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The Distributed Simplex Architecture (DSA) extends the Simplex control architecture of Sha et al. to provide runtime safety assurance for multi-agent systems under distributed control. In this paper, we show how DSA can be used to ensure collision-free 3D flocking behavior, such that agents avoid colliding with each other and with cuboid-shaped obstacles.

Despite safe mechanical design being necessary for collaborative robots, one should not underestimate the importance of active safety due to a multi-objective control design. Active safety not only complements mechanical compliance but also enables classical industrial robots to fulfil additional task-space objectives. Through a task-space controller based on a quadratic programming (QP) formulation, the proposed approach assigns strict priority to avoid collisions and specifies other QP objectives with soft task priorities. Through experiments performed on a dual-arm robot, the proposed solution can generate safe robot motion while fulfilling other task specifications.

We study a model for a multiple-needle robot based on the Fibonacci sequence scaling. The paper determines the collision-free trajectories of the robot who is carrying drugs into a tumoral target. The purpose of the paper is to predict the insertion trajectories for all needles so as the trajectories should avoid the collisions to ribs, blood vessels and tissues in the abdominal area, the interference between trajectories and finally, to reach the target. Comparison with genetic algorithm and the particle swarm optimization algorithm are performed.

Collision avoidance is one of the most primary problems in the decentralized multiagent navigation: while the agents are moving towards their own targets, attentions should be paid to avoid the collisions with the others. In this paper, we introduced the concept of the local action cell, which provides for each agent a set of velocities that are safe to perform. Consequently, as long as the local action cells are updated on time and each agent selects its motion within the corresponding cell, there should be no collision caused. Furthermore, we coupled the local action cell with an adaptive learning framework, in which the performance of selected motions are evaluated and used as the references for making decisions in the following updates. The efficiency of the proposed approaches were demonstrated through the experiments for three commonly considered scenarios, where the comparisons have been made with several well studied strategies.

In this study, we developed a deadlock-free and collision-free liver surgical navigation method by switching potential-based and sensor-based approaches. The potential-based approach selects a near-optimal route from a scalpel tip to an arbitrary neighbor position around a tumor in a 3D organ map converted from digital imaging and communications in medicine (DICOM) data captured by magnetic resonance imaging or computed tomography. However, among complex-shaped blood vessels, the approach sometimes loses the route. To overcome this drawback, we switch to the sensor-based approach. This approach always finds a route near a tumor. However, the path becomes longer. Therefore, when the potential-based approach recovers to find another path, we switch the sensor-based approach back to the potential-based approach. The usefulness of this switching method was carefully ascertained in several kinds of allocations of tumor and blood vessels.

The paper addresses the problem of map building and collision free path planning based on hexagonal lattice and Dempster-Shafer theory. We presented the advantages of the proposed solution. It is shown that hexagonal maps of an environment can be easily build based on sensors reading and is useful in collision-free path planning and mobile robot localization.

International audience; A Mobile Cable-Driven Parallel Robot (MCDPR) is a special type of Reconfigurable Cable-Driven Parallel Robot (RCDPR) composed of a classical Cable-Driven Parallel Robot (CDPR) mounted on multiple mobile bases. The additional mobility of the mobile bases allows such systems to autonomously modify their geometric architecture, and thus make them suitable for multiple manipulative tasks in constrained environments. Moreover, these additional mo-bilities make MCDPRs kinematically redundant. Therefore, the subject of this paper is to introduce a two stage path planning algorithm for MCDPRs. The first stage searches for a feasible and collision free path of mobile bases. The second stage deals with generating an optimal path of the moving-platform to displace it from an initial to a desired pose. The proposed algorithm is validated through simulation on a three degree-of-freedom (DoF) point mass moving-platform displaced by four cables with each cable carried by an independent mobile base.

This paper presents a study of the mutual visibility problem for a set of opaque asynchronous robots in the Euclidean plane. Due to opacity, if three robots lie on a line, the middle robot obstructs the visions of the two other robots. The mutual visibility problem requires the robots to coordinate their movements to form a configuration in which no three robots are collinear. This work presents a distributed algorithm which solves the mutual visibility problem for a set of \(n\ge 2\) asynchronous robots under the FState computational model. The proposed algorithm assumes 1 bit of persistent memory and the knowledge of n. The proposed solution works under the non-rigid movements of the robots and also provides collision free movements for the robots.

This paper proposes a distributed algorithm for forming a Uniform Circle by \(n>1\) autonomous, homogeneous disc shaped robots (also known as fat robots), where the robots are asked to place themselves equidistant apart on the periphery of a circle (not given in advanced). The robots operate by executing cycles of the states “sense-compute-move” simultaneously. They do not interact by message passing. The robots can recollect neither the past actions nor the sensed data from previous cycle. They are indistinguishable. The robots do not have any global coordinate system. They agree upon only Y-axis (South and North direction). This paper presents a collision free movement strategy for the robots in order to form a uniform circle.

