct interaction with humans and may not necessarily be collaborative. Developing such robots requires comparing the effectiveness and efficiency of similar design approaches and techniques. Yet, issues regarding the reproducibility of results, comparing different approaches between research groups, and creating challenging milestones to measure performance and development over time make this difficult. Here we discuss the international robotics competition called RoboCup as a benchmark for the progress and open challenges in AI and robotics development. The long term goal of RoboCup is developing a robot soccer team that can win against the world’s best human soccer team by 2050. We selected RoboCup because it requires robots to be able to play with and against humans in unstructured environments, such as uneven fields and natural lighting conditions, and it challenges the known accepted dynamics in HRI. Considering the current state of robotics technology, RoboCup’s goal opens up several open research questions to be addressed by roboticists. In this paper, we (a) summarise the current challenges in robotics by using RoboCup development as an evaluation metric, (b) discuss the state-of-the-art approaches to these challenges and how they currently apply to RoboCup, and (c) present a path for future development in the given areas to meet RoboCup’s goal of having robots play soccer against and with humans by 2050.

This paper improves the performance of RRT(^*)-like sampling-based path planners by combining admissible informed sampling and local sampling (i.e., sampling the neighborhood of the current solution). An adaptive strategy regulates the trade-off between exploration (admissible informed sampling) and exploitation (local sampling) based on online rewards from previous samples. The paper demonstrates that the algorithm is asymptotically optimal and has a better convergence rate than state-of-the-art path planners (e.g., Informed-RRT(^*)) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available.

Humanoid robots have strong adaptability to complex environments and possess human-like flexibility, enabling them to perform precise farming and harvesting tasks in varying depths of terrains. They serve as essential tools for agricultural intelligence. In this article, a novel method was proposed to improve the robustness of autonomous navigation for humanoid robots, which intercommunicates the data fusion of the footprint planning and control levels. In particular, a deep reinforcement learning model - Proximal Policy Optimization (PPO) that has been fine-tuned is introduced into this layer, before which heuristic trajectory was generated based on imitation learning. In the RL period, the KL divergence between the agent’s policy and imitative expert policy as a value penalty is added to the advantage function. As a proof of concept, our navigation policy is trained in a robotic simulator and then successfully applied to the physical robot GTX for indoor multi-mode navigation. The experimental results conclude that incorporating imitation learning imparts anthropomorphic attributes to robots and facilitates the generation of seamless footstep patterns. There is a significant improvement in ZMP trajectory in y-direction from the center by 21.56% is noticed. Additionally, this method improves dynamic locomotion stability, the body attitude angle falling between less than ± 5.5(^circ ) compared to ± 48.4(^circ ) with traditional algorithm. In general, navigation error is below 5 cm, which we verified in the experiments. It is thought that the outcome of the proposed framework presented in this article can provide a reference for researchers studying autonomous navigation applications of humanoid robots on uneven ground.

Terrain traversability estimation is a fundamental task for supporting robot navigation on uneven surfaces. Recent learning-based approaches for predicting traversability from RGB images have shown promising results, but require manual annotation of a large number of images for training. To address this limitation, we present a method for traversability estimation on unlabeled videos that combines dataset synthesis, self-supervision and unsupervised domain adaptation. We pose the traversability estimation as a vector regression task over vertical bands of the observed frame. The model is pre-trained through self-supervision to reduce the distribution shift between synthetic and real data and encourage shared feature learning. Then, supervised training on synthetic videos is carried out, while employing an unsupervised domain adaptation loss to improve its generalization capabilities on real scenes. Experimental results show that our approach is on par with standard supervised training, and effectively supports robot navigation without the need of manual annotations. Training code and synthetic dataset will be publicly released at: https://github.com/perceivelab/traversability-synth.

Spatial search, and environmental monitoring are key technologies in robotics. These problems can be reformulated as maximal coverage problems with routing constraints, which are NP-hard problems. The generalized cost-benefit algorithm (GCB) can solve these problems with theoretical guarantees. To achieve better performance, evolutionary algorithms (EA) boost its performance via more samples. However, it is hard to know the terminal conditions of EA to outperform GCB. To solve these problems with theoretical guarantees and terminal conditions, in this research, the cross-entropy based Monte Carlo Tree Search algorithm (CE-MCTS) is proposed. It consists of three parts: the EA for sampling the branches, the upper confidence bound policy for selections, and the estimation of distribution algorithm for simulations. The experiments demonstrate that the CE-MCTS outperforms benchmark approaches (e.g., GCB, EAMC) in spatial search problems.

It is often unnoticed that the predominant way to use collocation methods is fundamentally flawed when applied to optimal control in robotics. Such methods assume that the system dynamics is given by a first order ODE, whereas robots are often governed by a second or higher order ODE involving configuration variables and their time derivatives. To apply a collocation method, therefore, the usual practice is to resort to the well known procedure of casting an Mth order ODE into M first order ones. This manipulation, which in the continuous domain is perfectly valid, leads to inconsistencies when the problem is discretized. Since the configuration variables and their time derivatives are approximated with polynomials of the same degree, their differential dependencies cannot be fulfilled, and the actual dynamics is not satisfied, not even at the collocation points. This paper draws attention to this problem, and develops improved versions of the trapezoidal and Hermite–Simpson collocation methods that do not present these inconsistencies. In many cases, the new methods reduce the dynamics transcription error in one order of magnitude, or even more, without noticeably increasing the cost of computing the solutions.

Mobile service robots are a promising technology for supporting workflows throughout the hospital. Combined with an understanding of the environment and the current situation, such systems have the potential to become invaluable tools for overcoming personal shortages and streamlining healthcare workflows. However, few robotic systems have actually been translated to practical application so far, which is due to many challenges centered around the strict and unique requirements imposed by the different hospital environments, which have not yet been collected and analyzed in a structured manner. To address this need, we now present a comprehensive classification of different dimensions of risk to be considered when designing mobile service robots for the hospital. Our classification consists of six risk categories – environmental complexity, hygienic requirements, interaction with persons and objects, workflow flexibility and autonomy – for each of which a scale with distinct risk levels is provided. This concept, for the first time allows for a precise classification of mobile service robots for the hospital, which can prove useful for certification and admission procedures as well as for defining architectural and safety requirements throughout the design process of such robots.