Perception-aware Path Planning

In this paper, we give a double twist to the problem of planning under uncertainty. State-of-the-art planners seek to minimize the localization uncertainty by only considering the geometric structure of the scene. In this paper, we argue that motion planning for vision-controlled robots should be perception aware in that the robot should also favor texture-rich areas to minimize the localization uncertainty during a goal-reaching task. Thus, we describe how to optimally incorporate the photometric information (i.e., texture) of the scene, in addition to the the geometric one, to compute the uncertainty of vision-based localization during path planning. To avoid the caveats of feature-based localization systems (i.e., dependence on feature type and user-defined thresholds), we use dense, direct methods. This allows us to compute the localization uncertainty directly from the intensity values of every pixel in the image. We also describe how to compute trajectories online, considering also scenarios with no prior knowledge about the map. The proposed framework is general and can easily be adapted to different robotic platforms and scenarios. The effectiveness of our approach is demonstrated with extensive experiments in both simulated and real-world environments using a vision-controlled micro aerial vehicle.Comment: 16 pages, 20 figures, revised version. Conditionally accepted for IEEE Transactions on Robotic

PiP: Planning-informed Trajectory Prediction for Autonomous Driving

It is critical to predict the motion of surrounding vehicles for self-driving planning, especially in a socially compliant and flexible way. However, future prediction is challenging due to the interaction and uncertainty in driving behaviors. We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. Our approach is differentiated from the traditional manner of prediction, which is only based on historical information and decoupled with planning. By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets. Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.Comment: European Conference on Computer Vision (ECCV) 2020; Project page at http://haoran-song.github.io/planning-informed-predictio

URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments

A major challenge with off-road autonomous navigation is the lack of maps or road markings that can be used to plan a path for autonomous robots. Classical path planning methods mostly assume a perfectly known environment without accounting for the inherent perception and sensing uncertainty from detecting terrain and obstacles in off-road environments. Recent work in computer vision and deep neural networks has advanced the capability of terrain traversability segmentation from raw images; however, the feasibility of using these noisy segmentation maps for navigation and path planning has not been adequately explored. To address this problem, this research proposes an uncertainty-aware path planning method, URA* using aerial images for autonomous navigation in off-road environments. An ensemble convolutional neural network (CNN) model is first used to perform pixel-level traversability estimation from aerial images of the region of interest. The traversability predictions are represented as a grid of traversal probability values. An uncertainty-aware planner is then applied to compute the best path from a start point to a goal point given these noisy traversal probability estimates. The proposed planner also incorporates replanning techniques to allow rapid replanning during online robot operation. The proposed method is evaluated on the Massachusetts Road Dataset, the DeepGlobe dataset, as well as a dataset of aerial images from off-road proving grounds at Mississippi State University. Results show that the proposed image segmentation and planning methods outperform conventional planning algorithms in terms of the quality and feasibility of the initial path, as well as the quality of replanned paths

Handling uncertainty in transport planning and decision making - Report of a roundtable discussion held in London on 20 July 2018

