2,599 research outputs found
Optimization and Control of Cyber-Physical Vehicle Systems
A cyber-physical system (CPS) is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs) are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined
Logic programming for deliberative robotic task planning
Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application
Mission-Phasing Techniques for Constrained Agents in Stochastic Environments.
Resource constraints restrict the set of actions that an agent can take, such that the agent might
not be able to perform all its desired tasks. Computational time limitations restrict the number of
states that an agent can model and reason over, such that the agent might not be able to formulate
a policy that can respond to all possible eventualities. This work argues that, in either
situation, one effective way of improving the agent's performance is to adopt a phasing strategy.
Resource-constrained agents can choose to reconfigure resources and switch action sets for handling
upcoming events better when moving from phase to phase; time-limited agents can choose to focus
computation on high-value phases and to exploit additional computation time during the execution of
earlier phases to improve solutions for future phases.
This dissertation consists of two parts, corresponding to the aforementioned resource constraints
and computational time limitations. The first part of the dissertation focuses on the development
of automated resource-driven mission-phasing techniques for agents operating in
resource-constrained environments. We designed a suite of algorithms which not only can find
solutions to optimize the use of predefined phase-switching points, but can also automatically
determine where to establish such points, accounting for the cost of creating them, in complex
stochastic environments. By formulating the coupled problems of mission decomposition, resource
configuration, and policy formulation into a single compact mathematical formulation, the presented
algorithms can effectively exploit problem structure and often considerably reduce computational
cost for finding exact solutions.
The second part of this dissertation is the design of computation-driven mission-phasing techniques
for time-critical systems. We developed a new deliberation scheduling approach, which can
simultaneously solve the coupled problems of deciding both when to deliberate given its cost, and
which phase decision procedures to execute during deliberation intervals. Meanwhile, we designed a
heuristic search method to effectively utilize the allocated time within each phase. As illustrated
in experimental results, the computation-driven mission-phasing techniques, which
extend problem decomposition techniques with the across-phase deliberation scheduling and
inner-phase heuristic search methods mentioned above, can help an agent generate a better
policy within time limit.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60650/1/jianhuiw_1.pd
Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning
The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques
Metareasoning for Planning Under Uncertainty
The conventional model for online planning under uncertainty assumes that an
agent can stop and plan without incurring costs for the time spent planning.
However, planning time is not free in most real-world settings. For example, an
autonomous drone is subject to nature's forces, like gravity, even while it
thinks, and must either pay a price for counteracting these forces to stay in
place, or grapple with the state change caused by acquiescing to them. Policy
optimization in these settings requires metareasoning---a process that trades
off the cost of planning and the potential policy improvement that can be
achieved. We formalize and analyze the metareasoning problem for Markov
Decision Processes (MDPs). Our work subsumes previously studied special cases
of metareasoning and shows that in the general case, metareasoning is at most
polynomially harder than solving MDPs with any given algorithm that disregards
the cost of thinking. For reasons we discuss, optimal general metareasoning
turns out to be impractical, motivating approximations. We present approximate
metareasoning procedures which rely on special properties of the BRTDP planning
algorithm and explore the effectiveness of our methods on a variety of
problems.Comment: Extended version of IJCAI 2015 pape
- ā¦