Most collision avoidance methods do not consider the vehicle shape and its kinematic and dynamic constraints, assuming the robot to be point-like and omnidirectional with no acceleration constraints. The contribution of this paper is a methodology to consider the exact shape and kinematics, as well as the effects of dynamics in the collision avoidance layer, since the original avoidance method does not address them. This is achievable by abstracting the constraints from the avoidance methods in such a way that when the method is applied, the constraints already have been considered. This study is a starting point to extend the domain of applicability to a wide range of collision avoidance methods.

Autonomous navigation in dynamic environments where people move unpredictably is an essential task for service robots in real-world populated scenarios. Recent works in reinforcement learning (RL) have been applied to autonomous vehicle driving and to navigation around pedestrians. In this paper, we present a novel planner (reinforcement learning dynamic object velocity space, RL-DOVS) based on an RL technique for dynamic environments. The method explicitly considers the robot kinodynamic constraints for selecting the actions in every control period. The main contribution of our work is to use an environment model where the dynamism is represented in the robocentric velocity space as input to the learning system. The use of this dynamic information speeds the training process with respect to other techniques that learn directly either from raw sensors (vision, lidar) or from basic information about obstacle location and kinematics. We propose two approaches using RL and dynamic obstacle velocity (DOVS), RL-DOVS-A, which automatically learns the actions having the maximum utility, and RL-DOVS-D, in which the actions are selected by a human driver. Simulation results and evaluation are presented using different numbers of active agents and static and moving passive agents with random motion directions and velocities in many different scenarios. The performance of the technique is compared with other state-of-the-art techniques for solving navigation problems in environments such as ours.

Auction based techniques are a highly successful tool used for multi-robot task allocation. However, theoretical performance and a proper taxonomy of optimization objectives have remained scarce until recent studies. Implementations from different authors have not been compared in common grounds and in light of these recent findings. In this paper we address this lack of comparative experimentation, providing simulation results on a large real life based scenario and in random worlds. Two intuitive optimization objectives, minimum total resource usage and minimum total time, are evaluated in object searching missions. A method for flexible tailoring of the bidding rules is presented and new insight is gained on the effect of using hybrid criteria for the optimization objective.