In the multi-agent pathfinding problem, an agent may have multiple possible destinations, each of which must be appropriately selected in terms of conflict avoidance and shortest path. For example, in an evacuation system when faced with a fire hazard in a building with multiple exits for evacuation, agents need to consider not only the shortest path but also the conflicts of other agents and the fire spread to respond appropriately. However, recent reinforcement-learning-based evacuation pathfinding methods only consider the scenarios of independent individual agents and ignore the influence of other agents and the spread of fire, limiting their application to evacuation. To address these issues, we propose a multi-agent reinforcement learning for pathfinding during hazard evacuation (MARL-HE), which is integrated into reinforcement learning with two novel components: an efficient artificial potential field adapter (APF-adapter) and policy-sharing-enhanced multi-agent proximity policy optimization (PS-MAPPO). The APF adapter utilizes an artificial potential field to eliminate the dynamic state transitions during hazard evacuation. PS-MAPPO leverages policy sharing by determining which other agents each agent has to share part of their policy model to accelerate training efficiency. To evaluate our method, we built a multi-agent fire evacuation guidance system (MAFEGS), a Unity-based fire hazard environment. The experimental results show that our MARL-HE method achieved a 7.02% higher evacuation success rate and 25.12% lower average evacuation number than MAPPO. MAFEGS can plan a safer and faster evacuation guidance path to keep individual safe and increase evacuation efficiency. The simulation environment and code can be downloaded from: https://github.com/ColaZhang22/PS_MARL_PF.

Minimally Invasive Surgery (MIS) is one of the most successful applications of surgical robots. Although the introduction of robotic technology has brought a number of benefits, further advancements in MIS are limited by the size and bending radius of instruments. In this paper, we present a compliant four degree-of-freedom manipulator that consists of elastic elements with partly thinner structures. The proposed mechanism allows the elastic element to deform locally, thus minimizing its bending radius while the low number of mechanical parts greatly contributes to its compactness. This paper describes the design strategy, optimization method using FEA, prototype implementation, and evaluations. The evaluations reveal high accuracy and repeat accuracy, which are key elements for robotic instruments in MIS. Further, the prototype is able to exert sufficient force and it is possible to perform a simulated needle insertion task using the manipulator, demonstrating the feasibility of the proposed mechanism.