Motion planning of agents is one of the fundamental research problems in autonomous systems. An important aspect of motion planning is collision avoidance of the agents with other agents and obstacles that are present in the agent's environment. Typically, the collision avoidance constraints are non-linear and non-convex. Thus, the mathematical formulation of motion planning of multiple agents, in the presence of other agents and obstacles, is NP-Hard. In this paper, a novel heuristic approach for motion planning in multi-agent dynamic environment is proposed. The approach is computationally cheap, and can be launched locally on each agent for the trajectory planning. The applicability of the proposed approach is illustrated by numerical examples considering uncertainty in the environment. Detailed discussions on the performance of the proposed approach are presented. Finally, the observations on the key characteristics of the proposed approach are summarized.
In order to drive forward the energy transition, construction companies and other suppliers of deep retrofitting solutions have started to give guarantees on the energy performance of very energy efficient houses. With these initiatives, a need has arisen for methods that can assess per household the actual energy performance during the use phase. An RC-network simulation model calibrated with monitoring data has been developed and tested on deep retrofitted Net-Zero houses in Emmen (the Netherlands). The results show that this has been a successful first step in order to arrive at a realistic analysis of the actual energy performance of individual houses. The big challenge will be to determine the parameters in the model with more certainty. This applies especially, but not exclusively, to the behavioural parameters.
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