Automated Hierarchical, Forward-Chaining Temporal Planner for Planetary Robots Exploring Unknown Environments

The transition of mobile robots from a controlled environment towards the real-world represents a major leap in terms of complexity coming primarily from three different factors: partial observability, nondeterminism and dynamic events. To cope with them, robots must achieve some intelligence behaviours to be cost and operationally effective. Two particularly interesting examples of highly complex robotic scenarios are Mars rover missions and the Darpa Robotic Challenge (DRC). In spite of the important differences they present in terms of constraints and requirements, they both have adopted certain level of autonomy to overcome some specific problems. For instance, Mars rovers have been endowed with multiple systems to enable autonomous payload operations and consequently increase science return. In the case of DRC, most teams have autonomous footstep planning or arm trajectory calculation. Even though some specific problems can be addressed with dedicated tools, the general problem remains unsolved: to deploy on-board a reliable reasoning system able to operate robots without human intervention even in complex environments. This is precisely the goal of an automated mission planner. The scientific community has provided plenty of planners able to provide very fast solutions for classical problems, typically characterized by the lack of time and resources representation. Moreover, there are also a handful of applied planners with higher levels of expressiveness at the price of lowest performance. However, a fast, expressive and robust planner has never been used in complex robotic missions. These three properties represent the main drivers for the outcomes of the thesis. To bridge the gap between classical and applied planning, a novel formalism named Hierarchical TimeLine Networks (HTLN) combining Timeline and HTN planning has been proposed. HTLN has been implemented on a mission planner named QuijoteExpress, the first forward-chaining timeline planner to the best of our knowledge. The main idea is to benefit from the great performance of forward-chaining search to resolve temporal problems on the state-space. In addition, QuijoteExpress includes search enhancements such as parallel planning by division of the problem in sub-problems or advanced heuristics management. Regarding expressiveness, the planner incorporates HTN techniques that allow to define hierarchical models and solutions. Finally, plan robustness in uncertain scenarios has been addressed by means of sufficient plans that allow to leave parts of valid plans undefined. To test the planner, a novel lightweight, timeline and ROS-based executive named SanchoExpress has been designed to translate the plans into actions understandable by the different robot subsystems. The entire approach has been tested in two realistic and complementary domains. A cooperative multirover Mars mission and an urban search and rescue mission. The results were extremely positive and opens new promising ways in the field of automated planning applied to robotics

Efficiently Reasoning with Interval Constraints in Forward Search Planning

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting

Automated Hierarchical, Forward-Chaining Temporal Planner for Planetary Robots Exploring Unknown Environments

The transition of mobile robots from a controlled environment towards the real-world represents a major leap in terms of complexity coming primarily from three different factors: partial observability, nondeterminism and dynamic events. To cope with them, robots must achieve some intelligence behaviours to be cost and operationally effective. Two particularly interesting examples of highly complex robotic scenarios are Mars rover missions and the Darpa Robotic Challenge (DRC). In spite of the important differences they present in terms of constraints and requirements, they both have adopted certain level of autonomy to overcome some specific problems. For instance, Mars rovers have been endowed with multiple systems to enable autonomous payload operations and consequently increase science return. In the case of DRC, most teams have autonomous footstep planning or arm trajectory calculation. Even though some specific problems can be addressed with dedicated tools, the general problem remains unsolved: to deploy on-board a reliable reasoning system able to operate robots without human intervention even in complex environments. This is precisely the goal of an automated mission planner. The scientific community has provided plenty of planners able to provide very fast solutions for classical problems, typically characterized by the lack of time and resources representation. Moreover, there are also a handful of applied planners with higher levels of expressiveness at the price of lowest performance. However, a fast, expressive and robust planner has never been used in complex robotic missions. These three properties represent the main drivers for the outcomes of the thesis. To bridge the gap between classical and applied planning, a novel formalism named Hierarchical TimeLine Networks (HTLN) combining Timeline and HTN planning has been proposed. HTLN has been implemented on a mission planner named QuijoteExpress, the first forward-chaining timeline planner to the best of our knowledge. The main idea is to benefit from the great performance of forward-chaining search to resolve temporal problems on the state-space. In addition, QuijoteExpress includes search enhancements such as parallel planning by division of the problem in sub-problems or advanced heuristics management. Regarding expressiveness, the planner incorporates HTN techniques that allow to define hierarchical models and solutions. Finally, plan robustness in uncertain scenarios has been addressed by means of sufficient plans that allow to leave parts of valid plans undefined. To test the planner, a novel lightweight, timeline and ROS-based executive named SanchoExpress has been designed to translate the plans into actions understandable by the different robot subsystems. The entire approach has been tested in two realistic and complementary domains. A cooperative multirover Mars mission and an urban search and rescue mission. The results were extremely positive and opens new promising ways in the field of automated planning applied to robotics

