Autonomous Robots最新文献

In this paper, we present a novel method for autonomous robotic exploration using a car-like robot. The proposed method uses the frontiers in the map to build a tree representing the structure of the environment to aid the goal-selection method. An augmentation of the method is also proposed which is able to manage the loops present in the environment. In this case, the environment is represented with a graph structure. We compared the two proposed methods with seven state-of-the-art exploration methods in three simulated environments. The experiments show, that the proposed methods outperform the existing methods both in the time taken until full exploration and the distance traveled during the exploration, while offering a robust solution for autonomous robotic exploration without the need to tune several parameters to the unknown environment. The proposed exploration method was also tested using a real-life robot in an office scenario.

This paper addresses the problem of both actively searching and tracking multiple unknown dynamic objects in a known environment with multiple cooperative autonomous agents with partial observability. The tracking of a target ends when the uncertainty is below a specified threshold. Current methods typically assume homogeneous agents without access to external information and utilize short-horizon target predictive models. Such assumptions limit real-world applications. We propose a fully integrated pipeline where the main novel contributions are: (1) a time-varying weighted belief representation capable of handling knowledge that changes over time, which includes external reports of varying levels of trustworthiness in addition to the agents involved; (2) the integration of a Long Short Term Memory-based trajectory prediction within the optimization framework for long-horizon decision-making, which accounts for trajectory prediction in time-configuration space, thus increasing responsiveness; and (3) a comprehensive system that accounts for multiple agents and enables information-driven optimization during both the search and track tasks. When communication is available, our proposed strategy consolidates exploration results collected asynchronously by agents and external sources into a headquarters, who can allocate each agent to maximize the overall team’s utility, effectively using all available information. We tested our approach extensively in Monte Carlo simulations against baselines, representative of classes of approaches from the literature, and in robustness and ablation studies. In addition, we performed experiments in a 3D physics based engine robot simulator to test the applicability in the real world, as well as with real-world trajectories obtained from an oceanography computational fluid dynamics simulator. Results show the effectiveness of our proposed method, which achieves mission completion times that are 1.3 to 3.2 times faster in finding all targets, in most scenarios, including challenging ones, where the number of targets is 5 times greater than that of the agents.

This paper proposes a novel system to conduct visual servoing of a mobile robot using multiple uncalibrated, wall-mounted cameras. Specifically, we utilise a constellation of such sensors to cover a wide area by allowing robot control to be passed between cameras in regions where their fields of view overlap. This method, in conjunction with the fact that all computing is also executed offboard, allows for simpler and cheaper robots to be deployed in controlled and finite spaces. Our method simplifies the natural installation cycle of a newly deployed camera network, eliminating the need for explicit camera positioning or orientation, both globally (relative to a building plan) and locally (among viewpoints). Our system memorises pixel-wise topological connections between viewpoints by leveraging natural human exploration of the environment. We detect graph edges through simultaneous detections of the same person across different cameras, allowing us to automatically construct an evolving graph that represents overlapping fields of view within the camera network. In combination with a hybrid-A*-based planner, our approach allows efficient planning and control of robots across a wide area by traversing cameras between areas of overlap. We validate our approach through autonomous traversals in a productive office environment, using a network of six cameras, and compare our performance against both human teleoperation and a traditional Simultaneous Localisation and Mapping (SLAM) approach.

Multi-robot systems are broadly used in applications such as search and rescue, environmental monitoring, and mapping of unknown environments. Effective coordination among these robots often relies on distributed information and local decision-making. However, maintaining constant communication links between robots can be challenging due to environmental and task constraints. Robots can move around to seek temporal communication links that over time jointly establish the intermittent connectivity of the network. This paper aims to incorporate temporal communication constraints into the path planning for multi-robot teams with stochastic motion and handling complex tasks specified in a temporal order. We use formal methods to model the temporal specification of tasks. Task assignments and high-level communication requirements are provided to individual robots on a multi-robot team as independent temporal logic expressions. Robots update their plans for future communication events according to their local decision-making algorithms and jointly synthesize a bottom-up policy to meet the communication requirements. We provide a strategy to maintain intermittent connectivity while satisfying a risk constraint. In addition, we systematically analyze the impact of different rendezvous selection strategies, comparing cost functions that minimize the total traveled distance, balance distances among robots, or incorporate risk awareness. Our simulation results suggest that the proposed method effectively accommodates diverse operational preferences, enhancing flexibility, robustness, and overall mission performance.

Building a distributed spatial awareness within a swarm of locally sensing and communicating robots enables new swarm algorithms. We use local observations by robots of each other and Gaussian belief propagation message passing combined with continuous swarm movement to build a global and distributed swarm-centric frame of reference. With low bandwidth and computation requirements, this shared reference frame allows new swarm algorithms. We characterise the system in simulation and demonstrate two example algorithms, then demonstrate reliable performance on real robots with imperfect sensing.

