No abstract

Abstract:Designing an urban reconnaissance robot is highly challenging work given the nature of the terrain in which these robots are required to operate. In this work, we attempt to extend the locomotion capabilities of these robots beyond what is currently feasible. The design and unique features of our bio-inspired reconfigurable robot, called Scorpio, with rolling, crawling, and wall-climbing locomotion abilities are presented in this paper. The design of the Scorpio platform is inspired by Cebrennus rechenbergi, a rare spider species that has rolling, crawling and wall-climbing locomotion attributes. This work also presents the kinematic and dynamic model of Scorpio. The mechanical design and system architecture are introduced in detail, followed by a detailed description on the locomotion modes. The conducted experiments validated the proposed approach and the ability of the Scorpio platform to synthesise crawling, rolling and wall-climbing behaviours. Future work is envisioned for using these robots as active, unattended, mobile ground sensors in urban reconnaissance missions. The accompanying video demonstrates the shape shifting locomotion capabilities of the Scorpio robot.

Legged robots are able to move across irregular terrains and those based on 1-degree-of-freedom planar linkages can be energy efficient, but are often constrained by a limited range of gaits which can limit their locomotion capabilities considerably. This article reports the design of a novel reconfigurable Theo Jansen linkage that produces a wide variety of gait cycles, opening new possibilities for innovative applications. The suggested mechanism switches from a pin-jointed Grübler kinematic chain to a 5-degree-of-freedom mechanism with slider joints during the reconfiguration process. It is shown that such reconfigurable linkage significantly extend the capabilities of the original design, while maintaining its mechanical simplicity during normal operation, to not only produce different useful gait patterns but also to realize behaviors beyond locomotion. Experiments with an implemented prototype are presented, and their results validate the proposed approach.

Abstract:As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM)-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU) sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC) and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

Swing-up of a rotating type pendulum from the pendant to the inverted state is known to be one of most difficult control problems, since the system is nonlinear, underactuated, and has uncontrollable states. This paper studies a time optimal swing-up control of the pendulum using bounded input. Time optimal control of a nonlinear system can be formulated by Pontryagin’s Maximum Principle, which is, however, hard to compute practically. In this paper, a new computational approach is presented to attain a numerical solution of the time optimal swing-up problem. Time optimal control problem is described as minimization of the achievable time to attain the terminal state under the bounded input amplitude, although algorithms to solve this problem are known to be complicated. Therefore, in this paper, it is shown how the optimal time swing-up control is formulated as an auxiliary problem in that the minimal input amplitude is searched so that the terminal state satisfies a specification at a given time. Through the proposed approach, time optimal control can be solved by nonlinear optimization. Its approach is evaluated by numerical simulations of a simplified pendulum model, is checked satisfying the necessary condition of Maximum Principle, and is experimentally verified using the rotating type pendulum.

No abstract

Coverage path planning technique is an essential ingredient in every floor cleaning robotic systems. Even though numerous approaches demonstrate the benefits of conventional coverage motion planning techniques, they are mostly limited to fixed morphological platforms. In this article, we put forward a novel motion planning technique for a Tetris-inspired reconfigurable floor cleaning robot named “hTetro” that can reconfigure its morphology to any of the seven one-sided Tetris pieces. The proposed motion planning technique adapts polyomino tiling theory to tile a defined space, generates reference coordinates, and produces a navigation path to traverse on the generated tile-set with an objective of maximizing the area coverage. We have summarized all these aspects and concluded with experiments in a simulated environment that benchmarks the proposed technique with conventional approaches. The results show that the proposed motion planning technique achieves significantly higher performance in terms of area recovered than the traditional methods.

No abstract