QuijoteExpress - A novel APSI planning system for future space robotic missions

Communications delay, an undeterministic/dynamic environment, science return or cost efficiency are some of the reasons that claim for more autonomy on Space Missions. The main effort of previous Mars Rover missions (i.e., MER) have been focused on-board, where at least three systems have been successfully deployed: Autonomous navigation, SPOTTER (dust-devil & cloud detection) and AEGIS (autonomous data collection). Considering the sophistication of future rover payloads (e.g., Exomars Pasteur & Humboldt) and with the lessons learnt from previous rover missions, an advanced planner/replanner represents one of the main building blocks for enabling technology. This paper discusses a possible advanced planner candidate: QuijoteExpress an heuristic-driven planner based on a evolution of the APSI framework, APSI*. APSI is an ESA owned software framework to develop automated planning and scheduling applications. APSI* allows the definition of complex goals using a planning technique called Hierarchical Task Networks (HTN). The human operator can define in this way a plan in terms of complex goals while the planner is in charge of decomposing them into commands. This technique provides several advantages. First, it represents an improvement on the planner performance, as HTN simplifies the search space. The planner does not need to search anymore how to achieve repetitive tasks. Instead, predefined methods specify the different ways to accomplish complex goals. Second, it also simplifies the modeling which is one of the major problems that engineers need to face to deploy automated tools. Finally, it makes plans easier to understand and validate by humans. QuijoteExpress extends the AP2 planner developed in the frame of the ESA Goal Oriented Autonomous Controller Study (GOAC). It is divided in a strategic planner that leads the search and four tactical planners used to fix the plan. In QuijoteExpress, the problem can be evolved to different layers, being each layer more detailed than the previous one. It tries to extract a solution using an iterative repair technique as follows: First, it repairs the present layer by adding / deleting or moving elements of the plan. Once the layer is valid, the planner decomposes the problem to the next level and repeat the process. Besides the use of HTN, QuijoteExpress present other novelties. By analysing the structure of the problem, it can identify independent parts that will be then processed in parallel. This technique aims to improve the performance and might enable other techniques such as distributed computing. To manage the inherent uncertainty of rover missions, QuijoteExpress replaces the concept of valid plan by sufficient plan. It allows the user to represent problems for which some information is missing and the planner to find solutions for such problems. Finally, we have implemented a hierarchical model of a rover to test the new platform

Design Concepts for a new Temporal Planning Paradigm

Throughout the history of space exploration, the complexity of missions has dramatically increased, from Sputnik in 1957 to MSL, a Mars rover mission launched in November 2011 with advanced autonomous capabilities. As a result, the mission plan that governs a spacecraft has also grown in complexity, pushing to the limit the capability of human operators to understand and manage it. However, the effective representation of large plans with multiple goals and constraints still represents a problem. In this paper, a novel approach to address this problem is presented. We propose a new planning paradigm named HTLN, intended to provide a compact and understandable representation of complex plans and goals based on Timeline planning and Hierarchical Temporal Networks.We also present the design of a planner based on HTLN, which enables new planning approaches that can improve the performance of present real-world domains

Efficiently reasoning with interval constraints in forward search planning

27 de enero - 1 de febrero 2019, Hilton Hawaiian Village, Honolulu,Hawaii, USAIn this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.This work has received funding from the European Union’s Horizon 2020 Research and Innovation programme (Grant Agreement 730086, ERGO); EPSRC grant EP/P008410/1 (AI Planning with Continuous Non-Linear Change); the European Space Agency (ESA/ESTEC) GOTCHA project, Contract No. 4000117648/16/NL/GLC/fk; and Ministerio de Economía, Industria y Competitividad TIN2017-88476-C2-2-R and TIN2015-65686-C5

