Constraint Based Object State Modeling

The International Journal of Robotics Research, 2015

We investigate the problem of planning under uncertainty, with application to mobile robotics. We propose a probabilistic framework in which the robot bases its decisions on the generalized belief, which is a probabilistic description of its own state and of external variables of interest. The approach naturally leads to a dual-layer architecture: an inner estimation layer, which performs inference to predict the outcome of possible decisions, and an outer decisional layer which is in charge of deciding the best action to undertake. Decision making is entrusted to a Model Predictive Control (MPC) scheme. The formulation is valid for general cost functions and does not discretize the state or control space, enabling planning in continuous domain. Moreover, it allows to relax the assumption of maximum likelihood observations: predicted measurements are treated as random variables, and binary random variables are used to model the event that a measurement is actually taken by the robot. We successfully apply our approach to the problem of uncertainty-constrained exploration, in which the robot has to perform tasks in an unknown environment, while maintaining localization uncertainty within given bounds. We present an extensive numerical analysis of the proposed approach and compare it against related work. In practice, our planning approach produces smooth and natural trajectories and is able to impose soft upper bounds on the uncertainty. Finally, we exploit the results of this analysis to identify current limitations and show that the proposed framework can accommodate several desirable extensions.

2003

This paper deals with the probabilistic modeling of an environment that a robot has to navigate in. We use a method for the probabilistic modeling of space called the Bayesian Map formalism. This formalism allows incremental building of models: we define the Superposition operator, which is a formally well-defined operator. We present first a syntactic version of this operator, and second, a version where the previously obtained model is refined and enriched by experimental learning. In the resulting superposed map, locations are the conjunction of underlying possible locations, which allows for more precise localization and more complex tasks. A theoretical example validates the concept, and hints at its usefulness for realistic robotic scenarios. * Julien Diard is currently with the Laboratoire de Physiologie de la Perception et de l'Action, at the Collège de France, Paris and the Department of Mechanical Engineering of the National University of Singapore. He can be reached at Julien.Diard@free.fr.

Lecture Notes in Computer Science, 2004

2008

In this paper we present a novel approach using constraint based techniques for world modeling, i.e. self localization and object modeling. Within the last years, we have seen a reduction of landmarks as beacons, colored goals, within the RoboCup domain. Using other features as line information becomes more important. Using such sensor data is tricky, especially when the resulting position belief is stretched over a larger area. Constraints can overcome this limitations, as they have several advantages: They can represent large distributions and are easy to store and to communicate to other robots. Propagation of a several constraints can be computationally cheap. Even high dimensional belief functions can be used. We will describe a sample implementation and show experimental results.

Robotics and Autonomous Systems, 2003

Mobile robot localization deals with uncertain sensory information as well as uncertain data association. In this paper we present a probabilistic feature-based approach to global localization and pose tracking which explicitly addresses both problems. Location hypotheses are represented as Gaussian distributions. Hypotheses are found by a search in the tree of possible local-to-global feature associations, given a local map of observed features and a global map of the environment. During tree traversal, several types of geometric constraints are used to determine statistically feasible associations. As soon as hypotheses are available, they are tracked using the same constraint-based technique. Track splitting is performed when location ambiguity arises from uncertainties and sensing. This yields a very robust localization technique which can deal with significant errors from odometry, collisions and kidnapping. Experiments in simulation and with a real robot demonstrate these properties at low computational costs.

The robot localization problem is a key problem in making truly autonomous robots. If a robot does not know where it is, it can be difficult to determine what to do next. In order to localize itself, a robot has access to relative and absolute measurements giving the robot feedback about its driving actions and the situation of the environment around the robot. Given this information, the robot has to determine its location as accurately as possible. What makes this difficult is the existence of uncertainty in both the driving and the sensing of the robot. The uncertain information needs to be combined in an optimal way.

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

1995

Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. We have developed an approach that uses partially observable Markov models to robustly track a robot’s location and integrates it with a planning and execution monitoring approach that uses this information to control the robot’s actions. The approach explicitly maintains a probability distributionover the possible locations of the robot, taking into account various sources of uncertainty, including approximate knowledge of the environment, actuator uncertainty, and sensor noise. A novel feature of our approach is its integration of topological map information with approximate metric information. We demonstrate the reliability of this approach, especially its ability to smoothly recover from errors in sensing.

2008

The lack of a parameterized observation model in robot localization using occupancy grids requires the application of sampling-based methods, or particle filters. This work addresses the problem of optimal Bayesian filtering for dynamic systems with observation models that cannot be approximated properly as any parameterized distribution, which includes localization and SLAM with occupancy grids. By integrating ideas from previous works on adaptive sample size, auxiliary particle filters, and rejection sampling, we derive a new particle filter algorithm that enables the usage of the optimal proposal distribution to estimate the true posterior density of a non-parametric dynamic system. Our solution avoids approximations adopted in previous approaches at the cost of a higher computational burden. We present simulations and experimental results for a real robot showing the suitability of the method for localization.

Robotics Automation and Control, 2008