Abstract: Motion parameters estimation for localization of differential drive vehicles is an important part of robotics and autonomous navigation. Conventional methods require introceptive as well extroceptive sensors for localization. The present disclosure provides a control command based adaptive system and method for estimating motion parameters of differential drive vehicles. The method utilizes information from one or more time synchronized command signals and generate an experimental model for estimating one or more motion parameters of the differential drive vehicle by computing a mapping function. The experimental model is validated to determine change in the one or more motion parameters with change in one or more factors and adaptively updated to estimate updated value of the one or more motion parameters based on the validation. The system and method of present disclosure provide accurate results for localization with minimum use of extroceptive sensors.

Abstract: The disclosure relates to motion planning for autonomous vehicles to find a contiguous path to travel from a source to a destination in a real time environment. But, motion planning suffers due to dimensionality problem during planning a path in high dimensional configuration space. Conventional a priori path planning techniques utilize deterministic selection of a seed and there is a challenge in providing resolution completeness, especially in a cluttered environment. Here, the resolution problem associated with the cluttered environment, is rectified by utilizing random seed generation technique. A convex region is grown around each seed and a set of hyperplanes are generated around each convex region. A contiguous polytope is created by utilizing the set of hyperplanes associated with each convex region. Further, an undirected graph is created based on a plurality of polytope points associated with the contiguous polytope and a shortest path in the graph is computed.

Abstract: This disclosure relates generally to real-time path planning. Planning amidst obstacles in a cluttered indoor environment is a difficult task for a robotic agent. The disclosed method provides semidefinite programming induced free-space based path planning. Free-space is generated by an efficient environment grid resolution independent seeding technique. In the proposed resolution independent seeding technique, initial position of the robotic agent is considered as the first seed. For subsequent seeding, information of the expanded earlier seeds are employed intelligently. This process is followed unto a finite sequence, which naturally results in a contiguous navigable convex free-space. This contiguous navigable convex free-space is employed to create an undirected graph, which is then used for path planning. Path planning is done locally by evaluating the subgoal with respect to a final goal. Local planning cumulatively assists the planner to attain the final goal.

Abstract: Method and system for narrow passage path sampling based on levy flight is disclosed. The disclosed technique is an improvisation of Random Walk to Surface (RWS), wherein, instead of performing a random walk, the disclosed technique utilizes levy flight mechanism to identify samples in narrow passages (on the obstacle boundaries). The disclosed technique for identification of narrow passages sampling points in the narrow passage is based on several techniques that include random uniform sampling technique, a levy flight function (step size) and a collision detection technique. Moreover, in addition to identification of narrow passages sampling points, the disclosed technique also performs an additional check to ensure that the identified narrow passages sampling points are present in the narrow passage based on a levy flight bridge sampler technique.

Abstract: An auction-based bid generation technique is a NP-hard problem and is not suitable for real-time scheduling of multi-agents. The embodiments thus provide a system and method for scheduling a set of tasks among a plurality of agents. Herein, agents self-allocate tasks among themselves dynamically in a distributed fashion, following an ordered sequence of agent indexes. The motivation of following the ordered sequence of agent indexes is allowing each agent to select its best strategy once by exploiting the greedy characteristic of the agent. The preferred agent (based on the ordered sequence) self-allocates tasks among multiple based on the minimum L2 Norm between task attributes and agent attributes, results in a strategy. The strategy offered by a sequence needs to satisfy constraints. A heuristic reward function for each strategy is proposed. Based on these rewards, agents reach consensus by playing an exact potential game for scheduling tasks among the agents.

Abstract: This disclosure relates generally to real-time path planning. Planning amidst obstacles in a cluttered indoor environment is a difficult task for a robotic agent. The disclosed method provides semidefinite programming induced free-space based path planning. Free-space is generated by an efficient environment grid resolution independent seeding technique. In the proposed resolution independent seeding technique, initial position of the robotic agent is considered as the first seed. For subsequent seeding, information of the expanded earlier seeds are employed intelligently. This process is followed unto a finite sequence, which naturally results in a contiguous navigable convex free-space. This contiguous navigable convex free-space is employed to create an undirected graph, which is then used for path planning. Path planning is done locally by evaluating the subgoal with respect to a final goal. Local planning cumulatively assists the planner to attain the final goal.

Abstract: Motion parameters estimation for localization of differential drive vehicles is an important part of robotics and autonomous navigation. Conventional methods require introceptive as well extroceptive sensors for localization. The present disclosure provides a control command based adaptive system and method for estimating motion parameters of differential drive vehicles. The method utilizes information from one or more time synchronized command signals and generate an experimental model for estimating one or more motion parameters of the differential drive vehicle by computing a mapping function. The experimental model is validated to determine change in the one or more motion parameters with change in one or more factors and adaptively updated to estimate updated value of the one or more motion parameters based on the validation. The system and method of present disclosure provide accurate results for localization with minimum use of extroceptive sensors.

Abstract: The disclosure relates to motion planning for autonomous vehicles to find a contiguous path to travel from a source to a destination in a real time environment. But, motion planning suffers due to dimensionality problem during planning a path in high dimensional configuration space. Conventional a priori path planning techniques utilize deterministic selection of a seed and there is a challenge in providing resolution completeness, especially in a cluttered environment. Here, the resolution problem associated with the cluttered environment, is rectified by utilizing random seed generation technique. A convex region is grown around each seed and a set of hyperplanes are generated around each convex region. A contiguous polytope is created by utilizing the set of hyperplanes associated with each convex region. Further, an undirected graph is created based on a plurality of polytope points associated with the contiguous polytope and a shortest path in the graph is computed.