Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Motion Planning II
Modelling Search with a Binary Sensor Utilizing Self-Conjugacy of the Exponential FamilyIn this paper, we consider the problem of an autonomous robot searching for a target object whose position is characterized by a prior probability distribution over the workspace (the object prior). We consider the case of a continuous search domain, and a robot equipped with a single binary sensor whose ability to recognize the target object varies probabilistically as a function of the distance from the robot to the target (the sensor model). We show that when the object prior and sensor model are taken from the exponential family of distributions, the searcher's posterior probability map for the object location belongs to a finitely parameterizable class of functions, admitting an exact representation of the searcher's evolving belief. Unfortunately, the cost of the representation grows exponentially with the number of stages in the search. For this reason, we develop an approximation scheme that exploits regularized particle filtering methods. We present simulation studies for several scenarios to demonstrate the effectiveness of our approach using a simple, greedy search strategy.
On the Probabilistic Completeness of the Sampling-Based Feedback Motion Planners in Belief SpaceThis paper extends the concept of â€œprobabilistic completenessâ€ defined for the motion planners in the state space (or configuration space) to the concept of â€œprobabilistic completeness under uncertaintyâ€ for the motion planners in the belief space. Accordingly, an approach is proposed to verify the probabilistic completeness of the sampling-based planners in the belief space. Finally, through the proposed approach, it is shown that under mild conditions the samplingbased method constructed based on the abstract framework of FIRM (Feedback-based Information-state Roadmap Method) are probabilistically complete under uncertainty.
Egress: An Online Path Planning Algorithm for Boundary ExplorationWe consider the problem of navigating a mobile robot that is located at any arbitrary point within a bounded environment, to a point on the environment's outer boundary and then, using the robot to explore the perimeter of the boundary. The environment can have obstacles in it and the location and size of these obstacles are not provided a priori to the robot. We present an online path planning algorithm to solve this problem that requires very simple behaviors and computation on the robot. We analytically prove that by using our algorithm, the robot is guaranteed to reach and explore the outer boundary of the environment within a finite time.
Shortest Paths for Visibility-Based Pursuit-EvasionWe present an algorithm that computes a minimal-cost pursuer trajectory for a single pursuer to solve the visibility-based pursuit-evasion problem in a simply-connected two dimensional environment. This algorithm improves upon the known algorithm of Guibas, Latombe, LaValle, Lin, and Motwani, which is complete but not optimal. Our algorithm uses a Tour of Segments (ToS) subroutine to construct a pursuer path that minimizes the distance traveled by the pursuer while guaranteeing that all evaders in the environment will be captured. We have implemented our algorithm in simulation and provide results.
Hierarchical Motion Planning with Kinodynamic Feasibility Guarantees: Local Trajectory Planning Via Model Predictive ControlMotion planners for autonomous vehicles often involve a two-level hierarchical structure consisting of a high-level, discrete planner and a low-level trajectory generation scheme. To ensure compatibility between these two levels of planning, we previously introduced a motion planning framework based on multiple-edge transition costs in the graph used by the discrete planner. This framework is enabled by a special local trajectory generation problem, which we address in this paper. In particular, we discuss a trajectory planner based on model predictive control for complex vehicle dynamical models. We demonstrate the efficacy of our overall motion planning approach via examples involving non-trivial vehicle models and complex environments, and we offer comparisons of our motion planner with state-of-the-art randomized sampling-based motion planners.
Using State Dominance for Path Planning in Dynamic Environments with Moving ObstaclesPath planning in dynamic environments with moving obstacles is computationally complex since it requires modeling time as an additional dimension. While in other domains there are state dominance relationships that can significantly reduce the complexity of the search, in dynamic environments such relationships do not exist. This paper presents a novel state dominance relationship tailored specifically for dynamic environments, and presents a planner that uses that property to plan paths over ten times faster than without using state dominance.