Jonathan D. Gammell

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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)
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)
A network of reusable paths (NRP) allows for a new approach to planetary surface exploration using a mobile robot. NRP gives the robot the ability to accurately return to any previously visited point. This allows missionlevel improvements by enabling parallel exploration of scientific targets. NRP would be particularly useful for sample-return missions to(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, we(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 describes a proposed operational architecture for a planetary worksite mapping mission concept. To map three-dimensional (3D) planetary terrain, we propose to use a rover equipped with a laser rangefinder, and employ a stop-scan-go approach with a human-inthe-loop. In the operational cycle, the rover collects locally consistent 3D range data(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)
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 future(More)