Social Robot Navigation through Constrained Optimization: a Comparative Study of Uncertainty-based Objectives and Constraints

This work is dedicated to the study of how uncertainty estimation of the human motion prediction can be embedded into constrained optimization techniques, such as Model Predictive Control (MPC) for the social robot navigation. We propose several cost objectives and constraint functions obtained from the uncertainty of predicting pedestrian positions and related to the probability of the collision that can be applied to the MPC, and all the different variants are compared in challenging scenes with multiple agents. The main question this paper tries to answer is: what are the most important uncertainty-based criteria for social MPC? For that, we evaluate the proposed approaches with several social navigation metrics in an extensive set of scenarios of different complexity in reproducible synthetic environments. The main outcome of our study is a foundation for a practical guide on when and how to use uncertainty-aware approaches for social robot navigation in practice and what are the most effective criteria

Safe Navigation of a Car-Like Robot in a Dynamic Environment

voir basilic : http://emotion.inrialpes.fr/bibemotion/2005/PF05a/ address: Ancona (IT)This paper addresses the problem of navigation of a car-like robot in dynamic environments. Such environments impose a hard real time constraint. However, computing a complete motion to the goal within a limited time is impossible to achieve in most real situations. Besides, the limited duration validity of the model used for planning requires the model and therefore the plan to be updated. In this paper, we present a Partial Motion Planning (PMP) approach as the answer to this problem. The issue of safety raised by this approach is addressed using the Inevitable Collision State formalism and effectiveness of the approach is demonstrated with several simulation examples. The quality of the generated trajectories is discussed and continuous curvature metric is integrated as a mean to improve it

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

Passively Safe Partial Motion Planning for Mobile Robots with Limited Field-of-Views in Unknown Dynamic Environments

International audienceThis 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 \textit{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 \textit{passive motion safety}: it guarantees that, if a collision takes place, the robot will be at rest. The primary contribution of this paper is {\passivepmp}, a partial motion planner enforcing passive motion safety. {\passivepmp} 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 \textit{Braking ICS}, {\ie} states such that, whatever the future braking trajectory of the robot, a collision occurs before it is at rest. Simulation results demonstrate how {\passivepmp} operates and handles limited sensory field-of-views, occlusions and moving obstacles with unknown future behaviour. More importantly, {\passivepmp} is provably passively safe

Dynamic Certification for Autonomous Systems

Autonomous systems are often deployed in complex sociotechnical environments, such as public roads, where they must behave safely and securely. Unlike many traditionally engineered systems, autonomous systems are expected to behave predictably in varying "open world" environmental contexts that cannot be fully specified formally. As a result, assurance about autonomous systems requires us to develop new certification methods and mathematical tools that can bound the uncertainty engendered by these diverse deployment scenarios, rather than relying on static tools

Probabilistic Collision Checking with Chance Constraints

Abstract-Obstacle avoidance, and by extension collision checking, is a basic requirement for robot autonomy. Most classical approaches to collision checking ignore the uncertainties associated with the robot and obstacle's geometry and position. It is natural to use a probabilistic description of the uncertainties. However, constraint satisfaction cannot be guaranteed in this case and collision constraints must instead be converted to chance constraints. Standard results for linear probabilistic constraint evaluation have been applied to probabilistic collision evaluation, but this approach ignores the uncertainty associated with the sensed obstacle. An alternative formulation of probabilistic collision checking that accounts for robot and obstacle uncertainty is presented which allows for dependent object distributions (e.g., interactive robotobstacle models). In order to efficiently enforce the resulting collision chance constraints, an approximation is proposed and the validity of this approximation is evaluated. The results presented here have been applied to robot motion planning in dynamic, uncertain environments

A New Approach towards Non-holonomic Path Planning of Car-like Robots using Rapidly Random Tree Fixed Nodes(RRT*FN)

Autonomous car driving is gaining attention in industry and is also an ongoing research in scientific community. Assuming that the cars moving on the road are all autonomous, this thesis introduces an elegant approach to generate non-holonomic collision-free motion of a car connecting any two poses (configurations) set by the user. Particularly this thesis focusses research on "path-planning" of car-like robots in the presence of static obstacles. Path planning of car-like robots can be done using RRT and RRT*. Instead of generating the non-holonomic path between two sampled configurations in RRT, our approach finds a small incremental step towards the next random configuration. Since the incremental step can be in any direction we use RRT to guide the robot from start configuration to end configuration. This "easy-to-implement" mechanism provides flexibility for enabling standard plan- ners to solve for non-holonomic robots without much modifications. Thus, strength of such planners for car path planning can be easily realized. This thesis demon- strates this point by applying this mechanism for an effective variant of RRT called as RRT - Fixed Nodes (RRT*FN). Experiments are conducted by incorporating our mechanism into RRT*FN (termed as RRT*FN-NH) to show the effectiveness and quality of non-holonomic path gener- ated. The experiments are conducted for typical benchmark static environments and the results indicate that RRT*FN-NH is mostly finding the feasible non-holonomic solutions with a fixed number of nodes (satisfying memory requirements) at the cost of increased number of iterations in multiples of 10k. Thus, this thesis proves the applicability of mechanism for a highly constrained planner like RRT*-FN, where the path needs to be found with a fixed number of nodes. Although, comparing the algorithm (RRT*FN-NH) with other existing planners is not the focus of this thesis there are considerable advantages of the mechanism when applied to a planner. They are a) instantaneous non-holonomoic path generation using the strengths of that particular planner, b) ability to modify on-the-fly non-holomic paths, and c) simple to integrate with most of the existing planners. Moreover, applicability of this mechanism using RRT*-FN for non-holonomic path generation of a car is shown for a more realistic urban environments that have typical narrow curved roads. The experiments were done for actual road map obtained from google maps and the feasibility of non-holonomic path generation was shown for such environments. The typical number of iterations needed for finding such feasible solutions were also in multiple of 10k. Increasing speed profiles of the car was tested by limiting max speed and acceleration to see the effect on the number of iterations