Vehicle safety depends on (a) the range of identified hazards and (b) the operational situations for which mitigations of these hazards are acceptably decreasing risk. Moreover, with an increasing degree of autonomy, risk ownership is likely to increase for vendors towards regulatory certification. Hence, highly automated vehicles have to be equipped with verified controllers capable of reliably identifying and mitigating hazards in all possible operational situations. To this end, available methods for the design and verification of automated vehicle controllers have to be supported by models for hazard analysis and mitigation.
@cite_12 present an algorithm for finding permissive robot action plans optimal to safety and performance. They employ (helpful in regarding uncertainty and robot limitations) to model robot behavior, and two abstractions from this model to capture a system's modes and hazards. Our framework uses three layers of abstraction ( @math , @math , @math ), operational situations to capture control modes, and a structure to capture hazards. While they directly encode hazard severity for plan selection, our framework allows the planner to calculate the risk priority based on a causal event tree towards mishaps. As opposed to complete behavioral planning, our approach focuses the construction of mitigation planning models. For example, for system faults we can plan mitigations by using adaptation mechanisms of a given control system architecture.
@cite_12
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Assuring safety of autonomous vehicles requires that the vehicle control system can perceive the situation in the environment and react to actions of other entities. One approach to vehicle safety assurance is based on the assumption that hazardous sequences of events should be identified during hazard analysis and then some means of hazard avoidance and mitigation, like barriers, should be designed and implemented. Another approach is to design a system which is able to dynamically examine the risk associated with possible actions and then select the safest action to carry it out. Dynamic risk assessment requires maintaining the situation awareness and prediction of possible future situations. We analyse how these two approaches can be applied for autonomous vehicles and what strategies can be used for safety argumentation. 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. We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and “certificates” that ensure the output of an abstract high-level planner can be realized by deeper layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of rough terrains. We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The frame-work features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper.