Multi-goal motion planning under motion and sensor uncertainty is the problem of finding a reliable policy for visiting a set of goal points. In this paper, the problem is formulated as a formidable traveling salesman problem in the belief space. To solve this intractable problem, we propose an algorithm to construct a TSP-FIRM graph which is based on the feedback-based information roadmap (FIRM) algorithm. Also, two algorithms are proposed for the online planning of the obtained policy in the offline mode and overcoming changes in the map of the environment. Finally, we apply the algorithms on a physical nonholonomic mobile robot in the presence of challenging situations like the discrepancy between the real and computation model, map updating and kidnapping.
Within this paper, a new fast algorithm that provides efficient solutions to the problem of inspection path planning for complex 3D structures is presented. The algorithm assumes a triangular mesh representation of the structure and employs an alternating two-step optimization paradigm to find good viewpoints that together provide full coverage and a connecting path that has low cost. In every iteration, the viewpoints are chosen such that the connection cost is reduced and, subsequently, the tour is optimized. Vehicle and sensor limitations are respected within both steps. Sample implementations are provided for rotorcraft and fixed-wing unmanned aerial systems. The resulting algorithm characteristics are evaluated using simulation studies as well as multiple real-world experimental test-cases with both vehicle types.
Considering the time-delayed feedback and environmental perturbations in spin-crossover system, we construct a stochastic delayed differential equation to study the state transitions from the low spin (LS) state to the high spin (HS) state in spin-crossover solids. It is shown that the delayed feedback and noise can induce optical bistability and state transitions. The mean first-passage time (MFPT) of the transition from the LS state to the HS state as the function of the noise intensity exhibits a maximum, and the noise-enhanced stability is observed. However the MFPT decreases with increase of the delayed feedback intensity, thus the delayed feedback accelerates the conversion from the LS state to the HS state.