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Source publication
The paper presents neuroevolution approach to a crawler robot motion that autonomously solves sequences of navigation and flipper control tasks to overcome obstacles in 3D simulation domain. When modelling scenarios of robot locomotion, we used our model of a novel Russian crawler robot "Engineer" in ROS/Gazebo. The modelled robot measured obstacle...
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Context 1
... and a flipper control. The track approximation by wheels is used for fast simulation of crawler-type vehicles [10]. Further, it will allow to retrain our neuroevolution algorithm with other, more complicated robot model with tracks. The current model includes a base link, flippers and 2x4 pseudo wheels for motion (three pairs for base part, see Fig. 3). The robot model (Fig. 3) was developed in ROS\Gazebo environment. ROS 2 allows creating algorithm, integrating different parts (packages\nodes) to each other. ROS has also many ready-to-use packages to solve typical mobile robotics tasks, like navigation, SLAM, action planning and others. Gazebo 3 allows simulating robot actions in ...
Context 2
... The track approximation by wheels is used for fast simulation of crawler-type vehicles [10]. Further, it will allow to retrain our neuroevolution algorithm with other, more complicated robot model with tracks. The current model includes a base link, flippers and 2x4 pseudo wheels for motion (three pairs for base part, see Fig. 3). The robot model (Fig. 3) was developed in ROS\Gazebo environment. ROS 2 allows creating algorithm, integrating different parts (packages\nodes) to each other. ROS has also many ready-to-use packages to solve typical mobile robotics tasks, like navigation, SLAM, action planning and others. Gazebo 3 allows simulating robot actions in close to real physics mode ...
Citations
... Regarding HyperNEAT, it also has been applied in several areas of robotics, such as locomotion, cooperative behaviour, and morphology creation. To develop locomotion controllers, a universal flipper controller for autonomous crawler robots was introduced [35]. The HyperNEAT-based model is tested in the ROS/Gazebo simulator. ...
... However, these strategies are limited in generating complex motions and may not be applicable to different types of terrain. In recent years, learningbased approaches have been used to address flipper control and autonomous traversal, such as neuroevolution [7] and reinforcement learning [8], [9]. These studies are conducted solely on a single type of terrain, while our work focuses on a general terrain traversal scheme. ...
Autonomous terrain traversal of articulated tracked robots can reduce operator cognitive load to enhance task efficiency and facilitate extensive deployment. We present a novel hybrid trajectory optimization method aimed at generating efficient, stable, and smooth traversal motions. To achieve this, we develop a planar robot-terrain contact model and divide the robot's motion into hybrid modes of driving and traversing. By using a generalized coordinate description, the configuration space dimension is reduced, which facilitates real-time planning. The hybrid trajectory optimization is transcribed into a nonlinear programming problem and divided into subproblems to be solved in a receding-horizon planning fashion. Mode switching is facilitated by associating optimized motion durations with a predefined traversal sequence. A multi-objective cost function is formulated to further improve the traversal performance. Additionally, map sampling, terrain simplification, and tracking controller modules are integrated into the autonomous terrain traversal system. Our approach is validated in simulation and real-world scenarios with the Searcher robotic platform. Comparative experiments with expert operator control and state-of-the-art methods show advantages in terms of time and energy efficiency, stability, and smoothness of motion.
... Besides, there are data-based methods that can train a mapping from state to action [16], [19], [20], which should be able to learn to use the flippers. But the weakness is the limited coverage of the training terrain, which in turn might cause a crash due to the overfitted parameters. ...
Autonomous run is always a goal in the field of rescue robot and the utilization of flipper will strongly improve the mobility and safety of robot. In this work, we simplify the rescue robot as a skeleton on inflated terrain. Its morphology can be represented by configuration of several parameters. Based on our previous paper, we further configure four flippers individually. The proposed flipper planning is of a mobile movement on 3D terrain with 2.5D maps. The experiment shows that our method can well tackle various terrain and have high efficiency on manipulating the flippers.
... Make effort on the observation, [6] model the incomplete measurement and make control on the robot morphology under RL. Similarly, [16] describe a detailed implemented framework to learn the mobility from simulation experiments using deep neural network. [17] learn the effect of action and make plan on reconfiguration of tracks to tackle various obstacles. ...
For rescue robots, flipper endows the robot with additional ability to pass through various terrain. Autonomous motion becomes more important. In recent work autonomy is done by either planning with several special states or based on collected data. We are considering if it is possible to find a way to build continues states without collecting old trail data. In this paper, we first model the possible states as a global planning path with parameter configuration of the scene. Then, we follows the path to achieve the autonomous run. We plot the morphology of each path points to show the correctness of the path and implement a simple path following on real robot to demonstrate the performance of our algorithm.
