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Rapidly-exploring random trees (RRTs) are popular in motion planning because they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s) extend RRTs to the problem of finding the optimal solution, but in doing so asymptotically find the optimal path from the initial state to every state in the planning domain. This behaviour is not only(More)
— Path planning in continuous spaces has traditionally been divided between discrete and sampling-based techniques. Discrete techniques use the principles of dynamic programming to solve a discretized approximation of the problem, while sampling-based techniques use random samples to perform a stochastic search on the continuous state space. In this paper,(More)
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based techniques, such as A*, with the anytime scalability of(More)
Sampling-based optimal planners, such as RRT*, almost-surely converge asymptotically to the optimal solution, but have provably slow convergence rates in high dimensions. This is because their commitment to finding the global optimum compels them to prioritize exploration of the entire problem domain even as its size grows exponentially. Optimization(More)
This paper develops a practical framework for estimating rover position in full-dark conditions by correcting relative odometric estimates with periodic, absolute-attitude measurements from a star tracker. The framework is validated using just under 2.5 kilometres of field data gathered at the University of Toronto’s Koffler Scientific Reserve at Jokers(More)
This paper presents a method to exploit inherent deficiencies in the sensing technology of a SICK laser rangefinder to detect sun positions from 3D lidar scans. Given the common use of SICK lidars on mobile robots, this method enables sun sensing for some existing configurations without requiring additional hardware or configuration costs. Adding sun(More)
Anytime almost-surely asymptotically optimal planners, such as RRT*, incrementally find paths to every state in the search domain. This is inefficient once an initial solution is found as then only states that can provide a better solution need to be considered. Exact knowledge of these states requires solving the problem but can be approximated with(More)
—In this paper, we introduce initial work on an anytime optimal sampling-based planning algorithm, Batch Informed Trees (BIT*). BIT* unifies the developments of Optimal RRT (RRT*) and Fast Marching Trees (FMT*) while extending them with a heuristic. An overview of the algorithm and some initial results are presented, along with a discussion of ongoing(More)
This paper presents a proof-of-concept, rover-based system to locate the source(s) of methane gas on Mars. A distributed open-path spectrometer is mounted on a rover and pointed at several retroreflective signs to measure the line-of-sight methane concentration between the rover and sign. By moving the rover around and accumulating such measurements, the(More)