Task scheduling and merging in space and time

Every day, robots are being deployed in more challenging environments, where they are required to perform complex tasks. In order to achieve these tasks, robots rely on intelligent deliberation algorithms. In this thesis, we study two deliberation approaches – task scheduling and task planning. We extend these approaches in order to not only deal with temporal and spatial constraints imposed by the environment, but also exploit them to be more efficient than the state-of-the-art approaches. Our first main contribution is a scheduler that exploits a heuristic based on Allen’s interval algebra to prune the search space to be traversed by a mixed integer program. We empirically show that the proposed scheduler outperforms the state of the art by at least one order of magnitude. Furthermore, the scheduler has been deployed on several mobile robots in long-term autonomy scenarios. Our second main contribution is the POPMERX algorithm, which is based on merging of partially ordered temporal plans. POPMERX first reasons with the spatial and temporal structure of separately generated plans. Then, it merges these plans into a single final plan, while optimising the makespan of the merged plan. We empirically show that POPMERX produces better plans that the-state-ofthe- art planners on temporal domains with time windows

Where's Waldo at time t? Using spatio-temporal models for mobile robot search

We present a novel approach to mobile robot search for non-stationary objects in partially known environments. We formulate the search as a path planning problem in an environment where the probability of object occurrences at particular locations is a function of time. We propose to explicitly model the dynamics of the object occurrences by their frequency spectra. Using this spectral model, our path planning algorithm can construct plans that reflect the likelihoods of object locations at the time the search is performed. Three datasets collected over several months containing person and object occurrences in residential and office environments were chosen to evaluate the approach. Several types of spatio-temporal models were created for each of these datasets and the efficiency of the search method was assessed by measuring the time it took to locate a particular object. The results indicate that modeling the dynamics of object occurrences reduces the search time by 25% to 65% compared to maps that neglect these dynamics

Partial Order Temporal Plan Merging for Mobile Robot Tasks

For many mobile service robot applications, planning problems are based on deciding how and when to navigate to certain locations and execute certain tasks. Typically, many of these tasks are independent from one another, and the main objective is to obtain plans that efficiently take into account where these tasks can be executed and when execution is allowed. In this paper, we present an approach, based on merging of partial order plans with durative actions, that can quickly and effectively generate a plan for a set of independent goals. This plan exploits some of the synergies of the plans for each single task, such as common locations where certain actions should be executed. We evaluate our approach in benchmarking domains, comparing it with state-of-the-art planners and showing how it provides a good trade-off between the approach of sequencing the plans for each task (which is fast but produces poor results), and the approach of planning for a conjunction of all the goals (which is slow but produces good results)

The STRANDS project: long-term autonomy in everyday environments

Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance