Efficient goal directed navigation using RatSLAM

RatSLAM is a system for vision based Simultaneous Localization and Mapping (SLAM) that has been shown to be capable of building stable representations of real world environments. In this paper we describe a method for using RatSLAM representations as the basis for navigation to designated goal locations. The method uses a new component, goal memory, to learn the temporal gradient between places. Paths are recalled or inferred from the goal memory by following the temporal gradient from the robot’s current position to the goal location. Experimental results have been gathered in a combined office and laboratory environment using a Pioneer robot. The experiments show that the robot can perform vision based SLAM on-line and in real time, and then use those representations immediately to navigate directly to designated goal locations.

A hierarchical model of goal directed navigation selects trajectories in a visual environment

We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model’s flexibility in representing large real world environments using odometry information obtained from challenging video sequences. We acquire the visual data from a camera mounted on a small tele-operated vehicle. The camera has a panoramic field of view with its focal point approximately 5 cm above the ground level, similar to what would be expected from a rat’s point of view. Using established algorithms for calculating perceptual speed from the apparent rate of visual change over time, we generate raw dead reckoning information which loses spatial fidelity over time due to error accumulation. We rectify the loss of fidelity by exploiting the loop-closure detection ability of a biologically inspired, robot navigation model termed RatSLAM. The rectified motion information serves as a velocity input to the HiLAM to encode the environment in the form of grid cell and place cell maps. Finally, we show goal directed path planning results of HiLAM in two different environments, an indoor square maze used in rodent experiments and an outdoor arena more than two orders of magnitude larger than the indoor maze. Together these results bridge for the first time the gap between higher fidelity bio-inspired navigation models (HiLAM) and more abstracted but highly functional bio-inspired robotic mapping systems (RatSLAM), and move from simulated environments into real-world studies in rodent-sized arenas and beyond.

Principles of goal-directed spatial robot navigation in biomimetic models

Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in ‘real-world’ environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.

Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration

In this paper we present for the first time a complete symbolic navigation system that performs goal-directed exploration to unfamiliar environments on a physical robot. We introduce a novel construct called the abstract map to link provided symbolic spatial information with observed symbolic information and actual places in the real world. Symbolic information is observed using a text recognition system that has been developed specifically for the application of reading door labels. In the study described in this paper, the robot was provided with a floor plan and a destination. The destination was specified by a room number, used both in the floor plan and on the door to the room. The robot autonomously navigated to the destination using its text recognition, abstract map, mapping, and path planning systems. The robot used the symbolic navigation system to determine an efficient path to the destination, and reached the goal in two different real-world environments. Simulation results show that the system reduces the time required to navigate to a goal when compared to random exploration.

Constructing abstract maps from spatial descriptions for goal-directed exploration

This paper describes ongoing work on a system using spatial descriptions to construct abstract maps that can be used for goal-directed exploration in an unfamiliar office environment. Abstract maps contain membership, connectivity, and spatial layout information extracted from symbolic spatial information. In goal-directed exploration, the robot would then link this information with observed symbolic information and its grounded world representation. We demonstrate the ability of the system to extract and represent membership, connectivity, and spatial layout information from spatial descriptions of an office environment. In the planned study, the robot will navigate to the goal location using the abstract map to inform the best direction to explore in.

Persistent navigation and mapping using a biologically inspired SLAM system

The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times.

The role of desire in understanding intentions to drink responsibly : an application of the model of goal-directed behaviour

This study models young people's moderate drinking decision-making using the Model of Goal-Directed Behaviour (MGB), thus presenting insights into young people's desires and intentions to drink responsibly. Testing the applicability of the MGB to quantitatively analyse responsible drinking, the explanatory sphere of the MGB is extended. An online survey resulted in 1522 completed questionnaires from respondents aged between 18 and 25 years. Collected data were analysed with structural equation modelling (SEM) using SPSS AMOS21 (IBM, New York, NY, USA) software. The key finding of this study is that an individual's desire to drink moderately is the most important predictor of young people's responsible drinking intentions. Our use of MGB provides further evidence that there is a strong distinction between consumer desires and intentions.

RatSLAM on the edge : revealing a coherent representation from an overloaded rat brain

The RatSLAM system can perform vision based SLAM using a computational model of the rodent hippocampus. When the number of pose cells used to represent space in RatSLAM is reduced, artifacts are introduced that hinder its use for goal directed navigation. This paper describes a new component for the RatSLAM system called an experience map, which provides a coherent representation for goal directed navigation. Results are presented for two sets of real world experiments, including comparison with the original goal memory system's performance in the same environment. Preliminary results are also presented demonstrating the ability of the experience map to adapt to simple short term changes in the environment.

Reasoning about natural language phrases for semantic goal driven exploration

This paper presents a symbolic navigation system that uses spatial language descriptions to inform goal-directed exploration in unfamiliar office environments. An abstract map is created from a collection of natural language phrases describing the spatial layout of the environment. The spatial representation in the abstract map is controlled by a constraint based interpretation of each natural language phrase. In goal-directed exploration of an unseen office environment, the robot links the information in the abstract map to observed symbolic information and its grounded world representation. This paper demonstrates the ability of the system, in both simulated and real-world trials, to efficiently find target rooms in environments that it has never been to previously. In three unexplored environments, it is shown that on average the system travels only 8.42% further than the optimal path when using only natural language phrases to complete navigation tasks.

