1,249 research outputs found
The Critical Radius in Sampling-based Motion Planning
We develop a new analysis of sampling-based motion planning in Euclidean
space with uniform random sampling, which significantly improves upon the
celebrated result of Karaman and Frazzoli (2011) and subsequent work.
Particularly, we prove the existence of a critical connection radius
proportional to for samples and dimensions:
Below this value the planner is guaranteed to fail (similarly shown by the
aforementioned work, ibid.). More importantly, for larger radius values the
planner is asymptotically (near-)optimal. Furthermore, our analysis yields an
explicit lower bound of on the probability of success. A
practical implication of our work is that asymptotic (near-)optimality is
achieved when each sample is connected to only neighbors. This is
in stark contrast to previous work which requires
connections, that are induced by a radius of order . Our analysis is not restricted to PRM and applies to a
variety of PRM-based planners, including RRG, FMT* and BTT. Continuum
percolation plays an important role in our proofs. Lastly, we develop similar
theory for all the aforementioned planners when constructed with deterministic
samples, which are then sparsified in a randomized fashion. We believe that
this new model, and its analysis, is interesting in its own right
Priority-based intersection management with kinodynamic constraints
We consider the problem of coordinating a collection of robots at an
intersection area taking into account dynamical constraints due to actuator
limitations. We adopt the coordination space approach, which is standard in
multiple robot motion planning. Assuming the priorities between robots are
assigned in advance and the existence of a collision-free trajectory respecting
those priorities, we propose a provably safe trajectory planner satisfying
kinodynamic constraints. The algorithm is shown to run in real time and to
return safe (collision-free) trajectories. Simulation results on synthetic data
illustrate the benefits of the approach.Comment: to be presented at ECC2014; 6 page
Optimal task positioning in multi-robot cells, using nested meta-heuristic swarm algorithms
Abstract Process planning of multi-robot cells is usually a manual and time consuming activity, based on trials-and-errors. A co-manipulation problem is analysed, where one robot handles the work-piece and one robot performs a task on it and a method to find the optimal pose of the work-piece is proposed. The method, based on a combination of Whale Optimization Algorithm and Ant Colony Optimization algorithm, minimize a performance index while taking into account technological and kinematics constraints. The index evaluates process accuracy considering transmission elasticity, backslashes and distance from joint limits. Numerical simulations demonstrate the method robustness and convergence
Sampling-Based Temporal Logic Path Planning
In this paper, we propose a sampling-based motion planning algorithm that
finds an infinite path satisfying a Linear Temporal Logic (LTL) formula over a
set of properties satisfied by some regions in a given environment. The
algorithm has three main features. First, it is incremental, in the sense that
the procedure for finding a satisfying path at each iteration scales only with
the number of new samples generated at that iteration. Second, the underlying
graph is sparse, which guarantees the low complexity of the overall method.
Third, it is probabilistically complete. Examples illustrating the usefulness
and the performance of the method are included.Comment: 8 pages, 4 figures; extended version of the paper presented at IROS
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