This paper presents a formal definition and explicit representation of robot motion risk. Currently, robot motion risk has not been formally defined, but has already been used in motion and path planning. Risk is either implicitly represented as model uncertainty using probabilistic approaches, where the definition of risk is somewhat avoided, or explicitly modeled as a simple function of states, without a formal definition. In this work, we provide formal reasoning behind what risk is for robot motion and propose a formal definition of risk in terms of a sequence of motion, namely path. Mathematical approaches to represent motion risk are also presented, which is in accordance with our risk definition and properties. The definition and representation of risk provide a meaningful way to evaluate or construct robot motion or path plans. The understanding of risk is even of greater interest for the search and rescue community: the deconstructed environments cast extra risk onto the robot, since they are working under extreme conditions. A proper risk representation has the potential to reduce robot failure in the field.
Researchers have looked at explicit representation of risk as well, although a formal definition of risk is still lacking. @cite_2 proposed risk as an accumulative parameterized function based on distance to threats. A similar approach was taken by @cite_6 , where a risk map was generated based on ground orography and the risk of each location could be looked up from the map. Those works based risk only on the distance to some ad hoc hazards. @cite_11 further represented risk of navigating in construction sites in terms of not only distance to hazard zones but also visibility value as a function of state. All those approaches treated risk as a function of state (or location) on the path, and the total motion risk of executing that path was simply the summation of all states' risk (Fig. ). This only focused on one very specific form of risk, neglecting other more general forms.
@cite_11 @cite_6 @cite_2
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This paper presents a generic approach for tactical decision-making under uncertainty in the context of driving. The complexity of this task mainly stems from the fact that rational decision-making in this context must consider several sources of uncertainty: The temporal evolution of situations cannot be predicted without uncertainty because other road users behave stochastically and their goals and plans cannot be measured. Even more important, road users are only able to perceive a tiny part of the current situation with their sensors because measurements are noisy and most of the environment is occluded. In order to anticipate the consequences of decisions a probabilistic approach, considering both forms of uncertainty, is necessary. The authors address this by formulating the task of driving as a continuous Partially Observable Markov Decision Process (POMDP) that can be automatically optimized for different scenarios. As driving is a continuous-space problem, the belief space is infinite-dimensional. The authors do not use a symbolic representation or discretize the state space a priori because there is no representation of the state space that is optimal for every situation. Instead, they employ a continuous POMDP solver that learns a good representation of the specific situation. Risk analysis is traditionally considered a critical activity for the whole software system’s lifecycle. Risks are identified by considering technical aspects (e.g., failures of the system, unavailability of services, etc.) and handled by suitable countermeasures through a refined design. This, however, introduces the problem of reconsidering system requirements. In this paper, we propose a goal-oriented approach for analyzing risks during the requirements analysis phase. Risks are analyzed along with stakeholder interests, and then countermeasures are identified and introduced as part of the system’s requirements. This work extends the Tropos goal modeling formal framework proposing new concepts, qualitative reasoning techniques, and methodological procedures. The approach is based on a conceptual framework composed of three main layers: assets, events, and treatments. We use “loan origination process” case study to illustrate the proposal, and we present and discuss experimental results obtained from the case study. Advancements in mobile technology and computing have fostered the collection of a large number of civic datasets that capture the pulse of urban life. Furthermore, the open government and data initiative has led many local authorities to make these datasets publicly available, hoping to drive innovation that will further improve the quality of life for the city-dwellers. In this paper, we develop a novel application that utilizes crime data to provide safe urban navigation. Specifically, using crime data from Chicago and Philadelphia we develop a risk model for their street urban network, which allows us to estimate the relative probability of a crime on any road segment. Given such model we define two variants of the SafePaths problem where the goal is to find a short and low-risk path between a source and a destination location. Since both the length and the risk of the path are equally important but cannot be combined into a single objective, we approach the urban-navigation problem as a biobjective shortest path problem. Our algorithms aim to output a small set of paths that provide tradeoffs between distance and safety. Our experiments demonstrate the efficacy of our algorithms and their practical applicability. Autonomous planning in safety critical systemsis a difficult task where decisions must carefully balanceoptimisation for performance goals of the system while alsokeeping the system away from safety hazards. These tasksoften conflict, and hence present a challenging multi-objectiveplanning problem where at least one of the objectives relates tosafety risk. Recasting safety risk into an objective introducesadditional requirements on planning algorithms: safety riskcannot be "averaged out" nor can it be combined with otherobjectives without loss of information and losing its intendedpurpose as a tool in risk reduction. Thus, existing algorithmsfor multi-objective planning cannot be used directly as theydo not provide any facility to accurately track and updatesafety risk. A common workaround is to restrict availabledecisions to those guaranteed safe a priori, but this can beoverly conservative and hamper performance significantly. Inthis paper, we propose a planning algorithm based on multiobjectiveMonte-Carlo Tree Search to resolve these problems byrecognising safety risk as a first class objective. Our algorithmexplicitly models the safety of the system separately from theperformance of the system, uses safety risk to both optimiseand provide constraints for safety in the planning process, anduses an ALARP-based preference selection method to choose anappropriate safe plan from its output. The preference selectionmethod chooses from the set of multiple safe plans to weighrisk against performance. We demonstrate the behaviour ofthe algorithm using an example representative of safety criticaldecision-making.