arXiv subjects

Mark Campbell

Publications and source records attributed to Mark Campbell.

61 records · Page 4Linked to original sources

arXiv2017

An Extended Consideration of Joint Exploration and Tracking: JET

Autonomous exploration and multi-object tracking by a team of agents have traditionally been considered as two separate, yet related, problems which are usually solved in two phases: an exploration phase then a tracking phase. The exploration problem is usually viewed through an information theoretic framework where a robotic agent attempts to gather as much information about the environment or an Object of Interest (OI). Conversely, the tracking problem attempts to maintain precise location information about an OI over time. This work proposes a single framework which enables the multi-robot multi-object problem to be solved simultaneously. A hierarchical architecture is used to coordinate robotic agents in the tracking of multiple OIs while simultaneously allowing the task to remain computationally efficient. The primary contributions of this work are a probabilistic constraint on the tracked OIs' covariances guarantees tracking performance throughout the entire mission. The automatic discovery of new OIs, a seamless transition to guaranteed tracking of discovered OIs, and the automatic balancing of exploration with the requirements of tracking.

cs.RO↗

arXiv2017

An Extended Treatment of Uncertainty Constrained robotic Exploration: An Integrated Exploration Planner

Efficient robotic exploration of unknown, sensor limited, global-information-deficient environments poses unique challenges to path planning algorithms. In these difficult environments, no deterministic guarantees on path completion and mission success can be made in general. Integrated Exploration (IE), which strives to combine localization and exploration, must be solved in order to create an autonomous robotic system capable of long term operation in new and challenging environments. This paper formulates a probabilistic framework which allows the creation of exploration algorithms providing probabilistic guarantees of success. A novel connection is made between the Hamiltonian Path Problem and exploration. The Guaranteed Probabilistic Information Explorer (G-PIE) is developed for the IE problem, providing a probabilistic guarantee on path completion, and asymptotic optimality of exploration. A receding horizon formulation, dubbed RH-PIE, is presented which addresses the exponential complexity present in G-PIE. Finally, RH-PIE planner is verified via autonomous, hardware-in-the-loop experiments.

cs.RO↗

arXiv2016

All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles

A novel probabilistic perception algorithm is presented as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle in all-weather conditions. The presented algorithm extends a Rao-Blackwellized Particle Filter originally built with a particle filter for data association and a Kalman filter for multi-object tracking (Miller et al. 2011a) to now also include multiple model tracking for classification. Additionally a state-of-the-art vision detection algorithm that includes heading information for autonomous ground vehicle (AGV) applications was implemented. Cornell's AGV from the DARPA Urban Challenge was upgraded and used to experimentally examine if and how state-of-the-art vision algorithms can complement or replace lidar and radar sensors. Sensor and algorithm performance in adverse weather and lighting conditions is tested. Experimental evaluation demonstrates robust all-weather data association, tracking, and classification where camera, lidar, and radar sensors complement each other inside the joint probabilistic perception algorithm.

eess.SY↗

arXiv2014

Qualitative Relational Mapping and Navigation for Planetary Rovers

This paper presents a novel method for qualitative mapping of large scale spaces. The proposed framework makes use of a graphical representation of the world in order to build a map consisting of qualitative constraints on the geometric relationships between landmark triplets. A novel measurement method based on camera imagery is presented which extends previous work from the field of Qualitative Spatial Reasoning. Measurements are fused into the map using a deterministic, iterative graph update. A Branch-and-Bound approach is taken to solve a set of non-convex feasibility problems required for generating on-line measurements and off-line operator lookup tables. A navigation approach for travel between distant landmarks is developed, using estimates of the Relative Neighborhood Graph extracted from the qualitative map in order to generate a sequence of landmark objectives based on proximity. Average and asymptotic performance of the mapping algorithm is evaluated using Monte Carlo tests on randomly generated maps, and data-driven simulation results are presented for a robot traversing the Jet Propulsion Laboratory Mars Yard while building a relational map. Simulation results demonstrate an initial rapid convergence of qualitative state estimates for visible landmarks, followed by a slow tapering as the remaining ambiguous states are removed from the map.

cs.RO↗

arXiv2013

Discrete and Continuous, Probabilistic Anticipation for Autonomous Robots in Urban Environments

This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to their ability to probabilistically capture continuous and discrete obstacle decisions and behaviors; the predictive system uses the probabilistic output (state estimate and covariance) of a tracking system, and map of the environment to compute the probability distribution over future obstacle states for a specified anticipation horizon. A Gaussian splitting method is proposed based on the sigma-point transform and the nonlinear dynamics function, which enables increased accuracy as the number of mixands grows. An approach to caching elements of this optimal splitting method is proposed, in order to enable real-time implementation. Simulation results and evaluations on data from the research community demonstrate that the proposed algorithm can accurately anticipate the probability distributions over future states of nonlinear systems.

cs.RO↗

arXiv2013

On Generalized Bayesian Data Fusion with Complex Models in Large Scale Networks

Recent advances in communications, mobile computing, and artificial intelligence have greatly expanded the application space of intelligent distributed sensor networks. This in turn motivates the development of generalized Bayesian decentralized data fusion (DDF) algorithms for robust and efficient information sharing among autonomous agents using probabilistic belief models. However, DDF is significantly challenging to implement for general real-world applications requiring the use of dynamic/ad hoc network topologies and complex belief models, such as Gaussian mixtures or hybrid Bayesian networks. To tackle these issues, we first discuss some new key mathematical insights about exact DDF and conservative approximations to DDF. These insights are then used to develop novel generalized DDF algorithms for complex beliefs based on mixture pdfs and conditional factors. Numerical examples motivated by multi-robot target search demonstrate that our methods lead to significantly better fusion results, and thus have great potential to enhance distributed intelligent reasoning in sensor networks.

cs.RO↗

arXiv2013

Maximum Likelihood Fusion of Stochastic Maps

The fusion of independently obtained stochastic maps by collaborating mobile agents is considered. The proposed approach includes two parts: matching of stochastic maps and maximum likelihood alignment. In particular, an affine invariant hypergraph is constructed for each stochastic map, and a bipartite matching via a linear program is used to establish landmark correspondence between stochastic maps. A maximum likelihood alignment procedure is proposed to determine rotation and translation between common landmarks in order to construct a global map within a common frame of reference. A main feature of the proposed approach is its scalability with respect to the number of landmarks: the matching step has polynomial complexity and the maximum likelihood alignment is obtained in closed form. Experimental validation of the proposed fusion approach is performed using the Victoria Park benchmark dataset.

stat.AP↗