Traditional offline redundancy resolution of trajectories for redundant manipulators involves computing inverse kinematic solutions for Cartesian space paths, constraining the manipulator to a fixed path without real-time adjustments. Online redundancy resolution can achieve real-time adjustment of paths, but it cannot consider subsequent path points, leading to the possibility of the manipulator being forced to stop mid-motion due to joint constraints. To address this, this paper introduces a dynamic programming-based offline redundancy resolution for redundant manipulators along prescribed paths with real-time adjustment. The proposed method allows the manipulator to move along a prescribed path while implementing real-time adjustment along the normal to the path. Using Dynamic Programming, the proposed approach computes a global maximum for the variation of adjustment coefficients. As long as the coefficient variation between adjacent sampling path points does not exceed this limit, the algorithm provides the next path point's joint angles based on the current joint angles, enabling the end-effector to achieve the adjusted Cartesian pose. The main innovation of this paper lies in augmenting traditional offline optimal planning with real-time adjustment capabilities, achieving a fusion of offline planning and online planning.

This paper presents the AWKWARD architecture for the development of hybrid agents in Multi-Agent Systems. AWKWARD agents can have their plans re-configured in real time to align with social role requirements under changing environmental and social circumstances. The proposed hybrid architecture makes use of Behaviour Oriented Design (BOD) to develop agents with reactive planning and of the well-established OperA framework to provide organisational, social, and interaction definitions in order to validate and adjust agents' behaviours. Together, OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles, while providing the additional benefit of transparency into the interactions that drive this behavioural change in individual agents. We present this architecture to motivate the bridging between traditional symbolic- and behaviour-based AI communities, where such combined solutions can help MAS researchers in their pursuit of building stronger, more robust intelligent agent teams. We use DOTA2, a game where success is heavily dependent on social interactions, as a medium to demonstrate a sample implementation of our proposed hybrid architecture.

This paper presents the AWKWARD agent architecture for the development of agents in Multi-Agent Systems. AWKWARD agents can have their plans re-configured in real time to align with social role requirements under changing environmental and social circumstances. The proposed hybrid architecture makes use of Behaviour Oriented Design (BOD) to develop agents with reactive planning and of the well-established OperA framework to provide organisational, social, and interaction definitions in order to validate and adjust agents' behaviours. Together, OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles, while providing the additional benefit of transparency into the interactions that drive this behavioural change in individual agents. We present this architecture to motivate the bridging between traditional symbolic- and behaviour-based AI communities, where such combined solutions can help MAS researchers in their pursuit of building stronger, more robust intelligent agent teams.