We consider a variant of collision-free routing problem CRP. In this problem, we are given set C of n vehicles which are moving in a plane along a predefined directed rectilinear path. Our objective (CRP) is to find the maximum number of vehicles that can move without collision. CRP is shown to be NP-Hard by Ajaykumar et al. [1]. It was also shown that the approximation of this problem is as hard as Maximum Independent Set problem (MIS) even if the paths between a pair of vehicles intersects at most once. So we study the constrained version CCRP of CRP in which each vehicle \(c_i\) is allowed to move in a directed L-Shaped Path.

In order to take into account evading mechanism and make more realistic simulation results, we propose a data-driven and collision-free hybrid crowd simulation model in this paper. The first part of the model is a data-driven process in which we introduce an algorithm called MS-ISODATA (Main Streams Iterative Self-organizing Data Analysis) to learn motion patterns from real scenarios. The second part introduces an agent-based collision-free mechanism in which a steering approach is improved and this part uses the output from the first part to guide its agents. The hybrid simulation model we propose can reproduce simulated crowds with motion features of real scenarios, and it also enables agents in simulation evade from mutual collisions. The simulation results show that the hybrid crowd simulation model mimics the desired crowd dynamics well. According to a collectiveness measurement, the simulation results and real scenarios are very close. Meanwhile, it reduces the number of virtual crowd collisions and makes the movement of the crowd more effective.

Pedestrians have an intuitive ability for navigation to avoid obstacles and nearby pedestrians. If we want to predict future positions of a pedestrian, we should know how the pedestrian adjust his direction to avoid collisions. In this work, we present a simple and effective framework for human trajectory prediction to generate the future sequence based on pedestrian past positions. The method, called Collision-Free LSTM, extends the classical LSTM by adding Repulsion pooling layer to share hidden-states of neighboring pedestrians. The model can learn both the temporal information of trajectories and the interactions between pedestrians, which is in contrast to traditional methods using hand-crafted features such as Social forces. The experiments results on two public datasets show that our model can achieve state-of-the-art performance with assessment metrics.

We implemented a closed-loop control algorithm that uses information on the turning radius of a biohybrid fly-robot interface (FRI) to interpret the spike rate of a motion sensitive interneuron, the H1-cell, as a function of distance from a patterned wall. The fly-robot interface repeatedly triggers collision avoidance manoeuvres during an oscillatory forward movement with bias towards the wall whenever the H1-cell spiking activity exceeds a certain threshold value. In addition, we further investigated the parameters which will ultimately enable the system to manoeuvre autonomously, and in an energy-efficient way within arbitrary visual environments.

We propose in this paper a minimal speed-based pedestrian model for which particle dynamics are intrinsically collision-free. The speed model is an optimal velocity function depending on the agent length (i.e.\ particle diameter), maximum speed and time gap parameters. The direction model is a weighted sum of exponential repulsion from the neighbors, calibrated by the repulsion rate and distance. The model's main features like the reproduction of empirical phenomena are analysed by simulation. We point out that phenomena of self-organisation observable in force-based models and field studies can be reproduced by the collision-free model with low computational effort., Comment: 8 pages, 4 figures. TGF'15 Conference, 2015

This study, proposes a methodology to evaluate the performance of a novel emergency lane change algorithm. The algorithm, defines a number of constraints, based on the vehicle’s dynamics and environmental conditions, which must be satisfied for a safe and comfortable lane change maneuver. Inclusion of the lateral position of other vehicles on the road, the tire-road friction, and real-time ability are the main advantages of the proposed algorithm. For performance evaluation of the developed algorithm, a set of driving scenarios were designed to consider different possible traffic situations that may appear in an emergency lane change maneuver. These scenarios were implemented later in IPG CarMaker, which is a vehicle’s dynamics platform. Based on the designed scenarios, the efficiency of the algorithm in collision free lane change maneuver was examined.

Carrier Sense Multiple Access with Enhanced Collision Avoidance (CSMA/ECA) is a totally distributed, collision-free MAC protocol for IEEE 802.11 WLANs. It is capable of achieving greater throughput than the MAC protocol used in the current standard, called Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), by means of picking a deterministic backoff after successful transmissions. This work is the first implementation of the main concept behind CSMA/ECA on real hardware. Experimental results confirm the advantages of CSMA/ECA over CSMA/CA in terms of throughput and set the ground for its complete prototyping on real hardware using OpenFWWF.

In this chapter we present simulation results of an algorithm designed to planning collision free path based on probabilistic search. The aim of this study is to design an algorithm for off-line planning a set of way-points which make the collision free route of an Unmanned Aerial Vehicle (UAV). The planning process is based on the graph that represents the map containing information about altitude of the terrain over which the UAV is intended to perform its mission. The size of the graph increases polynomially with the resolution of the map and the number of way points of UAV. The probabilistic method of making the representative model of the terrain was applied in order to reduce a complexity of planning the collision free path. The functioning and efficiency of the approach proposed was introduced in our previous work, here we use real terrain map and introduce indicators to illustrate properties of the algorithm.