Autonomous Robots

Journal: Autonomous Robots

Abbreviation

Auton. Robots

Publisher

Springer

ISSN

0929-5593
1573-7527

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Publications 1 - 10 of 38

Chasing millimeters: design, navigation and state estimation for precise in-flight marking on ceilings
Lanegger, Christian; Pantic, Michael; Bähnemann, Rik; et al. (2023)
Autonomous Robots
Precise markings for drilling and assembly are crucial, laborious construction tasks. Aerial robots with suitable end-effectors are capable of markings at the millimeter scale. However, so far, they have only been demonstrated under laboratory conditions where rigid state estimation and navigation assumptions do not impede robustness and accuracy. This paper presents a complete aerial layouting system capable of precise markings on-site under realistic conditions. We use a compliant actuated end-effector on an omnidirectional flying base. Combining a two-stage factor-graph state estimator with a Riemannian Motion Policy-based navigation stack, we avoid the need for a globally consistent state estimate and increase robustness. The policy-based navigation is structured into individual behaviors in different state spaces. Through a comprehensive study, we show that the system creates highly precise markings at a relative precision of 1.5 mm and a global accuracy of 5-6 mm and discuss the results in the context of future construction robotics.
Collision avoidance for aerial vehicles in multi-agent scenarios
Alonso-Mora, Javier; Naegeli, Tobias; Siegwart, Roland; et al. (2015)
Autonomous Robots
This article describes an investigation of local motion planning, or collision avoidance, for a set of decision-making agents navigating in 3D space. The method is applicable to agents which are heterogeneous in size, dynamics and aggressiveness. It builds on the concept of velocity obstacles (VO), which characterizes the set of trajectories that lead to a collision between interacting agents. Motion continuity constraints are satisfied by using a trajectory tracking controller and constraining the set of available local trajectories in an optimization. Collision-free motion is obtained by selecting a feasible trajectory from the VO’s complement, where reciprocity can also be encoded. Three algorithms for local motion planning are presented—(1) a centralized convex optimization in which a joint quadratic cost function is minimized subject to linear and quadratic constraints, (2) a distributed convex optimization derived from (1), and (3) a centralized non-convex optimization with binary variables in which the global optimum can be found, albeit at higher computational cost. A complete system integration is described and results are presented in experiments with up to four physical quadrotors flying in close proximity, and in experiments with two quadrotors avoiding a human.
Incremental topological segmentation for semi-structured environments using discretized GVG
Liu, Ming; Colas, Francis; Oth, Luc; et al. (2015)
Autonomous Robots
A multiagent framework for learning dynamic traffic management strategies
Chung, Jen Jen; Rebhuhn, Carrie; Yates, Connor; et al. (2019)
Autonomous Robots
There is strong commercial interest in the use of large scale automated transport robots in industrial settings (e.g. warehouse robots) and we are beginning to see new applications extending these systems into our urban environments in the form of autonomous cars and package delivery drones. This new technology comes with new risks—increasing traffic congestion and concerns over safety; it also comes with new opportunities—massively distributed information and communication systems. In this paper, we present a method that leverages the distributed nature of the autonomous traffic to provide improved traffic throughput while maintaining strict capacity constraints across the network. Our proposed multiagent-based dynamic traffic management strategy borrows concepts from both air traffic control and highway metering lights. We introduce controller agents whose actions are to adjust the robots’ perceived “costs” of traveling across different parts of the traffic network. This approach allows each robot the flexibility of using its own (potentially proprietary) navigation algorithm, while still being bound by the “rules of the road.” The control policies of the agents are defined as neural networks whose weights are learned via cooperative coevolution across the entire traffic management team. Results in a real world road network and a simulated warehouse domain demonstrate that our multiagent traffic management system provides substantial improvements to overall traffic throughput in terms of number of successful trips in a fixed amount of time, as well as faster average traversal times.
Autonomous construction using scarce resources in unknown environments
Magnenat, Stéphane; Philippsen, Roland; Mondada, Francesco (2012)
Autonomous Robots
The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a “real but contrived” experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real world.
3D multi-robot patrolling with a two-level coordination strategy
Freda, Luigi; Gianni, Mario; Pirri, Fiora; et al. (2019)
Autonomous Robots
Optimal surveillance coverage for teams of micro aerial vehicles in GPS-denied environments using onboard vision
Doitsidis, Lefteris; Weiss, Stephan; Renzaglia, Alessandro; et al. (2012)
Autonomous Robots
Opportunistic sampling-based active visual SLAM for underwater inspection
Chaves, Stephen M.; Kim, Ayoung; Galceran, Enric; et al. (2016)
Autonomous Robots
Receding horizon path planning for 3D exploration and surface inspection
Bircher, Andreas; Kamel, Mina; Alexis, Kostas; et al. (2018)
Autonomous Robots
Within this paper a new path planning algorithm for autonomous robotic exploration and inspection is presented. The proposed method plans online in a receding horizon fashion by sampling possible future configurations in a geometric random tree. The choice of the objective function enables the planning for either the exploration of unknown volume or inspection of a given surface manifold in both known and unknown volume. Application to rotorcraft Micro Aerial Vehicles is presented, although planning for other types of robotic platforms is possible, even in the absence of a boundary value solver and subject to nonholonomic constraints. Furthermore, the method allows the integration of a wide variety of sensor models. The presented analysis of computational complexity and thorough simulations-based evaluation indicate good scaling properties with respect to the scenario complexity. Feasibility and practical applicability are demonstrated in real-life experimental test cases with full on-board computation.
Towards Autonomous Indoor Micro VTOL
Bouabdallah, Samir; Murrieri, Pierpaolo; Siegwart, Roland (2005)
Autonomous Robots

Publications 1 - 10 of 38

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