Abstract-This paper addresses the problem of planning the motion of a mobile robot with a limited sensory field-of-view in an unknown dynamic environment. In such a situation, the upper-bounded planning time prevents from computing a complete motion to the goal, partial motion planning is in order. Besides the presence of moving obstacles whose future behaviour is unknown precludes absolute motion safety (in the sense that no collision will ever take place whatever happens) is impossible to guarantee. The stance taken herein is to settle for a weaker level of motion safety called passive motion safety: it guarantees that, if a collision takes place, the robot will be at rest. The primary contribution of this paper is PASSPMP, a partial motion planner enforcing passive motion safety. PASSPMP periodically computes a passively safe partial trajectory designed to drive the robot towards its goal state. Passive motion safety is handled using a variant of the Inevitable Collision State (ICS) concept called Braking ICS, i.e. states such that, whatever the future braking trajectory of the robot, a collision occurs before it is at rest. Simulation results demonstrate how PASSPMP operates and handles limited sensory field-of-views, occlusions and moving obstacles with unknown future behaviour. More importantly, PASSPMP is provably passively safe.

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In this paper, an evolutionary scan-matching approach is proposed to solve an optimization issue in simultaneous localization and mapping (SLAM). A rich literature has been invested in this direction, however, most of the proposed approaches lack fast convergence and simplicity regarding the optimization process, which should directly affect the accuracy of the environment's map and the estimated pose. It is a line of research that is always active, offering various solutions to this issue. Among many SLAM methods, the normal distributions transform approach (NDT) has shown high performances, where numerous works have been published up to date and many studies demonstrate its efficiency wrt other methods. Nevertheless, few works have been interested to solve the optimization issue. The proposed solution is based on NDT scanmatching using particle swarm optimization (PSO) and it is dubbed NDT-PSO. The main contribution is to solve the pose estimation problem based on PSO and iterative NDT maps. The performances of the NDT-PSO approach have been proven in real experiments performed on a car-like mobile robot in both static and dynamic environments. NDT-PSO is tested for different swarm sizes, and the results show that 70 particles are more than enough to find the best particle while avoiding local minima even in loop closing. The algorithm is also suitable for real time applications, with an average runnnig time of 145ms for 70 particles and 70 iterations of the optimization process. This value can be further reduced using fewer particles and iterations. The accuracy of the proposed approach is also evaluated wrt other SLAM methods widely used among the robot operating system community and it has been shown that NDT-PSO outperforms these algorithms.

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SUMMARY Robots are now among us and even though they compete with human beings in terms of performance and efficiency, they still fail to meet the challenge of performing a task optimally while providing strict motion safety guarantees. It is therefore necessary that the future generation of robots evolves in this direction. Generally, in robotics state-of-the-art approaches, the trajectory optimization and the motion safety issues have been addressed separately. An important contribution of this paper is to propose a motion planning method intended to simultaneously solve these two problems in a formal way. This motion planner is dubbed PassPMP-PSO. It is based on a periodic process that interleaves planning and execution for a regular update of the environment’s information. At each cycle, PassPMP-PSO computes a safe near-optimal partial trajectory using a new tree encoding technique based on particle swarm optimization (PSO). The performances of the proposed approach are firstly highlighted in simulation environments in the presence of moving objects that travel at high speed with arbitrary trajectories, while dealing with sensors field-of-view limits and occlusions. The PassPMP-PSO algorithm is tested for different tree expansions going from 13 to more than 200 nodes. The results show that for a population between 20 and 100 particles, the frequency of obtaining optimal trajectory is 100% with a rapid convergence of the algorithm to this solution. Furthermore, an experiment-based comparison demonstrates the performances of PassPMP-PSO over two other motion planning methods (the PassPMP, a previous variant of PassPMP-PSO, and the input space sampling). Finally, PassPMP-PSO algorithm is assessed through experimental tests performed on a real robotic platform using robot operating system in order to confirm simulation results and to prove its efficiency in real experiments.