This paper considers the problem of motion planning for a hybrid robotic system with complex and nonlinear dynamics in a partially unknown environment given a temporal logic specification. We employ a multi-layered synergistic framework that can deal with general robot dynamics and combine it with an iterative planning strategy. Our work allows us to deal with the unknown environmental restrictions only when they are discovered and without the need to repeat the computation that is related to the temporal logic specification. In addition, we define a metric for satisfaction of a specification. We use this metric to plan a trajectory that satisfies the specification as closely as possible in cases in which the discovered constraint in the environment renders the specification unsatisfiable. We demonstrate the efficacy of our framework on a simulation of a hybrid second-order car-like robot moving in an office environment with unknown obstacles. The results show that our framework is successful in generating a trajectory whose satisfaction measure of the specification is optimal. They also show that, when new obstacles are discovered, the reinitialization of our framework is computationally inexpensive.
This work introduces a motion-planning framework for a hybrid system with general continuous dynamics to satisfy a temporal logic specification consisting of co-safety and safety components in a partially unknown environment. The framework employs a multi-layered synergistic planner to generate trajectories that satisfy the specification and adopts an iterative replanning strategy to deal with unknown obstacles. When the discovery of an obstacle renders the specification unsatisfiable, a division between the constraints in the specification is considered. The co-safety component of the specification is treated as a soft constraint, whose partial satisfaction is allowed, while the safety component is viewed as a hard constraint, whose violation is forbidden. To partially satisfy the co-safety component, inspirations
This paper presents an extension to SyCLoP, a multilayered motion planning framework that has been shown to successfully solve high-dimensional problems with differential constraints. SyCLoP combines traditional sampling-based planning with a high-level decomposition of the workspace through which it attempts to guide a low-level tree of motions. We investigate a generalization of SyCLoP in which the highlevel decomposition is defined over a given low-dimensional projected subspace of the state space. We begin with a manually-chosen projection to demonstrate that projections other than the workspace can potentially work well. We then evaluate SyCLoP's performance with random projections and projections determined from linear dimensionality reduction over elements of the state space, for which the results are mixed. As we will see, finding a useful projection is a difficult problem, and we conclude this paper by discussing the merits and drawbacks of various types of projections.
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