Metrics for Planetary Rover Planning & Scheduling Algorithms

In addition to its utility in terrestrial-based applications, Automated Planning and Scheduling (P&S) has had a growing impact on space exploration. Such applications require an influx of new technologies to improve performance while not compromising safety. As a result, a reliable method to rapidly assess the effectiveness of new P&S algorithms would be desirable to ensure the fulfillment of all software requirements. This paper introduces RoBen, a mission independent benchmarking tool that provides a standard framework for the evaluation and comparison of P&S algorithms. RoBen considers metrics derived from the model (the system on which the P&S algorithm will operate) as well as user input (e.g., desired problem complexity) to automatically generate relevant problems for quality assessment. A thorough description of the algorithms and metrics used in RoBen is provided, along with the preliminary test results of a P&S algorithm solving RoBen-generated problems

ERGO:A FRAMEWORK FOR THE DEVELOPMENT OF AUTONOMOUS ROBOTS

The European Robotic Goal-Oriented Autonomous Controller ERGO (http://www.h2020-ergo.eu/) is one of the six space robotic projects in the frame of the PERASPERA SRC (http://www.h2020-peraspera.eu/). Its goal is to provide an Autonomy Framework capable of operating at different levels of autonomy, from teleoperations to full on-board autonomy. Even though it has been originally conceived for space robotics, its domain independent design facilitates its application to any terrestrial robotic system.  This paper presents the approach followed, current status and future steps

RoBen: Introducing a Benchmarking Tool for Planetary Rover Planning & Scheduling Algorithms

Automated Planning & Scheduling Systems are nowadays applied in a wide range of spacecraft, from satellites to Mars rovers. The planner is responsible for the generation of valid plans that determine the activities to be performed by the spacecraft, given a set of goals and constraints (the problem), and taking into consideration the status of the spacecraft and environment. Therefore, it represents a critical system that needs to be strictly validated and verified. This paper presents a benchmarking tool called RoBen intended to characterize the performance of timeline planning systems. Using a number of metrics and heuristics, RoBen can generate synthetic problems of a given complexity in order to stress planners at different levels. At the same time, we are looking for properties that could help us to determine when a problem is unsolvable

Efectos de la composición corporal sobre el equilibrio postural en varones adultos españoles sedentarios: un estudio transversal

The aim of this study was to analyze the influence of anthropometric variables, body composition variables and fat distribution on the postural control of sedentary Spanish males. 39 males aged between 25 and 60 years old, with a body mass index between 18 and 35 kg/m2, a stable body weight (no weight gain or loss of 2 kg or more in the last 3 months), and a level of physical activity classified as sedentary or low active (PAL <1.6 via accelerometer) were included in the study. Anthropometric variables (weight, height, body mass index and waist and hip perimeters), body composition variables (fat mass, lean mass and bone mass), body mass distribution (legs, android and total) and postural control were evaluated. A correlation was found between most of the anthropometric and body composition variables, assessed via the Somatosensory ratio of the Sensory Organization Test. Furthermore, individuals with a low percentage of leg and android fat mass presented improved scores when compared to those with higher percentages (97.05±2.66 vs. 95.84±1.64 and 97.00±2.61vs 95.83±1.69, respectively; p<0.05). Sedentary males with a greater body mass index and a higher percentage of leg fat mass and android fat mass are more proprioceptively challenged for maintaining balance.El objetivo de este estudio fue analizar la influencia de las variables antropométricas, de composición corporal y de distribución de la grasa en el control postural de varones españoles sedentarios. Se incluyeron en el estudio 39 varones de entre 25 y 60 años, con un índice de masa corporal entre 18 y 35 kg/m2, un peso corporal estable (sin ganancia o pérdida de peso igual o superior a 2 kg en los últimos 3 meses) y un nivel de actividad física clasificado como sedentario o poco activo (PAL <1,6 mediante acelerómetro). Se evaluaron variables antropométricas (peso, altura, índice de masa corporal y perímetros de cintura y cadera), variables de composición corporal (masa grasa, masa magra y masa ósea), distribución de la masa corporal (piernas, androide y total) y control postural. Se encontró una correlación entre la mayoría de las variables antropométricas y de composición corporal, evaluadas a través de la ratio Somatosensorial del Test de Organización Sensorial. Además, los individuos con un bajo porcentaje de masa grasa en piernas y androides presentaron mejores puntuaciones en comparación con aquellos con porcentajes más elevados (97,05±2,66 vs. 95,84±1,64 y 97,00±2,61vs 95,83±1,69, respectivamente; p<0,05). Los varones sedentarios con un mayor índice de masa corporal y un mayor porcentaje de masa grasa en las piernas y masa grasa androide tienen más dificultades propioceptivas para mantener el equilibrio