Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments

Ferrer, Gonzalo; Sanfeliu, Alberto

doi:10.1109/iros.2014.6942788

Cited by 73 publications

(48 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the repulsors ✓ rep are explicit in the obstacles and the attractor ✓ att is the force exerted by the goal (see also [9] in the framework of dynamical movement primitives, DMPs, or [10] in the framework of planning in a human-like manner). In the classic RRT, the repulsors ✓ rep are implicit in the obstacles to provide obstacle avoidance paths whereas the implicit attractor ✓ att is the force to reach a solution path toward the goal if it exists.…”

Section: Hhk Learning Methods a General Approachmentioning

confidence: 99%

See 1 more Smart Citation

Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning

Alzugaray

Sanfeliu

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

Abstract-We propose in this work a new model of how the hidden human knowledge (HHK) of UAV pilots can be incorporated in the UAVs path planning generation. We intuitively know that human's pilots barely manage or even attempt to drive the UAV through a path that is optimal attending to some criteria as an optimal planner would suggest. Although human pilots might get close but not reach the optimal path proposed by some planner that optimizes over time or distance, the final effect of this differentiation could be not only surprisingly better, but also desirable. In the best scenario for optimality, the path that human pilots generate would deviate from the optimal path as much as the hidden knowledge that its perceives is injected into the path. The aim of our work is to use real human pilot paths to learn the hidden knowledge using repulsion fields and to incorporate this knowledge afterwards in the environment obstacles as cause of the deviation from optimality. We present a strategy of learning this knowledge based on attractor and repulsors, the learning method and a modified RRT* that can use this knowledge for path planning. Finally we do real-life tests and we compare the resulting paths with and without this knowledge.

show abstract

Section: Hhk Learning Methods a General Approachmentioning

confidence: 99%

“…where w RiRj is a weight related to the importance of the relation between the repulsors R i and R j , computed as the number of considered n points, when obtainingd Rj ,TR i R j in (10).…”

Section: Optimizationmentioning

confidence: 99%

Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning

Alzugaray

Sanfeliu

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The requirement for human motion prediction arises when we intend to design a robot navigation system that is socially acceptable [17]. Prediction of human trajectories independent of robot plans, however, does not alleviate the problem of purely reactive robot behavior [18]. For example, consider a corridor situation where a robot and a person could only cross each other in a side-by-side configuration ( fig.…”

Section: Human Motion Predictionmentioning

confidence: 99%

Viewing Robot Navigation in Human Environment as a Cooperative Activity

Khambhaita

Alami

2019

Springer Proceedings in Advanced Robotics

View full text Add to dashboard Cite

We claim that navigation in human environments can be viewed as cooperative activity especially in constrained situations. Humans concurrently aid and comply with each other while moving in a shared space. Cooperation helps pedestrians to efficiently reach their own goals and respect conventions such as the personal space of others. To meet human comparable efficiency, a robot needs to predict the human trajectories and plan its own trajectory correspondingly in the same shared space. In this work, we present a navigation planner that is able to plan such cooperative trajectories, simultaneously enforcing the robot's kinematic constraints and avoiding other non-human dynamic obstacles. Using robust social constraints of projected time to a possible future collision, compatibility of human-robot motion direction, and proxemics, our planner is able to replicate human-like navigation behavior not only in open spaces but also in confined areas. Besides adapting the robot trajectory, the planner is also able to proactively propose co-navigation solutions by jointly computing human and robot trajectories within the same optimization framework. We demonstrate richness and performance of the cooperative planner with simulated and real world experiments on multiple interactive navigation scenarios.

show abstract

“…The robot navigation planners explained in [13] and [14] use the social force model to cope with uncertain human motions. In the extended social force model [15], [16] approach, every iteration of planning step uses the human prediction information which is dependent on the path calculated during the previous iteration. This is the second planner we are using for our comparison.…”

Section: Related Workmentioning

confidence: 99%

Assessing the social criteria for human-robot collaborative navigation: A comparison of human-aware navigation planners

Khambhaita

Alami

2017

2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

View full text Add to dashboard Cite

Abstract-This paper focuses on requirements for effective human robot collaboration in interactive navigation scenarios. We designed several use-cases where humans and robot had to move in the same environment that resemble canonical path-crossing situations. These use-cases include open as well as constrained spaces. Three different state-of-the-art humanaware navigation planners were used for planning the robot paths during all selected use-cases. We compare results of simulation experiments with these human-aware planners in terms of quality of generated trajectories together with discussion on capabilities and limitations of the planners. The results show that the human-robot collaborative planner [1] performs better in everyday path-crossing configurations. This suggests that the criteria used by the human-robot collaborative planner (safety, time-to-collision, directional-costs) are possible good measures for designing acceptable human-aware navigation planners. Consequently, we analyze the effects of these social criteria and draw perspectives on future evolution of human-aware navigation planning methods.

show abstract

Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments

Cited by 73 publications

References 20 publications

Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning

Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning

Viewing Robot Navigation in Human Environment as a Cooperative Activity

Assessing the social criteria for human-robot collaborative navigation: A comparison of human-aware navigation planners

Contact Info

Product

Resources

About