Lingodroids : studies in spatial cognition and language

The Lingodroids are a pair of mobile robots that evolve a language for places and relationships between places (based on distance and direction). Each robot in these studies has its own understanding of the layout of the world, based on its unique experiences and exploration of the environment. Despite having different internal representations of the world, the robots are able to develop a common lexicon for places, and then use simple sentences to explain and understand relationships between places even places that they could not physically experience, such as areas behind closed doors. By learning the language, the robots are able to develop representations for places that are inaccessible to them, and later, when the doors are opened, use those representations to perform goal-directed behavior.

Towards a multiple-goal pursuit and multilevel view of continuing information technology usage : a process model integration and a research agenda

The value of information technology (IT) is often realized when continuously being used after users’ initial acceptance. However, previous research on continuing IT usage is limited for dismissing the importance of mental goals in directing users’ behaviors and for inadequately accommodating the group context of users. This in-progress paper offers a synthesis of several literature to conceptualize continuing IT usage as multilevel constructs and to view IT usage behavior as directed and energized by a set of mental goals. Drawing from the self-regulation theory in the social psychology, this paper proposes a process model, positioning continuing IT usage as multiple-goal pursuit. An agent-based modeling approach is suggested to further explore causal and analytical implications of the proposed process model.

Find my office: Navigating real space from semantic descriptions

This paper shows that by using only symbolic language phrases, a mobile robot can purposefully navigate to specified rooms in previously unexplored environments. The robot intelligently organises a symbolic language description of the unseen environment and “imagines” a representative map, called the abstract map. The abstract map is an internal representation of the topological structure and spatial layout of symbolically defined locations. To perform goal-directed exploration, the abstract map creates a high-level semantic plan to reason about spaces beyond the robot’s known world. While completing the plan, the robot uses the metric guidance provided by a spatial layout, and grounded observations of door labels, to efficiently guide its navigation. The system is shown to complete exploration in unexplored spaces by travelling only 13.3% further than the optimal path.

Creative design and the generative evolutionary paradigm

Design as seen from the designer's perspective is a series of amazing imaginative jumps or creative leaps. But design as seen by the design historian is a smooth progression or evolution of ideas that they seem self-evident and inevitable after the event. But the next step is anything but obvious for the artist/creator/inventor/designer stuck at that point just before the creative leap. They know where they have come from and have a general sense of where they are going, but often do not have a precise target or goal. This is why it is misleading to talk of design as a problem-solving activity - it is better defined as a problem-finding activity. This has been very frustrating for those trying to assist the design process with computer-based, problem-solving techniques. By the time the problem has been defined, it has been solved. Indeed the solution is often the very definition of the problem. Design must be creative-or it is mere imitation. But since this crucial creative leap seem inevitable after the event, the question must arise, can we find some way of searching the space ahead? Of course there are serious problems of knowing what we are looking for and the vastness of the search space. It may be better to discard altogether the term "searching" in the context of the design process: Conceptual analogies such as search, search spaces and fitness landscapes aim to elucidate the design process. However, the vastness of the multidimensional spaces involved make these analogies misguided and they thereby actually result in further confounding the issue. The term search becomes a misnomer since it has connotations that imply that it is possible to find what you are looking for. In such vast spaces the term search must be discarded. Thus, any attempt at searching for the highest peak in the fitness landscape as an optimal solution is also meaningless. Futhermore, even the very existence of a fitness landscape is fallacious. Although alternatives in the same region of the vast space can be compared to one another, distant alternatives will stem from radically different roots and will therefore not be comparable in any straightforward manner (Janssen 2000). Nevertheless we still have this tantalizing possibility that if a creative idea seems inevitable after the event, then somehow might the process be rserved? This may be as improbable as attempting to reverse time. A more helpful analogy is from nature, where it is generally assumed that the process of evolution is not long-term goal directed or teleological. Dennett points out a common minsunderstanding of Darwinism: the idea that evolution by natural selection is a procedure for producing human beings. Evolution can have produced humankind by an algorithmic process, without its being true that evolution is an algorithm for producing us. If we were to wind the tape of life back and run this algorithm again, the likelihood of "us" being created again is infinitesimally small (Gould 1989; Dennett 1995). But nevertheless Mother Nature has proved a remarkably successful, resourceful, and imaginative inventor generating a constant flow of incredible new design ideas to fire our imagination. Hence the current interest in the potential of the evolutionary paradigm in design. These evolutionary methods are frequently based on techniques such as the application of evolutionary algorithms that are usually thought of as search algorithms. It is necessary to abandon such connections with searching and see the evolutionary algorithm as a direct analogy with the evolutionary processes of nature. The process of natural selection can generate a wealth of alternative experiements, and the better ones survive. There is no one solution, there is no optimal solution, but there is continuous experiment. Nature is profligate with her prototyping and ruthless in her elimination of less successful experiments. Most importantly, nature has all the time in the world. As designers we cannot afford prototyping and ruthless experiment, nor can we operate on the time scale of the natural design process. Instead we can use the computer to compress space and time and to perform virtual prototyping and evaluation before committing ourselves to actual prototypes. This is the hypothesis underlying the evolutionary paradigm in design (1992, 1995).