In the 1700s, the French philosopher Voltaire reportedly said “Uncertainty is an uncomfortable position. But certainty is an absurd one.” The transport sector is becoming increasingly alive to how uncertain the future is. There is significant (or ‘deep’) uncertainty about the extent to which existing trends, relationships, technologies, economic and social forces, preferences and constraints will carry into the future. Uncomfortable though it may be, there is a need in our transport planning and decision making to avoid absurdity and address this. This report reflects the insights gained from a roundtable workshop in London convened to discuss the matter.For some, absurdity concerns the continuation of a longstanding norm in transport planning of appraising future prospects through a reliance on being able to judge what the most likely future is or, in forecasting terms, what the central projection can be considered to be (with management of uncertainty through some form of error band either side of this). Such approaches no longer command a professional or public consensus. The notion of ‘most likely’ is either being challenged or a divergence of views exists over which type of future can be deemed most likely.Orthodox notions of predicting the future are giving way to scenario planning in which multiple alternative scenarios – substantially different pictures of what course of events might unfold - are entertained. One of the challenges here is in judging whether the true extent of uncertainty is being accounted for, distinguishing between probable (likely to happen), plausible (could happen) and possible (might happen) futures. Views are subjective (and further coloured by opinion on preferable (desirable) futures). This can appear uncomfortable compared to the illusion of a well-defined most likely future that frames the decision making task. Yet the act of embracing and accommodating rather than concealing uncertainty holds the prospect of better supporting and informing decision making.The following proposition emerges for an approach that may be especially well suited to the early strategic planning and optioneering stages of the policymaking process. Set against a wish to fulfil a high level vision, the aim is to consider the implications of different courses of policy action in the face of multiple plausible futures. Does a course of action align with the vision in each plausible future considered or does it align well in some and not in others? The intention is to reconcile risk and yield. The best course of policy action for one assumed future may give high yield in relation to the vision but may also carry a high risk of misalignment or even failure in other plausible futures. Meanwhile, a course of action which has reasonable alignment across multiple futures may offer a lower (but acceptable) yield but with lower risk.As becomes apparent from the considerations above, the handling of uncertainty is a wicked problem that is inherently insoluble. It is wicked in the sense that it concerns: (i) divergence in views, understanding and values across stakeholders; (ii) knowledge gaps and a lack of ‘evidence’; and (iii) needing to deal with complex relationships between multiple considerations. This should not imply policymaking paralysis if progress can be made as outlined above in terms of embracing and accommodating uncertainty.In critically examining how uncertainty has been, and continues to be, handled in mainstream transport planning practice and exploring the prospects for changing this, the following key issues emerged through the roundtable discussion:Transport planning inertia – Well established approaches, procedures and norms can conspire against developing and adopting new approaches that may be better able to handle uncertainty but which are unfamiliar and potentially challenging to communicate. Acknowledging uncertainty can have connotations of poor confidence and conviction in decisions being made – for example in the context of public inquiries.Learning by doing – The application of new approaches should be strongly encouraged, with a ‘learning by doing’ philosophy where experiences of those new approaches and the lessons learned are shared with others. Continued dialogue of the sort fostered by the roundtable is important. Guidance should be seen as an accompaniment to this evolutionary approach and as something which itself must be flexible and evolving. The growing signs of transport authorities wishing to take account of deep uncertainty in their decision making are to be welcomed.Closing down uncertainty – To avoid decision making paralysis, the exposure or opening out of uncertainty through scenario planning needs to be followed by an appropriate process of closing down. Closing down refers to how the exposed uncertainty is then accounted for in informing and enabling decision making. Closing down can take the form of concealing, reducing or accommodating uncertainty and distinguishing between them is important. Uncertainty may be concealed by reversion to focusing upon a most likely future. It may be reduced through better monitoring and understanding of change taking place or through greater effort to control the shaping of the future. Accommodation of uncertainty (as outlined above) involves making sense of what to do in decision making terms with the uncertainty that has been exposed.Analytical fitness for purpose – Especially in the face of finite time and resources, it is important that the analytical approach supporting each stage in the policymaking process is fit for purpose. There is a risk that emphasis is currently being put in the wrong place in terms of analytical effort and rigour. Heavyweight modelling tools may be used to address a small number of scenarios when, particularly at the earlier stages in the policymaking process, simpler (though not to infer less robust) analytical tools can be more effective in exploring the uncertainty space of plausible futures and enabling dialogue and development of views of actors in the process.Communication is key – The analytical tools will only ever be a part of the wider process of examining and interpreting the uncertainty faced. It is important that the actors involved in that process - from the analysts to the decision makers themselves - are enabled rather than confused by how the tools are used and their results conveyed. There is a balance to be struck between the breadth and depth of examination of the uncertainty space. There is also a need to recognise the place of both ‘narrative’ and ‘numbers’ in order to ensure effective engagement with actors and to communicate the credibility of, and insights from, scenarios analysis.Guidance and leadership – The current existence of guidance for appraisal, including the handling of uncertainty, may intend to provide latitude for interpretation rather than ‘rules’ to be complied with. However, this is not always how guidance is treated in practice. As approaches to handling uncertainty are evolved, it will be particularly important that accompanying guidance is enabling rather than constraining. This may require that practitioners are guided on how to use the guidance (with a role for case studies). Leadership will also be critical within organisations in providing staff with appropriate direction, mandate and agency to address uncertainty.Resources and expertise - We may be facing a perfect storm in transport planning: greater uncertainty over the future at a time of depleted resources and capabilities to address business as usual, let alone the handling of uncertainty. One means of starting to address this would be to consider how available resources can be redistributed across the transport planning and decision making process alongside seeking to reconsider the makeup of experts required to handle uncertainty and communicate it to decision makers. Handling uncertainty must become integral to mainstream practice rather than a bolt-on to it, with the latter risking being misaligned and ignored

Adaptation of WASH Services Delivery to Climate Change and Other Sources of Risk and Uncertainty

This report urges WASH sector practitioners to take more seriously the threat of climate change and the consequences it could have on their work. By considering climate change within a risk and uncertainty framework, the field can use the multitude of approaches laid out here to adequately protect itself against a range of direct and indirect impacts. Eleven methods and tools for this specific type of risk management are described, including practical advice on how to implement them successfully

Eco-polycentric urban systems: an ecological region perspective for network cities

The research presented in this paper is a work in progress. It provides linkages between the author’s earlier research under the sustainable land planning framework (SLP) and emergent ideas and planning and design strategies, centered on the (landscape) ecological dimension of cities’ sustainability. It reviews several concepts, paradigms, and metaphors that have been emerging during the last decade, which can contribute to expand our vision on city planning and design. Among other issues, city form—monocentric, polycentric, and diffused—is discussed. The hypothesis set forth is that cities can improve the pathway to sustainability by adopting intermediate, network urban forms such as polycentric urban systems (PUS) under a broader vision (as compared to the current paradigm), to make way to urban ecological regions. It discusses how both the principles of SLP and those emergent ideas can contribute to integrate PUS with their functional hinterland, adopting an ecosystemic viewpoint of cities. It proposes to redirect the current dominant economic focus of PUS to include all of the other functions that are essential to urbanites, such as production (including the 3Rs), recreation, and ecology in a balanced way. Landscape ecology principles are combined with complexity science in order to deal with uncertainty to improve regional systems’ resilience. Cooperation in its multiple forms is seen as a fundamental social, but also economic process contributing to the urban network functioning, including its evolving capabilities for self-organization and adaptation.info:eu-repo/semantics/publishedVersio

Differentiable Algorithm Networks for Composable Robot Learning

This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a differentiable robot algorithm and an associated model; and it is trained end-to-end from data. DAN combines the strengths of model-driven modular system design and data-driven end-to-end learning. The algorithms and models act as structural assumptions to reduce the data requirements for learning; end-to-end learning allows the modules to adapt to one another and compensate for imperfect models and algorithms, in order to achieve the best overall system performance. We illustrate the DAN methodology through a case study on a simulated robot system, which learns to navigate in complex 3-D environments with only local visual observations and an image of a partially correct 2-D floor map.Comment: RSS 2019 camera ready. Video is available at https://youtu.be/4jcYlTSJF4