Autonomous system (mathematics)

About: Autonomous system (mathematics) is a research topic. Over the lifetime, 1648 publications have been published within this topic receiving 38373 citations.

Analysis of a new three-dimensional system with multiple chaotic attractors

Abstract: In this paper, a new three-dimensional autonomous system with complex dynamical behaviors is reported. This new system has three quadratic nonlinear terms and one constant term. One remarkable feature of the system is that it can generate multiple chaotic and multiple periodic attractors in a wide range of system parameters. The presence of coexisting chaotic and periodic attractors in the system is investigated. Moreover, it is easily found that the new system also can generate four-scroll chaotic attractor. Some basic dynamical behaviors of the system are investigated through theoretical analysis and numerical simulation.

Generating two simultaneously chaotic attractors with a switching piecewise-linear controller

Abstract: It has been demonstrated that a piecewise-linear system can generate chaos under suitable conditions. This paper proposes a novel method for simultaneously creating two symmetrical chaotic attractor––an upper-attractor and a lower-attractor––in a 3D linear autonomous system. Basically dynamical behaviors of this new chaotic system are further investigated. Especially, the chaos formation mechanism is explored by analyzing the structure of fixed points and the system trajectories.

Plan-N-Scan: A Robotic System for Collision-Free Autonomous Exploration and Workspace Mapping

Abstract: This paper presents a sensor-based robotic system, called Plan-N-Scan, for collision-free, autonomous exploration and workspace mapping, using a wrist-mounted laser range camera. This system involves gaze planning with collision-free sensor positioning in a static environment, resulting in a 3-D map suitable for real-time collision detection. This work was initially motivated by the great demand for autonomous exploration systems in the remediation of buried but leaking tanks containing hazardous nuclear waste. Plan-N-Scan uses two types of representations: a spherical model of the manipulator and a weighted voxel map of the workspace. In addition to providing efficient collision detection, the voxel map allows the incorporation of different types of spatial occupancy information. The mapping of unknown sections of the workspace is achieved by either target or volume scanning. Target scanning incorporates a powerful A*-based search, along with a viewing position selection strategy, to incrementally acquire scans of the scene and use them to capture targets, even if they are not immediately viewable by the range camera. Volume scanning is implemented as an iterative process which automatically selects scan targets, then employs the target scanning process to scan these targets and explore the selected workspace volume. The performance and reliability of the system was demonstrated through simulation and a number of experiments involving a real robot system. The ability of the Plan-N-Scan system to incrementally acquire range information and successfully scan both targets and workspace volumes was demonstrated.