Information Acquisition with Sensing Robots: Algorithms and Error Bounds

Utilizing the capabilities of configurable sensing systems requires addressing difficult information gathering problems. Near-optimal approaches exist for sensing systems without internal states. However, when it comes to optimizing the trajectories of mobile sensors the solutions are often greedy and rarely provide performance guarantees. Notably, under linear Gaussian assumptions, the problem becomes deterministic and can be solved off-line. Approaches based on submodularity have been applied by ignoring the sensor dynamics and greedily selecting informative locations in the environment. This paper presents a non-greedy algorithm with suboptimality guarantees, which does not rely on submodularity and takes the sensor dynamics into account. Our method performs provably better than the widely used greedy one. Coupled with linearization and model predictive control, it can be used to generate adaptive policies for mobile sensors with non-linear sensing models. Applications in gas concentration mapping and target tracking are presented.Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014 IEEE International Conference on Robotics and Automatio

Distributed Algorithms for Stochastic Source Seeking With Mobile Robot Networks

Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow a stochastic gradient of the mutual information (MI) between their expected measurements and the expected source location in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal

Robotic Motion Planning in Uncertain Environments via Active Sensing

Perception and control are at the foundation of automation, and in recent years, we have seen growth in feasible applications including self-driving cars and smart homes. As automation moves from regulated, well-monitored locations (e.g., factories) into society, uncertainty in hardware and the environment poses a safety concern. Within this thesis, we focus primarily on uncertainty in the environment and discuss models of the environment known a priori and learned as the robot functions. The robot is tasked with moving from one location or configuration to another while minimizing the expected cost of observation and motion actions. We focus on control that guides the robot to a position/configuration or identifies that it is impossible to reach the position/configuration. We first focus on a robot creating a plan, prior to deployment, based on a known environment model. This model encodes obstacle configurations into different environmental realizations along with a probability this realization will be encountered. The robot is also provided an observation model it may use to sense the environment during the task. We show that minimizing the expected cost from start to goal within these models is NP-Hard. Therefore, we present an efficient algorithm to create a policy which can react to obstacles in real-time while maintaining safety constraints on motion. A by-product of this algorithm is a lower bound on the expected cost of an optimal policy. We compare the policy and lower bound, generated by our algorithm, against that of an optimal policy and existing research. Our focus then shifts to remove prior information about environmental obstacles. We ask the robot to complete a finite number of start to goal tasks and show the general version of this problem is PSPACE-Hard. To reduce the complexity, we develop a method that uses an arbitrary reactionary algorithm from prior work to handle unexpected obstacles. For each new environment experienced, we incrementally update the robot's policy and show that the dependence on the reactionary algorithm is not increasing. Tests are performed on a flexible factory to demonstrate the scalability of this method