Aerial Robots have the potential to play a crucial role in assisting humans in complex and dangerous tasks with the goal to decrease users’ cognitive and physical workload. In addition, many applications will require aerial robots to be ubiquitous and share the same environment with human operators. Therefore, this calls for novel solutions to enable seamless, transparent, and efficient human-drone collaboration and co-working in the same workspace. In this paper, we present a novel tele-immersive approach that promotes cognitive and physical collaboration between humans and robots through Mixed Reality (MR). We develop a bi-directional spatial awareness module and a new virtual-physical interaction approach integrated on a head-mounted display with MR. Furthermore, we design two alternative methods for spatial and physical interaction. Both solutions use a 2D monitor for spatial representation, with one method involving a mouse and keyboard, and the other using a haptic interface with the new VAC solution for physical interaction. This setup allows us to study how to analyze how different physical embodiments might compensate for reduced spatial representation during interaction tasks. Finally, to validate our approach and our comparative study, we propose a comprehensive user case study where 12 subjects with different expertise and background are tasked to complete a target reaching task in an indoor cluttered environment. We consider as part of the evaluation both subjective metrics, such as the System Usability Scale and the NASA Task Load Index, as well as objective measures, including completion time, distance traveled to reach the goal, and smoothness of movements. The results demonstrate enhanced user interaction and control capabilities during the task when using our novel novel tele-immersive approach with MR compared to the two alternative solutions. Additionally, the experiments show the opportunity to employ the proposed system as a new innovative collaboration approach between a non-expert human and an aerial robot for exploration and inspection tasks in unknown environments. Video: https://youtu.be/q8Dq-cNxcig

In collective motion, perceptually-limited individuals move in an ordered manner, without centralized control. The perception of each individual is highly localized, as is its ability to interact with others. While natural collective motion is robust, most artificial swarms are brittle. This particularly occurs when vision is used as the sensing modality, due to ambiguities and information-loss inherent in visual perception. This paper presents mechanisms for robust collective motion inspired by studies of locusts. First, we develop a robust distance estimation method that combines visually perceived horizontal and vertical sizes of neighbors. Second, we introduce intermittent locomotion as a mechanism that allows robots to reliably detect peers that fail to keep up, and disrupt the motion of the swarm. We show how such faulty robots can be avoided in a manner that is robust to errors in classifying them as faulty. Through extensive physics-based simulation experiments, we show dramatic improvements to swarm resilience when using these techniques. We show these are relevant to both distance-based Avoid–Attract models, as well as to models relying on Alignment, in a wide range of experiment settings.

New settlements in remote environments require terrain modification, a task well suited for autonomous multi-robot systems. Simple, robust earthmover robots offer an inexpensive and scalable alternative to sophisticated construction robots. We present a mathematical model for such robots modifying continuous granular structures in 2D and develop both centralized and decentralized planning algorithms to achieve user-defined construction goals. These algorithms decompose long-horizon tasks into subtasks solvable using optimal transport theory and Wasserstein geodesics. Simulations across 100 randomly generated tasks show that a centralized controller with global information achieves on average 85% and 92% construction progress on untraversable and traversable terrains respectively, even with action noise. Multiple robots reduce overall travel distance by 70%, important because motion over the structure also disturbs it. The distributed algorithm—without global information—matches centralized performance on traversable terrain, reaching 93% progress. Increasing robot numbers accelerates convergence, lowers moved material, and raises convergence rates, though congestion can increase total travel distance. These results indicate that simple earthmover robots hold promise for construction tasks ranging from extraterrestrial habitat preparation to coastal protective berms.

This paper considers the problem of autonomous mobile robot navigation in unknown environments with moving obstacles. We propose a new method to achieve environment-aware safe tracking (EAST) of robot motion plans that integrates an obstacle clearance cost for path planning, a convex reachable set for robot motion prediction, and safety constraints for dynamic obstacle avoidance. EAST adapts the motion of the robot according to the locally sensed environment geometry and dynamics, leading to fast motion in wide open areas and cautious behavior in narrow passages or near moving obstacles. Our control design uses a reference governor, a virtual dynamical system that guides the robot’s motion and decouples the path tracking and safety objectives. While reference governor methods have been used for safe tracking control in static environments, our key contribution is an extension to dynamic environments using convex optimization with control barrier function (CBF) constraints. Thus, our work establishes a connection between reference governor techniques and CBF techniques for safe control in dynamic environments. We validate our approach in simulated and real-world environments, featuring complex obstacle configurations and natural dynamic obstacle motion.